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Brain-Computer Music Interface, a bibliometric analysis

Brain-Computer Music Interface, a bibliometric analysis BRAIN-COMPUTER INTERFACES 2022, VOL. 9, NO. 4, 226–245 https://doi.org/10.1080/2326263X.2022.2109313 ORIGINAL RESEARCH a b Héctor Fabio Torres-Cardona and Catalina Aguirre-Grisales a b Music Department, Universidad de Caldas, Caldas, Colombia; Engineering Department, Universidad Autónoma de Manizales, Manizales, Colombia ABSTRACT ARTICLE HISTORY Received 14 December 2021 This article depicts a bibliometric analysis done through visualization mechanisms and interpreta- Revised 18 June 2022 tion of bibliometric metadata on the research field of Brain-Computer Interface and Music or Brain- Accepted 30 July 2022 Computer Music Interface (BCMI). Citation, co-citation, co-authorship, and keywords co-occurrence analysis were carried out in this work in order to identify the intellectual structure, research trends, KEYWORDS the organizations involved, and the methodological structure of such research field. The biblio- Brain-Computer interface; metric metadata was visualized through VOSviewer and Scimat software. This study also includes Brain-Computer Music the analysis of 227 papers done through 2005–2021 which include research and review articles, Interface; bibliometric analysis; metadata and proceedings papers. The results of this work demonstrate the growing and legitimizing of the visualization research field, and the impact of the interdisciplinary work required in this area. 1. Introduction unconsciously as a response to external stimulation [7,9,10]. These modes can be found in Brain- Human-machine interaction has led to the development Computer Music Interfaces (BCMI) a branch of BCI of different types of systems that are capable of control- [11] that focuses on research that seeks to transform ling external environments through the processing and brain commands into sounds and musical structures. characterization of physiological signals, in which brain- As a result of the work with sound, the first brain computer interfaces are one of the most prominent in musical and sound examples were reported after the the field. In the 1990s, Wolpaw proposed the term invention of the EEG. In 1934, Adrian and Matthews Brain-Computer Interface (BCI), as a communication were able to correlate their Posterior Dominant Rhythm channel between the brain and external devices [1]. This (PDR) by reproducing their brain signals in system is based on the electroencephalogram (EEG), a loudspeaker. This was done by monitoring their which records brain activity through electrodes located brain signals through the brain PDR [12,13]. In 1965, on the scalp [2,3]. In these systems, brain activity is Alvin Lucier created the first brain music, controlling encoded into physiological and cognitive information, percussion instruments through their PDR rhythms which is used in real-time to capture the cognitive status [11,14]. Following Lucier’s experience, in 1970, David of the user for cognitive assessment, mitigation strate- Rosenboom developed a musical piece using several gies, restoration of motor skills, and robust control in users whose brain signals were processed through elec- the area of Augmented Cognition [4–6]. The acquisition tronic circuits this generated a sonorous and visual of communication and processing systems have also performance for ‘the Automation House’ in New York generated new applications in industrial and consumer [15]. Subsequent BCMI performances were based on the environments, including users with and without physi- sonification of EEG signals, being the performance of cal limitations [2,7,8]. Roseboom and Number in 1997, a benchmark in this BCI research has shown that brain activity can be field of research where the authors controlled the used in three modes; active, reactive and passive mode. sounds through the recognition of EEG signal patterns In the active mode, users can consciously control their (Rosenboom & Number, [16]1997). Years later, brain signals, generating commands for external appli- Eduardo Miranda focused his work on the field of cations. In the reactive mode, brain activity is indirectly musical creation based on EEG signals processing, modified by the user in order to control an application; allowing composers to modulate time and musical and in the passive mode, brain signals are generated dynamics, thus consolidating the Brain-Computer CONTACT Héctor Fabio Torres-Cardona hector.torres_c@ucaldas.edu.co Music Department, Universidad de Caldas, Caldas, Colombia This paper is part of the Doctoral Research Project ”Emotion induction and recognition system based on brain-computer interfaces using sound stimulation”, funded by the Ministry of Science of the Colombian Government, National Doctorate - 757 © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. BRAIN-COMPUTER INTERFACES 227 Table 1. Definition of guiding bibliometric questions based on Music Interface field of research (Brain-Computer the bibliometric techniques used. Music Interface, BCMI) [11], and within our previous Bibliometric works ‘Interpolation’ a model for sound representation techniques Guiding bibliometric analysis questions System [17]. Citation Who are the experts in the BCMI research field? On the subject of BCMI, the purpose of this Co-citation Who are the central, peripheral, or bridging researchers in the BCMI research field? article is to analyze the research area through How has the research structure of the BCMI field a bibliometric study using the Scimat and developed over time? Coauthors Are the BCMI researchers interdisciplinary, or do they VOSviewer platforms that allow the application of belong to the same research field? scientific mapping techniques (Zupic & Carter, [18] What is the social structure of the BCMI research field? Keywords Co- What are the dynamics of the conceptual structure of 2015) so as to identify, classify and summarize the occurrence the BCMI research field? main authors, research trends, main publication What are the topics associated with the BCMI research field? sources, and signal processing techniques. 2. Materials and methods 2.1. Bibliometric research design A bibliometric study was first mentioned in Pritchard’s The research design consists of defining the guiding seminal work, ‘Statistical Bibliography or Bibliometrics’ questions and interpreting the bibliometric analysis in 1969 [19]. It was defined as the application of quan- techniques (Table 1), which are subsequently used on titative methods that examine a scientific communica- the selected documents, as to identify the research fields tion process by measuring and analyzing various aspects and trends, as well as the intellectual network of of published research work. Since then, bibliometric researchers in the BCMI area. studies have gained great importance and became a tool for science policy and research management [20]. What impacts its growth is its relevancy in the 2.2. Compilation of articles evaluation of authorship and publication patterns of The process in which Articles have been compiled is communication in any chosen field of research. The based on a literature bibliographic search under principal aim of is to increase the understanding of a protocol based on keywords, time window, and doc- such growth of literature and research patterns over umentary taxonomy, this process allows information of time and take these forward [21]. Several studies have authors, research areas, type of publications, the evolu- already demonstrated the usefulness of bibliometric tion of the subject, networks of researchers, among studies to evaluate scientific productivity in different others to be accessible. The bibliographic search was fields of research. Hamadicharef [22] used bibliometrics carried out in the SCOPUS platform of the Scimago in order to study the status of BCI research. This study Research Group and Web of Science (WOS) of took into account scientific progress and productivity by Clarivate Analytics, establishing the search parameters analyzing the growth of literature, authorship patterns, described in Table 2. and citation rates [23]. Glanzel used bibliometrics to The detailed search in both databases gave as a result study authorship patterns and the relationship between a total of 309 documents, 220 in SCOPUS and 89 in productivity and citation characteristics of scientific WOS, where 35 articles from non-indexed sources, research between 1980 and 1998 [24]. Many biblio- workshops, master’s dissertations, doctoral theses, and metric studies adopt Lotka’s Law of 1926 on scientific book chapters were excluded. In addition, the results of productivity to analyze scientific productivity [25,26]. It both databases were filtered, eliminating 47 duplicate is an inverse square law , which proposes that only 6% of authors in any given field of research produce more than 10 published research articles [22,27]. Similarly, this study will rely on Lotka’s law to deter- mine the scientific productivity of BCMI literature. Table 2. Search parameters. The bibliometric study of the BCMI area is based on Search compilation parameters Search terms Brain Computer Interface mapping-oriented scientific methods proposed by Zupic Brain Computer Interfaces and Carter in 2015 (Zupic & Carter, 2015). Such analysis BCI of the literature is divided into two steps: the first step Music Time window 2005–2021 corresponds to the design of the research and document Kinds of documents Research papers collection, and the second step corresponds to the ana- Conference papers Review papers lysis, visualization, and interpretation of the results. 228 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES Figure 1. Outline of the literature search process for Scopus database, and classification paper scheme. Table 4. Institutions or organizations with the highest number results, giving a total of 227 splits into 7 review papers, of publications in the BCMI field between 2005 and 2021. 115 research papers, and 105 conference papers. Organizations Number of documents (Figure 1). Nanyang Technological University 9 Information on all selected studies used in this bib- University of California, San Diego 9 liometric review can be found in Table A1. University of Plymouth 8 Ministry of Education China 6 University of Toronto 6 Institute for Neural Computation 6 3. Results Technical University of Berlin 5 Aristotle University of Thessaloniki 5 3.1. Countries, institutions, and sources of Bloorview Kids Rehab 5 Technische Universitat Graz 5 publications The 227 articles selected for the study purpose belong to 56 different countries. Most of the studies were con- Table 5. Publication sources with the highest citations number in the BCMI field between 2005 and 2021. ducted in the United States of America followed by the Number of Number of Impact United Kingdom, China, and Japan. Table 3 shows the Sources of Publication citations documents Factor 10 countries with the highest number of publications in Frontiers in Human Neuroscience 354 7 3209 the time frame. Although the search methodology does Lectures Notes in Computer Science 170 8 1170 Journal of Neural Engineering 166 5 4551 not limit the language of the text, all the collected studies PLoS One 108 5 2776 were published in English. Likewise, these articles were IEEE Transactions on Biomedical 97 2 4780 Engineering published by more than 100 institutions or organiza- IEEE Transactions on Neural Systems 65 2 3478 tions worldwide such as universities, hospitals, labora- and Rehabilitation Engineering tories, and government entities. Table 4 shows the 10 Frontiers in Neuroscience 47 2 3566 Conference on Human Factors in 46 3 - institutions with the highest number of documents pub- Computing Systems – Proceedings lished in the BCMI area between 2005 and 2021. Neurocomputing 44 3 4072 Leonardo 36 2 70 Table 3. Number of documents published by country or These 227 articles were published in different territory. indexed sources, including research articles, conference Countries or territories Number of documents articles, and review articles. Table 5 shows the top 10 United States of America 31 sources of publications ordered according to the num- United Kingdom 19 China 17 ber of citations. Furthermore, this table includes the Japan 15 number of published documents and the impact factor Taiwan 15 Germany 13 these sources had. Table 5 indicates that ‘Frontiers in Canada 12 Human Neuroscience’ is the open-access journal with India 11 Singapore 11 the highest citations index, with 7 publications in the Italy 8 BCMI field and a total of 354 citations. In addition, this BRAIN-COMPUTER INTERFACES 229 table demonstrates that the journal ‘IEEE transactions researcher in this area of research, even though he is not on biomedical engineering’ is the source with the high- the most cited author and has only 70 citations within est academic prestige in the BCMI research field due to the documents analyzed at the search period. This find - its high impact factor, which indicates that this source ing might have come up as a result of the author explor- has had the highest citation rate during the analyzed ing different ways of integrating interpretation, musical time frame. Additionally, this table depicts the evident creation, and art with new technologies, experimenting significance of the conference papers, highlighting the with different kinds of sensors and devices, among sources ‘Lectures Notes in Computer Science’ of the which we find the BCI. On the other hand, when con- German publisher Springer and ‘Conference on ducting a longitudinal analysis of the base documents, it Human Factor in Computing Systems – Proceedings’ was found that Sourina, O., Falk, T., Lin, Y., and Tseng, by ACM. These show that research results can have high K. are currently the most relevant authors in the BCMI visibility through publications at symposiums and con- research field. ferences. Finally, it was observed that the publications of When mapping the co-authorship analysis and mak- the BCMI field in the art area are made mainly in the ing a strategic authorship diagram through the use of journal ‘Leonardo’ of the MIT Press Publishing house. the Scimat mapping tool (Figure 2), it was confirmed The following is the impact factor of the 10 most that the author with the highest development in the relevant journals from 2020 to 2021: Frontiers in research area is Miranda E. The authors who have Human Neuroscience 3169, Sensors 3576, Etri Journal worked on BCMI in an detached way are Edlinger, G., 1347, Frontiers in Neuroinformatics 4081, Frontiers in Ito, S, Fernandez-Soto, A., Naraballobh, J., and Hsu, Neuroscience 4677, Frontiers in Psychology 2990, IEEE J. One of the main emerging authors is Fedotchev, Transactions on Affective Computing 10,506, Journal of A. and the authors with transversal research topics in Ambient Intelligence and Humanized Computing 7104, BCMI are Sourina, O., Falk, T., Lin Y., Adamos, D., Journal of Neural Engineering 5379, and Journal of The Chen, K., and Bai, L. It was noted that the author who Acoustical Society of Korea 1854. According to the is currently working on the motor issues in the area of above, it can be deduced that IEEE Transactions on BCMI is Jin, J. Affective Computing is the most cited in the 2020– To determine the intellectual structure in the field 2021 time period. of BCMI and its evolution over time, a co-authorship and co-citation analysis was made applying the VOSviewer platform. A total of 28 research networks 3.2. Analysis of authors were found, where seven networks, in which only Through citation analysis, 21 researchers in the BCMI field were identified (Table 6). consequently, this analy- sis also showed that Eduardo Miranda was the pioneer Table 6. Researchers with the highest citation rate in the BCMI research field. Author Citations Index H Liu, Yisi 230 16 Sourina, Olga 230 18 Nguyen, Minh Khoa 205 8 Chau, Tom 199 41 Falk, Tiago H. 191 25 Lin, Yuanpin 83 16 Jung, Tzyyping 74 54 Miranda, Eduardo Reck 70 16 Lin, Borshy Shyh 34 17 Tseng, Kevin 34 10 Wang, Qiang 32 6 Williams, Duncan 30 8 Daly, Ian 28 18 Leslie, Grace 27 3 Adamos, Dimitrios A 22 6 Li, Yuanqing 22 26 Folgieri, Raffaella 20 8 Chen, Kun 9 5 Guan, Cuntai 9 44 Liu, Quan 9 18 Figure 2. Strategic diagram of authors in the time frame of Fedotchev, Alexander Ivanovitch 7 7 2005–2021. 230 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES Figure 3. Visualization of research networks and their evolution over time. Co-authorship analysis (Figure generated in VOSviewer). one author participated, were excluded. These To establish the distribution of the authors according research networks have made it possible to identify to the research area, a co-citation analysis was carried collaborative communities. Figure 3 presents the co- out in VOSviewer software (Figure 4), finding four net- authorship analysis mapping developed on the works of authors. The networks of authors around VosViewer platform. In this image, it can be seen Blankertz, B. (green network) and Pfurtscheller, that there is only collaboration between two groups G. (blue network), include researchers who worked in of authors through the Daly I. Researcher. This col- the field of augmented cognition., where sound and laboration is evidenced in Table 7 through 1 and 2 music were used as control elements for external appli- networks and it is also observed that the other cations. The network, whose axis is Miranda E (yellow authorship groups are isolated. network), focuses its work on the integration of the BCI with the creation of music and sound in different areas of knowledge. The Jung T.P. network (red network), reflects the use of sound as a stimulus in applications Table 7. Researcher networks in the research field at BCMI. with BCI based on the field of cognitive neuroscience. Network Researchers 1 Brouse, A.; Daly, I.; Eaton, J.; Hwang, F.; Kirke, A.; Malik, A.; Miranda, E.; Nasuto, S. J.; Weaver, J; Williams, D 3.3. Analysis of research areas and research 2 Ai, Q.; Chen, K.; Cichocki, A.; Daly, I.; Jin, J.; Wang, X.: Zhang, Y. keywords 3 Chau, T; Falk, T. H.; Gupta, R.; Kushki, A.; Power, S. D.; Tiwari, A. 4 Bai, L.; Cheng, G. Guan, C.; Li, Y.; Yu, T. _ To determine the dynamics of the research areas and 5 Edlinger, G.; Groenegress, C.; Guger, C.; Holzner, C; Slater, M. 6 Fujisawa, S.; Fukumi, M.; Ito, S; Mitsukura, Y.; Sato, K.; topics, a publication analysis according to their topics 7 Jacob, R.; Leslie, G.; Makeig, S; Nijholt, A.; Yuksel, B. and a keyword co-occurrence was conducted. 8 Chiu, Y.; Hsu, J.; Lin, T; Zhen, Y. The analysis of research areas, in the time period 9 Liu, Y.; Nguyen, M; Sourina; O.; Wang, G. 10 Chinrungrueng, J.; Naraballobh, J.; Nishihara, A; Thanapatay, D. from 2005 to 2021 (Figure 5), showed that the predo- 11 Jung, T.; Lin, Y.; Yang, Y. minant research areas in the field of BCMI were com- 12 Fernandez-Caballero, A.; Martínez-Rodrigo, A.; Moncho-Bogani, J. puter science, engineering, neuroscience, and medicine, 13 Fedotchev, A.; Polevaya, S.; Velikova, S. with published articles showing percentages of 31,0%, 14 Adamos, D.; Laskaris, N. 15 Goh, S.; Tan, L. 19,3%, 11,8%, and 8% respectively. Areas of arts and 16 Fels, S.; Lyons, M. humanities were left behind with a publication rate of 17 Park, S.; Sim, K. 18 Lin, B; Tseng, K. 3,7%, after confirming the results found in the analysis 19 Pinegger, A; Wriessnegger, S. of published sources. In addition, it was observed that 20 Scherer, R.; Zander, T. a wide variety of areas of knowledge, such as 21 Folgieri, R.; Zichella, M. BRAIN-COMPUTER INTERFACES 231 Figure 4. Map of authors obtained from the co-citation analysis. Figure 5. Analysis of the thematic areas of the BCMI research field in the period 2005–2021. mathematics, biochemistry, genetics and molecular techniques, and communication interfaces, among biology, psychology, decision sciences, physics, and others. The second group (green network) refers to the astronomy, worked in the field of BCMI, reflecting the human being and to the configuration of the experi- interdisciplinarity of this field of research. mentation protocol, where the gender of the users, their When a keyword co-occurrence analysis was carried age ranges, type of stimuli, type of experiment (con- out, a total of 5 general clusters of topics were found, all trolled or uncontrolled), and validation techniques such connected by the term ‘Brain-Computer Interface’ as as questionnaires were identified. The third group iden- shown in Figure 6. The first group of terms (red net- tified (blue network) represents the brain functions work) covers the mechanisms for measuring physiolo- considered in the research, such as sound imagination gical signals according to the sound response including processes, musical learning techniques, task develop- types of devices and physiological signals, feedback ment through BCI interfaces, and sound navigation 232 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES Figure 6. Visualization of keyword analysis in the area of brain-computer Music interfaces in the time period 2005–2021. systems, among others. The fourth group (yellow net- cognitive process studied. Likewise, the main applica- work) identifies types of brain signals such as event- tions referenced by the authors, including modulation related potentials (ERP), visual evoked potentials of compositional patterns, classification of mental states, (SSVEP), and brain rhythms. Finally, the last group and patient rehabilitation, are listed in Table 8. found (violet network), relates the techniques of extrac- tion and classification of patterns based on machine 4. Discussion learning techniques, necessary in research processes based on BCI, which require the application of classifi - Because of the work done by Adrian and Matthews in cation methods based on artificial intelligence, which 1934, several researchers from different disciplines have allow better control of external applications or devices conducted a variety of studies relating human brain through the interface. activity to sound and music in various types of applica- In addition, within the analysis of co-occurrence of tions, using brain-computer interfaces as keywords (Table 8), the acquisition, processing and a communication channel between the brain and the classification techniques used by the authors were external device. Based on this is how this research pro- found, as well as the applications, brain regions and vides the intellectual structure, the trends in research, cognitive processes analyzed by the authors. In terms the main sources of publication, the academic origin, of signal acquisition techniques, it is remarkable how and the structures of authors in the BCMI research field, the NIRS technique has been adapted together with the according to the scientific mapping-oriented methods BCI interfaces. In terms of pattern extraction techni- proposed by Zupic and Carter in 2015 (Zupic & Carter, ques, the use of spectral and temporal techniques is 2015). The search performed with the parameters pre- evidenced, as well as the implementation of sound char- sented in Table 2, yielded a total of 220 documents, acterization techniques for the processes involved in the where after applying the inclusion and exclusion cri- creation and control of external systems. As evidenced teria, 187 documents remained, with which the corre- by the keyword network analysis (Figure 6), several sponding bibliometric analysis was performed. supervised classification techniques were found, ranging The results of the analysis of organizations and pub- from artificial neural networks and linear discriminant lication origins showed that more than 100 worldwide networks to deep learning techniques. Table 8 also pro- organizations, including universities, hospitals, labora- vides evidence of the brain regions involved in the tories, research centers, and others, have published in studies, including motor, auditory and visual processing the BCMI area in the time frame between 2005 and areas, depending on the type of stimulation and the 2021. In addition, it was found that 17% of the works BRAIN-COMPUTER INTERFACES 233 Table 8. Keyword metadata analysis. Systems and Rehabilitation Engineering, continue to Target Technique have a high publication rate, because of their trajectory Signal Acquisition ● Functional Near Infra-Red Spectrometer in the research area. This analysis also showed that the Techniques (fNIRS) journal ‘Leonardo’ was the only indexed art and music Electroencephalography (EEG) Brain Computer Interface (BCI) journal that has publications in the BCMI research field. Pattern extraction and Spectrogram analysis This result allows two hypotheses to be formulated. The characterization Zhao-Atlas marks distributions Hilbert – Huang Spectrum (HHS) first hypothesis suggests that a limited number of musi- EEG rhythm analysis cians and composers have the support of an interdisci- Asymmetric hemisphere response Spectral Power Density plinary research group that allows them to explore new Canonical Correlation Analysis (CCA) compositional techniques based on the control of sound Event Related Potentials (P300 from physiological signals; the second hypothesis sug- component) Event – Related Desynchronization gests that there seems to be a low interest of traditional (ERD) musicians and composers in this type of research. These Cronbach’s alpha coefficients Wavelet – based peak detections hypotheses are supported by the results obtained in the Cross frequency coupling (CFC) analysis of research areas, where it was observed that Spatial and temporal dynamic analysis Independent component analysis only 4% of the publications belong to the research field Principal component analysis of arts and humanities, while about 61% of the papers Machine Learning Conditional Transfer Learning (cTL) Techniques Markov model -based classifier cover the areas of computer science, engineering, and Augmented Transitions Networks medicine. Regularized Linear Discriminant analysis Within the methodology of the analysis presented in (RLDA) classifier Convolutional neural network (CNN) this article, it was proposed to determine who were the Long-short term memory network main experts and what was the intellectual and social (LSTM) Artificial Neural Networks (ANN) structure of authors in the area of BCMI, through cita- Brain Regions Pre-frontal cortex (PFC) tion, co-citation, and co-authorship analysis. The results Anterior Cingulate Cortex (ACC) Motor Cortex (MC) of this analysis showed that the most representative Temporoparietal cortex researchers are E., Sourina, O., Falk, T, Lin Y, Tseng, Visual Cortex (VC) Cognitive Process Motor Imagery K., Jong K. S., Chau T., Nguyen M. K., Liu Y. And Power Stimuli Sound stimuli S. D. The results of the co-authorship analysis showed Music stimuli ● that the research networks with the highest number of Visual stimuli General Application Modulate music composition patterns authors are networks 1, 2 and 3 (Table 7), and it was Classifications of mental states found that networks 1 and 2 are connected to each other Control external devices and systems Music composition by author Daly I., which allows inferring that there are Enhance Music production several research collaborations between these groups. Rehabilitation and disease treatment This co-authorship analysis also revealed that most of the research networks are isolated from each other, either because of their geographic location or the research focus of these groups. were published in North America, followed by the Finally, the analysis of co-occurrence of keywords United Kingdom, China, Japan, and Germany. This showed that most of the publications have a similar demonstrates that although this field of research is of experimental methodological structure, composed by high global interest from a technological, therapeutic, the acquisition of the brain signal, the processing and and commercial perspective, first-world countries con- extraction of signal patterns and the classification of the tinue leading the development of this research field. signal according to the purpose of the study carried out. The analysis of publication sources showed that the Furthermore, this analysis showed that although the journal ‘Lecture Notes of computer science’ is the source search was focused on analysis of BCI and Music or with the highest number of publications, being this BCMI, where EEG signals are processed, it was found a reference for conference and symposium papers, fol- that some authors complemented their studies with lowed by the open-access journal ‘Frontier in Human multimodal acquisition processes by adapting the Neuroscience’, evidencing the impact that this publica- NIRS technique within their signal acquisition pro- tion source has on researchers, due to their high rate of cesses. In addition, it was observed that the authors knowledge dissemination. Nevertheless, it should be used supervised learning techniques, which encompass noted that sources such as IEEE transactions on biome- traditional techniques such as artificial neural networks, dical engineering and IEEE transactions on Neural to deep learning techniques such as convolutional 234 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES neural networks. Moreover, in the keyword co- Science of the Colombian Government, National Doctorate - 757Doctorado Nacional, Gobierno de Colombia (CO); occurrence analysis it was noted that the studies are Doctorado Nacional, Gobierno de Colombia (CO) [757]; divided into two main groups, the first group seeks to evaluate the brain response of users to sound or musical stimuli in medical applications, whereas the second ORCID group looks to control musical or sound systems based Héctor Fabio Torres-Cardona http://orcid.org/0000-0001- on the human physiological response in augmented 9758-4038 cognition applications. 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Authors Year Source Title Miranda E.R., Brouse A. 2005 Leonardo Interfacing the brain directly with musical systems: On developing systems for making music with brain signals Zhan X.D., Kang T., Choi H.R. 2005 Journal of Mechanical Science and Technology An approach for pattern recognition of hand activities based on EEG and fuzzy neural network Miranda E.R., Brouse A., Boskamp B., Mullaney H. 2005 International Computer Music Conference, ICMC Plymouth brain-computer music interface 2005 project: Intelligent assistive technology for music-making Miranda E.R. 2006 International Journal on Disability and Human Brain-Computer music interface for composition Development and performance Huang Y.-C. 2006 CAADRIA 2006 – The Association for Computer- A space make you lively: A brain-computer Aided Architectural Design Research in Asia: interface approach to smart space Rhythm and Harmony in Digital Space Teo E., Huang A., Lian Y., Guan C., Li Y., Zhang H. 2006 Annual International Conference of the IEEE Media communication center using brain- Engineering in Medicine and Biology – computer interface Proceedings Lachaux J.-P., Jerbi K., Bertrand O., Minotti L., 2007 PLoS ONE A Blueprint for Real-Time Functional Mapping Hoffmann D., Schoendorff B., Kahane P. via Human Intracranial Recordings Zhao H.-B., Wang H. 2007 Xitong Fangzhen Xuebao/Journal of System Research of brain-computer interface based on Simulation PSD and ANN Khachab M., Kaakour S., Mokbel C. 2007 2007 4th IEEE International Symposium on Brain imaging and support vector machines for Biomedical Imaging: From Nano to Macro – brain computer interface Proceedings Solis-Escalante T., Gentiletti G.G., Yanez-Suarez 2007 Proceedings of the 3rd International IEEE EMBS Detection of steady-state visual evoked O. Conference on Neural Engineering potentials based on the multisignal classification algorithm Swift B., Sheridan J., Zhen Y., Gardner H.J. 2007 Australasian Computer-Human Interaction Mind-modulated music in the Mind Attention Conference, OZCHI’07 Interface Veekmans K., Ressel L., Mueller J., Vischer M., 2009 Audiology and Neurotology Comparison of music perception in bilateral and Brockmeier S.J. unilateral cochlear implant users and normal- hearing subjects Edlinger G., Holzner C., Guger C., Groenegress C., 2009 2009 4th International IEEE/EMBS Conference on Brain-computer interfaces for goal orientated Slater M. Neural Engineering, NER ‘09 control of a virtual smart home environment Antonietti A. 2009 Studies in Health Technology and Informatics Why is music effective in rehabilitation? Jovanovic A., Klonowski W., Duch W., Perovic A. 2009 Computational Intelligence and Neuroscience Some computational aspects of the brain computer interfaces based on inner music Edlinger G., Krausz G., Groenegress C., Holzner 2009 IFMBE Proceedings Brain-Computer Interfaces for Virtual C., Guger C., Slater M. Environment Control Miranda E.R., Matthias J. 2009 Leonardo Music neurotechnology for sound synthesis: Sound synthesis with spiking neuronal networks Ito S.-I., Mitsukura Y., Sato K., Fujisawa S., 2009 IECON Proceedings (Industrial Electronics A study on relationship between personal Fukumi M. Conference) feature of EEG and human’s characteristic for BCI based on mental state Power S.D., Falk T.H., Chau T. 2010 Journal of Neural Engineering Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near- infrared spectroscopy Hamadicharef B., Xu M., Aditya S. 2010 Proceedings – 2010 International Conference on Brain-Computer Interface (BCI) based musical Cyberworlds, CW 2010 composition Looney D., Park C., Xia Y., Kidmose P., Ungstrup 2010 Proceedings of the International Joint Toward estimating selective auditory attention M., Mandic D.P. Conference on Neural Networks from EEG using a novel time-frequency- synchronization framework Ito S.-I., Mitsukura Y., Sato K., Fujisawa S., 2010 Study on relationship between personality and Proceedings – 2010 IEEE Region 8 International Fukumi M. individual characteristic of EEG for Conference on Computational Technologies personalized BCI in Electrical and Electronics Engineering, SIBIRCON-2010 Rached T.S., De S. Santos D.F., Perkusich A., 2010 2010 International Conference on Information BCI-aware pervasive multimedia for motor Almeida H., De Almeida Holanda M.M. Society, i-Society 2010 disabled people Park S.-M., Park J.-H., Sim K.-B. 2010 SCIS and ISIS 2010 – Joint 5th International A study on brain information processing Conference on Soft Computing and mechanism for music genre distinction Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems Liu Y., Sourina O., Nguyen M.K. 2011 Lecture Notes in Computer Science (including Real-time EEG-based emotion recognition and subseries Lecture Notes in Artificial its applications Intelligence and Lecture Notes in Bioinformatics) (Continued) BRAIN-COMPUTER INTERFACES 237 Table A1. (Continued). Authors Year Source Title Falk T.H., Guirgis M., Power S., Chau T.T. 2011 IEEE Transactions on Neural Systems and Taking NIRS-BCIs outside the lab: Toward Rehabilitation Engineering achieving robustness against environment noise Makeig S., Leslie G., Mullen T., Sarma D., Bigdely- 2011 Lecture Notes in Computer Science (including First demonstration of a musical emotion BCI Shamlo N., Kothe C. subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vlek R.J., Schaefer R.S., Gielen C.C.A.M., Farquhar 2011 Journal of Neural Engineering Sequenced subjective accents for brain- J.D.R., Desain P. computer interfaces Sourina O., Wang Q., Liu Y., Nguyen M.K. 2011 BIOSIGNALS 2011 – Proceedings of the A real-time fractal-based brain state recognition International Conference on Bio-Inspired from EEG and its applications Systems and Signal Processing Sourina O., Liu Y., Wang Q., Nguyen M.K. 2011 Lecture Notes in Computer Science (including EEG-based personalized digital experience subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Park S.-M., Sim K.-B. 2011 Proceedings – 2011 8th International A study on the analysis of auditory cortex active Conference on Fuzzy Systems and Knowledge status by music genre: Drawing on EEG Discovery, FSKD 2011 Chew Y.C., Caspary E. 2011 Conference on Human Factors in Computing MusEEGk: A brain computer musical interface Systems – Proceedings Sourina O., Wang Q., Liu Y., Nguyen M.K. 2011 Communications in Computer and Information Fractal-Based Brain State Recognition from EEG Science in Human Computer Interaction Huang Y.-C. 2011 Advanced Materials Research How human-computer interface redefines original lifestyle in architecture? Zander T.O., Klippel M.D., Scherer R. 2011 ICMI’11 – Proceedings of the 2011 ACM Toward multimodal error responses: A passive International Conference on Multimodal BCI for the detection of auditory errors Interaction Chew Y.C.D., Caspary E. 2011 C and C 2011 – Proceedings of the 8th ACM MusEEGk: Design of a BCMI Conference on Creativity and Cognition Dobriyal M., Yilmazer N., Challoo R. 2011 Conference Proceedings – IEEE International Performance analysis of spectral estimation Conference on Systems, Man and Cybernetics techniques for steady State Visual Evoked Potentials (SSVEPs) based Brain Computer Interfaces (BCIs) Fels S., Lyons M. 2011 SIGGRAPH Asia 2011 Courses, SA’11 Advances in new interfaces for musical expression Hadjidimitriou S.K., Hadjileontiadis L.J. 2012 IEEE Transactions on Biomedical Engineering Toward an EEG-based recognition of music liking using time-frequency analysis Power S.D., Kushki A., Chau T. 2012 BMC Research Notes Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: Toward a three- state NIRS-BCI Sourina O., Liu Y., Nguyen M.K. 2012 Journal on Multimodal User Interfaces Real-time EEG-based emotion recognition for music therapy Liu Y., Sourina O. 2012 Proceedings – IEEE International Conference on EEG-based dominance level recognition for Multimedia and Expo emotion-enabled interaction Moghimi S., Kushki A., Guerguerian A.M., Chau T. 2012 Neuroscience Letters Characterizing emotional response to music in the prefrontal cortex using near infrared spectroscopy Folgieri R., Zichella M. 2012 Computers in Entertainment A BCI-based application in music: Conscious playing of single notes by brainwaves Tseng K.C., Wang Y.-T., Lin B.-S., Hsieh P.H. 2012 Proceedings of the 2012 8th International Brain computer interface-based multimedia Conference on Intelligent Information Hiding controller and Multimedia Signal Processing, IIH-MSP Zhao L., Guo X. 2012 Proceedings – 5th International Conference on EEG control of music player Intelligent Networks and Intelligent Systems, ICINIS 2012 Folgieri R., Zichella M. 2012 Computers in Entertainment A BCI-based application in music: Conscious playing of single notes by brainwaves Kakegawa M., Komiyama R., Masakura Y., Kikuchi 2012 6th International Conference on Soft Computing Analysis of music appreciation by Kansei M. and Intelligent Systems, and 13th evaluation and brain activity International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012 Aquino R.J., Battad J.R., Ngo C.F., Uy G., Trogo R., 2012 Lecture Notes in Computer Science (including Toward providing music for academic and Legaspi R., Suarez M.T. subseries Lecture Notes in Artificial leisurely activities of computer users Intelligence and Lecture Notes in Bioinformatics) Blain-Moraes, S; Chesser, S; Kingsnorth, S; 2013 Augmentative and Alternative Communication Biomusic: A Novel Technology for Revealing the Mckeever, P; Biddiss, E Personhood of People with Profound Multiple Disabilities (Continued) 238 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES Table A1. (Continued). Authors Year Source Title Moon, J; Kim, Y; Lee, H; Bae, C; Yoon, WC 2013 ETRI Journal Extraction of User Preference for Video Stimuli Using EEG-Based User Responses Friedrich E.V.C., Scherer R., Neuper C. 2013 Clinical Neurophysiology Stability of event-related (de-) synchronization during brain-computer interface-relevant mental tasks Tseng K.C., Lin B.-S., Han C.-M., Wang P.-S. 2013 ICOT 2013–1st International Conference on Emotion recognition of EEG underlying favorite Orange Technologies music by support vector machine Leslie G., Ojeda A., Makeig S. 2013 Proceedings – 2013 Humaine Association Toward an affective brain-computer interface Conference on Affective Computing and monitoring musical engagement Intelligent Interaction, ACII 2013 Lyons M., Fels S. 2013 SIGGRAPH Asia 2013 Courses, SA 2013 Creating New Interfaces for Musical Expression Morita Y., Huang H.-H., Kawagoe K. 2013 2013 IEEE/ACIS 12th International Conference Toward Music Information Retrieval driven by on Computer and Information Science, ICIS EEG signals: Architecture and preliminary 2013 – Proceedings experiments Uma M., Sridhar S.S. 2013 2013 International Conference on Human A feasibility study for developing an emotional Computer Interactions, ICHCI 2013 control system through brain computer interface Kasabov N., Hu J., Chen Y., Scott N., Turkova Y. 2013 Lecture Notes in Computer Science (including Spatio-temporal EEG data classification in the subseries Lecture Notes in Artificial NeuCube 3D SNN environment: Methodology Intelligence and Lecture Notes in and examples Bioinformatics) Effelsberg W. 2013 ACM Transactions on Multimedia Computing, A personal look back at twenty years of research Communications and Applications in multimedia content analysis Soriano D., Silva E.L., Slenes G.F., Lima F.O., Uribe 2013 ISSNIP Biosignals and Biorobotics Conference, Music versus motor imagery for BCI systems L.F.S., Coelho G.P., Rohmer E., Venancio T.D., BRC a study using fMRI and EEG: Preliminary Beltramini G.C., Campos B.M., Anjos C.A.S., results Suyama R., Li L.M., Castellano G., Attux R. Milsap G., Fifer M., Crone N., Thakor N. 2013 International IEEE/EMBS Conference on Neural Listening to the music of the brain: Live analysis Engineering, NER of ECoG recordings using digital audio workstation software Dhital A., Banic A.U. 2013 Proceedings – IEEE Virtual Reality Navigation in a virtual environment by dichotic listening: Simultaneous audio cues for user- directed BCI classification Lyons M.J., Tomonaga T. 2013 SIGGRAPH Asia 2013 Posters, SA 2013 Enactive Mandala: Sonigraphical brainwave display Jovanović A., Perović A. 2013 SISY 2013 – IEEE 11th International Symposium Structural features in brain signals and weak on Intelligent Systems and Informatics, brain connectivity Proceedings Tan L.-F., Goh S.-Y. 2013 Proceedings of the IADIS International Mental training on Brain-Computer interface Conferences – Interfaces and Human users Computer Interaction 2013, IHCI 2013 and Game and Entertainment Technologies 2013, GET 2013 Rui X., Li Y., Li D. 2013 2013 ICME International Conference on Complex Looking at beauties – Another possibility to Medical Engineering, CME 2013 brain computer interface? Dhital A., Banic A. 2013 2013 1st Workshop on Virtual and Augmented Navigation path differences for dichotic Assistive Technology, VAAT 2013; Co-located listening BCI in virtual environments with the 2013 Virtual Reality Conference – Proceedings Wang, XW; Nie, D; Lu, BL 2014 18th International Conference on Neural Emotional state classification from EEG data Information Processing (ICONIP), using machine learning approach Neurocomputing Jie X., Cao R., Li L. 2014 Bio-Medical Materials and Engineering Emotion recognition based on the sample entropy of EEG Liu, TM; Hu, XT; Li, XJ; Chen, M; Han, JW; Guo, L 2014 IEEE Transactions on Human-Machine Systems Merging Neuroimaging and Multimedia: Methods, Opportunities, and Challenges Lin Y.-P., Yang Y.-H., Jung T.-P. 2014 Frontiers in Neuroscience Fusion of electroencephalographic dynamics and musical contents for estimating emotional responses in music listening Tan L.-F., Dienes Z., Jansari A., Goh S.-Y. 2014 Consciousness and Cognition Effect of mindfulness meditation on brain- computer interface performance Treder M.S., Purwins H., Miklody D., Sturm I., 2014 Journal of Neural Engineering Decoding auditory attention to instruments in Blankertz B. polyphonic music using single-trial EEG classification Gibson R.M., Chennu S., Owen A.M., Cruse D. 2014 Clinical Neurophysiology Complexity and familiarity enhance single-trial detectability of imagined movements with electroencephalography Charland-Verville V., Lesenfants D., Sela L., 2014 Frontiers in Human Neuroscience Detection of response to command using Noirhomme Q., Ziegler E., Chatelle C., Plotkin voluntary control of breathing in disorders of A., Sobel N., Laureys S. consciousness (Continued) BRAIN-COMPUTER INTERFACES 239 Table A1. (Continued). Authors Year Source Title Folgieri R., Zampolini R. 2014 Computers in Entertainment Bci promises in emotional involvement in music and games Kroupi E., Vesin J.-M., Ebrahimi T. 2014 Brain-Computer Interfaces Implicit affective profiling of subjects based on physiological data coupling Bulaj G. 2014 Frontiers in Neurology Combining non-pharmacological treatments with pharmacotherapies for neurological disorders: A unique interface of the brain, drug-device, and intellectual property Daly I., Williams D., Hwang F., Kirke A., Malik A., 2014 Brain-Computer Interfaces Investigating music tempo as a feedback Roesch E., Weaver J., Miranda E., Nasuto S.J. mechanism for closed-loop BCI control Chen K., Liu Q., Ai Q.S. 2014 Applied Mechanics and Materials Multi-channel SSVEP pattern recognition based on MUSIC Cádiz R.F., De La Cuadra P. 2014 Proceedings – 40th International Computer Kara: A BCI approach to composition Music Conference, ICMC 2014 and 11th Sound and Music Computing Conference, SMC 2014 – Music Technology Meets Philosophy: From Digital Echos to Virtual Ethos Rosenboom D. 2014 Frontiers in Neuroscience Active imaginative listening-a neuromusical critique Christopher K.R., Kapur A., Carnegie D.A., 2014 Proceedings – 40th International Computer A history of emerging paradigms in EEG for Grimshaw G.M. Music Conference, ICMC 2014 and 11th Sound music and Music Computing Conference, SMC 2014 – Music Technology Meets Philosophy: From Digital Echos to Virtual Ethos Vamvakousis Z., Ramirez R. 2014 Proceedings – 40th International Computer P300 harmonies: A Brain-Computer Musical Music Conference, ICMC 2014 and 11th Sound Interface and Music Computing Conference, SMC 2014 – Music Technology Meets Philosophy: From Digital Echos to Virtual Ethos Bai L., Yu T., Li Y. 2014 Proceedings of the International Joint Explorer based on brain computer interfac Conference on Neural Networks Liberati, G; Federici, S; Pasqualotto, E 2015 Neurorehabilitation Extracting neurophysiological signals reflecting users’ emotional and affective responses to BCI use: A systematic literature review Naseer N., Hong K.-S. 2015 Frontiers in Human Neuroscience fNIRS-based brain-computer interfaces: A review Cordes J.S., Mathiak K.A., Dyck M., Alawi E.M., 2015 Frontiers in Behavioral Neuroscience Cognitive and neural strategies during control of Gaber T.J., Zepf F.D., Klasen M., Zvyagintsev the anterior cingulate cortex by fMRI M., Gur R.C., Mathiak K. neurofeedback in patients with schizophrenia Kovacevic N., Ritter P., Tays W., Moreno S., 2015 PLoS ONE ‘My virtual dream’: Collective neurofeedback in McIntosh A.R. an immersive art environment Bai L., Yu T., Li Y 2015 Journal of Neuroscience Methods A brain computer interface-based explorer Fouad M.M., Amin K.M., El-Bendary N., Hassanien 2015 Intelligent Systems Reference Library Brain computer interface: A review A.E. Wadeson A., Nijholt A., Nam C.S. 2015 Brain-Computer Interfaces Artistic brain-computer interfaces: state-of-the- art control mechanisms Tseng K.C., Lin B.-S., Wong A.M.-K., Lin B.-S. 2015 Sensors (Switzerland) Design of a mobile brain computer interface- based smart multimedia controller Mahajan R., Bansal D. 2015 International Journal of Biomedical Engineering Depression diagnosis and management using and Technology EEG-based affective brain mapping in real time Eaton J., Williams D., Miranda E. 2015 Brain-Computer Interfaces The Space Between Us: Evaluating a multi-user affective brain-computer music interface Lin Y.-P., Hsu S.-H., Jung T.-P. 2015 Lecture Notes in Computer Science (including Exploring day-to-day variability in the relations subseries Lecture Notes in Artificial between emotion and EEG signals Intelligence and Lecture Notes in Bioinformatics) Lancioni G.E., Simone I.L., De Caro M.F., Singh N. 2015 NeuroRehabilitation Assisting persons with advanced amyotrophic N., O’Reilly M.F., Sigafoos J., Ferlisi G., Zullo V., lateral sclerosis in their leisure engagement Schirone S., Denitto F., Zonno N. and communication needs with a basic technology-aided program Hsu J.-L., Zhen Y.-L., Lin T.-C., Chiu Y.-S. 2015 Proceedings – 2014 IEEE International Personalized music emotion recognition using Symposium on Multimedia, ISM 2014 electroencephalography (EEG) Naraballobh J., Thanapatay D., Chinrungrueng J., 2015 2015 6th International Conference on EEG-based analysis of auditory stimulus in Nishihara A. Information and Communication Technology a brain-computer interface for Embedded Systems, IC-ICTES 2015 Pinegger A., Wriessnegger S.C., Muller-Putz G.R. 2015 Proceedings of the Annual International Sheet music by mind: Toward a brain-computer Conference of the IEEE Engineering in interface for composing Medicine and Biology Society, EMBS (Continued) 240 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES Table A1. (Continued). Authors Year Source Title Naraballobh J., Thanapatay D., Chinrungrueng J., 2015 ECTI-CON 2015–2015 12th International Effect of auditory stimulus in EEG signal using Nishihara A. Conference on Electrical Engineering/ a Brain-Computer Interface Electronics, Computer, Telecommunications and Information Technology Politis D., Tsaligopoulos M., Kyriafinis G. 2015 Proceedings of 2014 International Conference Dialectic & reconstructive musicality: Stressing on Interactive Mobile Communication the brain-computer interface Technologies and Learning, IMCL 2014 Matossian V., Gehlhaar R. 2015 Annual Review of CyberTherapy and Human instruments: Accessible musical Telemedicine instruments for people with varied physical ability Bansal D., Sarkar A. 2015 11th IEEE India Conference: Emerging Trends HMM based predictive model of brain computer and Innovation in Technology, INDICON 2014 interface Crowley K., McDermott J. 2015 Proceedings of the 12th International Mapping brain signals to music via executable Conference in Sound and Music Computing, graphs SMC 2015 Tavares T.F., Rimoldi G., Pontes V.E., Manzolli J. 2015 Proceedings of the 12th International Cooperative musical creation using kinect, Conference in Sound and Music Computing, WiiMote, Epoc and microphones: A case study SMC 2015 with MinDSounDS De Quay Y., Beira J. 2015 41st International Computer Music Conference, Brain-computer interfaces and their application ICMC 2015: Looking Back, Looking Forward – as an audiovisual instrument Proceedings Hu J., Mu Z., Yin J. 2015 Metallurgical and Mining Industry Framework of music Controller Based on Brain Computer interface Chen H.-M., Chen S.-Y., Jheng T.-J., Chang S.-C. 2015 Lecture Notes in Electrical Engineering Design of a mobile brain-computer interface system with personalized emotional feedback Blankertz, B; Acqualagna, L; Dahne, S; Haufe, S; - 2016 Frontiers in Neuroscience The Berlin Brain-Computer Interface: Progress Kraft, MS; Sturm, I; Uscumlic, M; Wenzel, MA; Beyond Communication and Control Curio, G; Muller, KR Yuksel B.F., Oleson K.B., Harrison L., Peck E.M., 2016 Conference on Human Factors in Computing Learn piano with BACh: An adaptive learning Afergan D., Chang R., Jacob R.J.K. Systems – Proceedings interface that adjusts task difficulty based on brain state Morillo, LMS; Alvarez-Garcia, JA; Gonzalez-Abril, 2016 3rd International Work-Conference on Discrete classification technique applied to TV L; Ramirez, JAO Bioinformatics and Biomedical Engineering advertisements liking recognition system (IWBBIO). Biomedical Engineering Online based on low-cost EEG headsets Gupta R., ur Rehman Laghari K., Falk T.H. 2016 Neurocomputing Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization Daly I., Williams D., Kirke A., Weaver J., Malik A., 2016 Journal of Neural Engineering Affective brain-computer music interfacing Hwang F., Miranda E., Nasuto S.J. Norman S.L., Dennison M., Wolbrecht E., Cramer 2016 IEEE Transactions on Neural Systems and Movement Anticipation and EEG: Implications S.C., Srinivasan R., Reinkensmeyer D.J. Rehabilitation Engineering for BCI-Contingent Robot Therapy Adamos D.A., Dimitriadis S.I., Laskaris N.A. 2016 Information Sciences Toward the bio-personalization of music recommendation systems: A single-sensor EEG biomarker of subjective music preference Zhou S., Allison B.Z., Kübler A., Cichocki A., Wang 2016 Frontiers in Computational Neuroscience Effects of background music on objective and X., Jin J. subjective performance measures in an auditory BCI Huang M., Daly I., Jin J., Zhang Y., Wang X., 2016 Cognitive Neurodynamics An exploration of spatial auditory BCI paradigms Cichocki A. with different sounds: music notes versus beeps Hossan A., Chowdhury A.M.M. 2016 2016 5th International Conference on Real time EEG based automatic brainwave Informatics, Electronics and Vision, ICIEV 2016 regulation by music Kalaganis F., Adamos D.A., Laskaris N. 2016 IFIP Advances in Information and A consumer BCI for automated music evaluation Communication Technology within a popular on-demand music streaming service ‘taking listener’s brainwaves to extremes’ Zhang J., Huang X., Yang L., Nie L. 2016 Neurocomputing Bridge the semantic gap between pop music acoustic feature and emotion: Build an interpretable model Chavan D.R., Kumbhar M.S., Chavan R.R. 2016 2016 International Conference on Computation The human stress recognition of brain, using of Power, Energy, Information and music therapy Communication, ICCPEIC 2016 Chen K., Liu Q., Ai Q., Zhou Z., Xie S.Q., Meng W. 2016 Australasian Physical and Engineering Sciences A MUSIC-based method for SSVEP signal in Medicine processing Zerafa R., Camilleri T., Falzon O., Camilleri K.P. 2016 IFMBE Proceedings A real-time SSVEP-based brain-computer interface music player application Sugiono S., Rudy S., Denny W. 2016 Acta Neuropsychologica Investigating the impact of environment noise and music on the human brain by using a brain-computer interface (BCI) (Continued) BRAIN-COMPUTER INTERFACES 241 Table A1. (Continued). Authors Year Source Title Ali A.H., Al-Musawi R.S.H. 2016 Al-Sadiq International Conference on Investigating the possibility of using a single Multidisciplinary in IT and Communication electrode brain-computer interface device for Techniques Science and Applications, AIC- human machine interaction by means of MITCSA 2016 cluster analysis Melinscak F., Montesano L. 2016 Journal of Neuroscience Methods Beyond p-values in the evaluation of brain- computer interfaces: A Bayesian estimation approach Cincuegrani S.M., Jordà S., Väljamäe A. 2016 ACM Transactions on Computer-Human Physiopucks: Increasing user motivation by Interaction combining tangible and implicit physiological interaction Chen K., Xu F., Liu Q., Liu H., Zhang Y., Ma L., Ai 2016 Journal of Computing and Information Science SSVEP Recognition by Using Higher Harmonics Q. in Engineering Based on Music Eaton J., Miranda E.R. 2016 Lecture Notes in Computer Science (including The hybrid brain computer music interface – subseries Lecture Notes in Artificial Integrating brainwave detection methods for Intelligence and Lecture Notes in extended control in musical performance Bioinformatics) systems Lin Y.-P., Jung T.-P. 2017 Frontiers in Human Neuroscience Improving EEG-based emotion classification using conditional transfer learning Folgieri, R 2017 Biolaw Journal-Rivista di Biodiritto Brain computer interface and transcranical stimulation: frontiers, reliability, safety and threats Heo J., Baek H.J., Hong S., Chang M.H., Lee J.S., 2017 Computers in Biology and Medicine Music and natural sounds in an auditory steady- Park K.S. state response based brain–computer interface to increase user acceptance Zerafa, R; Camilleri, T; Bartolo, K; Camilleri, KP; 2017 Biomedical Physics & Engineering Express Reducing the training time for the SSVEP-based Falzon, O music player application Yanagimoto M., Sugimoto C. 2017 2016 IEEE 9th International Workshop on Recognition of persisting emotional valence Computational Intelligence and Applications, from EEG using convolutional neural IWCIA 2016 – Proceedings networks Pinegger A., Hiebel H., Wriessnegger S.C., 2017 PLoS ONE Composing only by thought: Novel application Müller-Putz G.R. of the P300 brain-computer interface Lin Y.-P., Jao P.-K., Yang Y.-H. 2017 Frontiers in Computational Neuroscience Improving cross-day EEG-based emotion classification using robust principal component analysis Martínez-Rodrigo A., Fernández-Sotos A., Miguel 2017 Frontiers in Neuroinformatics Neural correlates of phrase rhythm: An EEG Latorre J., Moncho-Bogani J., Fernández- study of bipartite vs. Rondo sonata form Caballero A. Ortiz-Rosario, A; Adeli, H; Buford, JA 2017 Behavioral Brain Research MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates Deuel T.A., Pampin J., Sundstrom J., Darvas F. 2017 Frontiers in Human Neuroscience The encephalophone: A novel musical biofeedback device using conscious control of electroencephalogram (EEG) Fedotchev A.I., Parin S.B., Polevaya S.A., Velikova 2017 Sovremennye Tehnologii v Medicine Brain–computer interface and neurofeedback S.D. technologies: Current state, problems and clinical prospects (review) Krol L.R., Freytag S.-C., Zander T.O. 2017 ICMI 2017 – Proceedings of the 19th ACM Meyendtris: A hands-free, multimodal tetris International Conference on Multimodal clone using eye tracking and passive BCI for Interaction intuitive neuroadaptive gaming Bajoulvand A., Zargari Marandi R., Daliri M.R., 2017 Applied Mathematics and Computation Analysis of folk music preference of people from Sabzpoushan S.H. different ethnic groups using kernel-based methods on EEG signals Zioga P., Chapman P., Ma M., Pollick F. 2017 Digital Creativity Enheduanna–A Manifesto of Falling: first demonstration of a live brain-computer cinema performance with multi-brain BCI interaction for one performer and two audience members Arora B., Choudhury T., Kumar P., Mukherjee 2017 Proceedings on 2016 2nd International An intelligent way to play music by brain activity Conference on Next Generation Computing using brain computer interface Technologies, NGCT 2016 Gang N., Kaneshiro B., Berger J., Dmochowski J.P. 2017 Proceedings of the 18th International Society for Decoding neurally relevant musical features Music Information Retrieval Conference, using canonical correlation analysis ISMIR 2017 Tiwari A., Tiwari R. 2017 Proceeding – IEEE International Conference on Design of a brain computer interface for stress Computing, Communication and Automation, removal using Yoga a smartphone application ICCCA 2017 Straticiuc V., Nicolae I.E. Strungaru R., Vasile T.M., 2017 Proceedings of the 8th International Conference A preliminary study on the effects of music on Bajenaru O.A., Ungureanu G.M. on Electronics, Computers and Artificial human brainwaves Intelligence, ECAI 2016 (Continued) 242 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES Table A1. (Continued). Authors Year Source Title Uma M., Sheela T. 2017 Intelligent Automation and Soft Computing Analysis of Collaborative Brain Computer Interface (BCI) based Personalized GUI for Differently Abled Chen M.L., Yao L., Jiang N. 2017 Lecture Notes in Computer Science (including Music imagery for brain-computer interface subseries Lecture Notes in Artificial control Intelligence and Lecture Notes in Bioinformatics) Nuyujukian P., Albites Sanabria J., Saab J., 2018 PLoS ONE Cortical control of a tablet computer by people Pandarinath C., Jarosiewicz B., Blabe C.H., with paralysis Franco B., Mernoff S.T., Eskandar E.N., Simeral J.D., Hochberg L.R., Shenoy K.V., Henderson J. M. Uma, M; Sheela, T 2018 Intelligent Automation and Soft Computing Analysis of Collaborative Brain Computer Interface (BCI) based Personalized GUI for Differently Abled Iscan, Z; Nikulin, VV 2018 PLoS ONE Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations de Cheveigné A., Wong D.E., Di Liberto G.M., 2018 NeuroImage Decoding the auditory brain with canonical Hjortkjær J., Slaney M., Lalor E. component analysis Fernández-Soto A., Martínez-Rodrigo A., 2018 International Journal of Neural Systems Neural Correlates of Phrase Quadrature Moncho-Bogani J., Latorre J.M., Fernández- Perception in Harmonic Rhythm: An EEG Caballero A. Study Using a Brain-Computer Interface Leite H.M.D.A., Carvalho S.N.D., Costa T.B.D.S., 2018 Computational Intelligence and Neuroscience Analysis of User Interaction with a Brain- Attux R., Hornung H.H., Arantes D.S. Computer Interface Based on Steady-State Visually Evoked Potentials: Case Study of a Game Hsu J.-L., Zhen Y.-L., Lin T.-C., Chiu Y.-S. 2018 Multimedia Systems Affective content analysis of music emotion through EEG Clerico A., Tiwari A., Gupta R., Jayaraman S., Falk 2018 Frontiers in Computational Neuroscience Electroencephalography amplitude modulation T.H. analysis for automated affective tagging of music video clips Ehrlich S., Guan C., Cheng G. 2018 Proceedings of the 2017 International A closed-loop brain-computer music interface Conference on Orange Technologies, ICOT for continuous affective interaction Liu C., Xie S., Xie X., Duan X., Wang W., 2018 2018 6th International Conference on Brain- Design of a video feedback SSVEP-BCI system for Obermayer K. Computer Interface, BCI 2018 car control based on improved MUSIC method Suto J., Oniga S. 2018 Elektronika ir Elektrotechnika Music stimuli recognition in electroencephalogram signal Cartocci G., Maglione A.G., Vecchiato G., Modica 2018 Acta Otorhinolaryngologica Italica Frontal brain asymmetries as effective E., Rossi D., Malerba P., Marsella P., Scorpecci parameters to assess the quality of A., Giannantonio S., Mosca F., Leone C.A., audiovisual stimuli perception in adult and Grassia R., Babiloni F. young cochlear implant users [Asimmetria nell’attività cerebrale frontale come parametro efficace della qualità percettiva degli stimoli audiovisivi in portatori di impianto cocleare giovani e adulti] Cibrian F.L., Mercado J., Escobedo L., Tentori M. 2018 ACM International Conference Proceeding Series A step toward identifying the sound preferences of children with autism Bairavi K., Sundhara Kumar K.B. 2018 ACM International Conference Proceeding Series EEG based emotion recognition system for special children Voznenko T.I., Dyumin A.A., Aksenova E.V., 2018 Procedia Computer Science The experimental study of ‘Unwanted Music’ Gridnev A.A., Delov V.A. noise pollution influence on command recognition by brain-computer interface Nayak L., Dasgupta A., Das R., Ghosh K., De R.K. 2018 Journal of Biosciences Computational neuroscience and neuroinformatics: Recent progress and resources Cheng S., Wei Q. 2018 ISS 2018 – Companion Proceedings of the 2018 Design preferred aesthetic user interface with ACM International Conference on Interactive eye movement and electroencephalography Surfaces and Spaces data Koudelková Z., Strmiska M. 2018 MATEC Web of Conferences Introduction to the identification of brain waves based on their frequency Safavi S.M., Lopour B., Chou P.H. 2018 IEEE Transactions on Biomedical Engineering Reducing the computational complexity of EEG source localization with cortical patch decomposition and optimal electrode selection Mohammadpour M., Alyannezhadi M.M., 2018 2017 IEEE 4th International Conference on Music Emotion Recognition based on Wigner- Hashemi S.M.R., Amiri Z. Knowledge-Based Engineering and Ville Distribution Feature Extraction Innovation, KBEI 2017 (Continued) BRAIN-COMPUTER INTERFACES 243 Table A1. (Continued). Authors Year Source Title Kalaganis F.P., Adamos D.A., Laskaris N.A. 2018 Neurocomputing Musical NeuroPicks: A consumer-grade BCI for on-demand music streaming services Bankar C., Bhide A., Kulkarni A., Ghube C., 2018 1st International Conference on Data Science Driving Control Using Emotion Analysis Via EEG Bedekar M. and Analytics, PuneCon 2018 – Proceedings Angeline R., Barhanpurkar M., Anand R., Singh D. 2018 Proceedings of the 3rd International Conference Brain Computer Interface: Music stimuli on Communication and Electronics Systems, recognition using Machine Learning and an ICCES 2018 Electroencephalogram Ramchurn R., Chamberlian A., Benford S. 2018 ACM International Conference Proceeding Series Designing musical soundtracks for brain controlled interface (BCI) systems Demirel C., Akkaya U.C., Yalçin M., Ince G. 2018 26th IEEE Signal Processing and Estimation of musical features using EEG signals Communications Applications Conference, [EEG işaretleri kullanarak müziksel özelliklerin SIU 2018 kestirimi] Desai B., Chen B., Sirocchi S., McMullen K.A. 2018 3rd International Conference on Digital Arts, Mindtrack: Using brain-computer interface to Media and Technology, ICDAMT 2018 translate emotions into music Nijholt A., Jacob R.J.K., Andujar M., Yuksel B.F., 2018 Conference on Human Factors in Computing Brain-computer interfaces for artistic expression Leslie G. Systems – Proceedings Castro R., Mejia A., Rojas D., Segovia A., Garcia C. 2018 2017 Congreso Internacional de Innovacion Music experiment to measure Colombian sense y Tendencias en Ingenieria, CONIITI 2017 – of belonging at catholic university of Conference Proceedings Colombia Koudelková Z., Strmiska M., Jašek R. 2018 International Journal of Biology and Biomedical Analysis of brain waves according to their Engineering frequency Levicán C., Belaúnde V., Vega A., Aparicio A., 2018 ICMC 2018 – Proceedings of the 2018 EnoBiO2OSC: Brain-computer interface for Cádiz R.F. International Computer Music Conference musical creation Ramirez R., Giraldo S., Vamvakousis Z. 2018 ICMC 2018 – Proceedings of the 2018 An expressive brain-computer music interface International Computer Music Conference for musical neurofeedback Langroudi G., Jordanous A., Li L. 2018 Proceedings of AISB Annual Convention 2018 Music emotion capture: Sonifying emotions in EEG data Sorbello R., Tramonte S., Calí C., Giardina M., 2018 Biologically Inspired Cognitive Architectures Embodied responses to musical experience Nishio S., Ishiguro H., Chella A. detected by human bio-feedback brain features in a Geminoid augmented architecture Soroush, MZ; Maghooli, K; Setarehdan, SK; 2018 Biomedical Engineering-Applications basis A novel method of EEG-based emotion Nasrabadi, AM Communications recognition using nonlinear features variability and Dempster-Shafer theory Rezazadeh Sereshkeh A., Yousefi R., Wong A.T., 2019 Journal of Neural Engineering Online classification of imagined speech using Chau T. functional near-infrared spectroscopy signals Alfano, V 2019 International Journal of Humanities and Arts Brain-Computer Interfaces and Art: Toward Computing-a Journal of Digital Humanities a Theoretical Framework Hong K.-S., Yaqub M.A. 2019 Journal of Innovative Optical Health Sciences Application of functional near-infrared spectroscopy in the healthcare industry: A review Morone G., Spitoni G.F., De Bartolo D., Ghanbari 2019 Expert Review of Medical Devices Rehabilitative devices for a top-down approach Ghooshchy S., Di Iulio F., Paolucci S., Zoccolotti P., Iosa M. Schemrbi P., Pelc M., Ma J. 2019 CHIRA 2019 – Proceedings of the 3rd The effect that an auditory distraction with International Conference on Computer- differing levels of intensity have on a visual Human Interaction Research and Applications P300 speller while utilizing low fidelity equipment: Alongside the development of a taxonomy Siddharth, S; Jung, TP; Sejnowski, TJ 2019 Scientific Reports Impact of Affective Multimedia Content on the Electroencephalogram and Facial Expressions Sasaki M., Iversen J., Callan D.E. 2019 Frontiers in Human Neuroscience Music Improvisation Is Characterized by Increase EEG Spectral Power in Prefrontal and Perceptual Motor Cortical Sources and Can be Reliably Classified From Non-improvisatory Performance Lee C.-S., Tsai Y.-L., Wang M.-H., Sekino H., 2019 Proceedings – 2019 International Conference on FML-based Machine Learning Tool for Human Huang T.-X., Hsieh W.-F., Sato-Shimokawara Technologies and Applications of Artificial Emotional Agent with BCI on Music E., Yamaguchi T. Intelligence, TAAI 2019 Application Shen Y.-W., Lin Y.-P. 2019 Frontiers in Human Neuroscience Challenge for Affective Brain-Computer Interfaces: Non-stationary Spatio-spectral EEG Oscillations of Emotional Responses Fedotchev, AI; Parin, SB; Gromov, KN; Savchuk, 2019 Zhurnal Vysshei Nervnoi Deyatelnosti Imeni Complex Feedback from the Biopotentials of the LV; Polevaya, SA I P Pavlova Brain and Heart in the Correction of Stress- Induced States Psotta L., Rezeika A., Volosyak I. 2019 Conference Proceedings – IEEE International Investigating the influence of background music Conference on Systems, Man and Cybernetics on the performance of an SSVEP-based BCI Ehrlich S.K., Agres K.R., Guan C., Cheng G. 2019 PLoS ONE A closed-loop, music-based brain-computer interface for emotion mediation (Continued) 244 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES Table A1. (Continued). Authors Year Source Title Xiao, J; Qu, J; Li, YQ 2019 IEEE Access An Electrooculogram-Based Interaction Method and Its Music-on-Demand Application in a Virtual Reality Environment Tseng Y.-L., Liu H.-H., Liou M., Tsai A.C., Chien V. 2019 Frontiers in Human Neuroscience Lingering sound: Event-related phase-amplitude S.C., Shyu S.-T., Yang Z.-S. coupling and phase-locking in fronto- temporo-parietal functional networks during memory retrieval of music melodies Teixeira A.R., Tome A., Roseiro L., Gomes A. 2019 Proceedings – 2018 IEEE International Attention and concentration in normal and deaf Conference on Bioinformatics and gamers Biomedicine, BIBM 2018 Hu P.-C., Chen P.-H., Kuo P.-C. 2019 Proceedings – 2018 IEEE International Educational Model Based on Hands-on Brain- Conference on Systems, Man, and Computer Interface: Implementation of Music Cybernetics, SMC 2018 Composition Using EEG Fedotchev A.I., Zemlyanaya A.A., Savchuk L.V., 2019 Sovremennye Tehnologii v Medicine Neurointerface with double feedback from Polevaya S.A. subject’s EEG for correction of stress-induced states Fedotchev A.I., Dvoryaninova V.V., Velikova S.D., 2019 Sovremennye Tehnologii v Medicine Modern technologies in studying the Zemlyanaya A.A. mechanisms, diagnostics, and treatment of autism spectrum disorders Chen, JX; Jiang, DM; Zhang, YN 2019 Journal of Advanced Computational Intelligence A Common Spatial Pattern and Wavelet Packet and Intelligent Informatics Decomposition Combined Method for EEG- Based Emotion Recognition Galíndez-Floréz I., Coral-Flores A., Moncayo- 2020 Communications in Computer and Information Biopotential Signals Acquisition from the Brain Torres E., Mayorca-Torres D., Guerrero-Chapal Science Through the MindWave Device: Preliminary H. Results de Wet L., Potgieter L. 2020 Advances in Intelligent Systems and Computing The impact of binaural beats on user performance and emotions using a bci for robot control Schembri, P; Pelc, M; Ma, JX 2020 Computers The Effect That Auditory Distractions Have on a Visual P300 Speller While Utilizing Low-Cost Off-the-Shelf Equipment Tsekoura, K; Foka, A 2020 Expert Systems with Applications Classification of EEG signals produced by musical notes as stimuli Phang, CR; Ko, LW 2020 IEEE Access Global Cortical Network Distinguishes Motor Imagination of the Left and Right Foot Paszkiel, S; Dobrakowski, P; Lysiak, A 2020 Brain Sciences The Impact of Different Sounds on Stress Level in the Context of EEG, Cardiac Measures and Subjective Stress Level: A Pilot Study Lee, M; Shin, GH; Lee, SW 2020 IEEE Access Frontal EEG Asymmetry of Emotion for the Same Auditory Stimulus Davanzo, N; Avanzini, F 2020 IEEE Access Hands-Free Accessible Digital Musical Instruments: Conceptual Framework, Challenges, and Perspectives Sheykhivand, S; Mousavi, Z; Rezaii, TY; 2020 IEEE Access Recognizing Emotions Evoked by Music Using Farzamnia, A CNN-LSTM Networks on EEG Signals Lee, CS; Wang, MH; Tsai, YL; Chang, WS; 2020 International Journal of Uncertainty Fuzziness FML-Based Reinforcement Learning Agent with Reformat, M; Acampora, G; Kubota, N and Knowledge-Based Systems Fuzzy Ontology for Human-Robot Cooperative Edutainment Kleih-Dahms, SC; Botrel, L; Kubler, A 2021 Psychophysiology The influence of motivation and emotion on sensorimotor rhythm-based brain-computer interface performance Browarska, N; Kawala-Sterniuk, A; Zygarlicki, J 2021 Bio-Algorithms and Med-Systems Initial study on changes in activity of brain waves during audio stimulation using noninvasive brain-computer interfaces: choosing the appropriate filtering method Huang, HY; Xie, QY; Pan, JH; He, YB; Wen, ZF; Yu, 2021 IEEE Transactions on Affective Computing An EEG-Based Brain Computer Interface for RH; Li, YQ Emotion Recognition and Its Application in Patients with Disorder of Consciousness Kim, MS; Lee, GY; Kim, HG 2021 Journal of the Acoustical Society of Korea Multi-channel EEG classification method according to music tempo stimuli using 3D convolutional bidirectional gated recurrent neural network Yan, W; Liu, XJ; Shan, BA; Zhang, XX; Pu, Y 2021 Frontiers in Psychology Research on the Emotions Based on Brain- Computer Technology: A Bibliometric Analysis and Research Agenda Huang, T; Ding, HM; Tseng, YL 2021 IEEE Access Event-Related Phase-Amplitude Coupling During Working Memory of Musical Chords Di Liberto, GM; Marion, G; Shamma, SA 2021 Frontiers in Neuroscience Accurate Decoding of Imagined and Heard Melodies (Continued) BRAIN-COMPUTER INTERFACES 245 Table A1. (Continued). Authors Year Source Title Lee, GY; Kim, MS; Kim, HG 2021 ETRI Journal Extraction and classification of tempo stimuli from electroencephalography recordings using convolutional recurrent attention model Xu, S; Wang, Z 2021 ARTNODES DIFFUSION: Emotional Visualization Based on Biofeedback Control by EEG Feeling, listening, and touching the real things through human brainwave activity Xie, LP; Lu, CH; Liu, ZE; Yan, LR; Xu, T 2021 Frontiers in Human Neuroscience Study of Auditory Brain Cognition Laws-Based Recognition Method of Automobile Sound Quality Tseng, KC 2021 SENSORS Electrophysiological Correlation Underlying the Effects of Music Preference on the Prefrontal Cortex Using a Brain-Computer Interface Bakas, S; Adamos, DA; Laskaris, N 2021 Journal of Neural Engineering On the estimate of music appraisal from surface EEG: a dynamic-network approach based on cross-sensor PAC measurements Lopez-Hernandez, JL; Gonzalez-Carrasco, I; 2021 Frontiers in Neuroinformatics Framework for the Classification of Emotions in Lopez-Cuadrado, JL; Ruiz-Mezcua, B People With Visual Disabilities Through Brain Signals Koh, DW; Kwon, JK; Lee, SG 2021 SENSORS Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Brain-Computer Music Interface, a bibliometric analysis

Brain-Computer Music Interface, a bibliometric analysis

Abstract

This article depicts a bibliometric analysis done through visualization mechanisms and interpretation of bibliometric metadata on the research field of Brain-Computer Interface and Music or Brain-Computer Music Interface (BCMI). Citation, co-citation, co-authorship, and keywords co-occurrence analysis were carried out in this work in order to identify the intellectual structure, research trends, the organizations involved, and the methodological structure of such research field. The...
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Taylor & Francis
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© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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2326-2621
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2326-263x
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10.1080/2326263X.2022.2109313
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Abstract

BRAIN-COMPUTER INTERFACES 2022, VOL. 9, NO. 4, 226–245 https://doi.org/10.1080/2326263X.2022.2109313 ORIGINAL RESEARCH a b Héctor Fabio Torres-Cardona and Catalina Aguirre-Grisales a b Music Department, Universidad de Caldas, Caldas, Colombia; Engineering Department, Universidad Autónoma de Manizales, Manizales, Colombia ABSTRACT ARTICLE HISTORY Received 14 December 2021 This article depicts a bibliometric analysis done through visualization mechanisms and interpreta- Revised 18 June 2022 tion of bibliometric metadata on the research field of Brain-Computer Interface and Music or Brain- Accepted 30 July 2022 Computer Music Interface (BCMI). Citation, co-citation, co-authorship, and keywords co-occurrence analysis were carried out in this work in order to identify the intellectual structure, research trends, KEYWORDS the organizations involved, and the methodological structure of such research field. The biblio- Brain-Computer interface; metric metadata was visualized through VOSviewer and Scimat software. This study also includes Brain-Computer Music the analysis of 227 papers done through 2005–2021 which include research and review articles, Interface; bibliometric analysis; metadata and proceedings papers. The results of this work demonstrate the growing and legitimizing of the visualization research field, and the impact of the interdisciplinary work required in this area. 1. Introduction unconsciously as a response to external stimulation [7,9,10]. These modes can be found in Brain- Human-machine interaction has led to the development Computer Music Interfaces (BCMI) a branch of BCI of different types of systems that are capable of control- [11] that focuses on research that seeks to transform ling external environments through the processing and brain commands into sounds and musical structures. characterization of physiological signals, in which brain- As a result of the work with sound, the first brain computer interfaces are one of the most prominent in musical and sound examples were reported after the the field. In the 1990s, Wolpaw proposed the term invention of the EEG. In 1934, Adrian and Matthews Brain-Computer Interface (BCI), as a communication were able to correlate their Posterior Dominant Rhythm channel between the brain and external devices [1]. This (PDR) by reproducing their brain signals in system is based on the electroencephalogram (EEG), a loudspeaker. This was done by monitoring their which records brain activity through electrodes located brain signals through the brain PDR [12,13]. In 1965, on the scalp [2,3]. In these systems, brain activity is Alvin Lucier created the first brain music, controlling encoded into physiological and cognitive information, percussion instruments through their PDR rhythms which is used in real-time to capture the cognitive status [11,14]. Following Lucier’s experience, in 1970, David of the user for cognitive assessment, mitigation strate- Rosenboom developed a musical piece using several gies, restoration of motor skills, and robust control in users whose brain signals were processed through elec- the area of Augmented Cognition [4–6]. The acquisition tronic circuits this generated a sonorous and visual of communication and processing systems have also performance for ‘the Automation House’ in New York generated new applications in industrial and consumer [15]. Subsequent BCMI performances were based on the environments, including users with and without physi- sonification of EEG signals, being the performance of cal limitations [2,7,8]. Roseboom and Number in 1997, a benchmark in this BCI research has shown that brain activity can be field of research where the authors controlled the used in three modes; active, reactive and passive mode. sounds through the recognition of EEG signal patterns In the active mode, users can consciously control their (Rosenboom & Number, [16]1997). Years later, brain signals, generating commands for external appli- Eduardo Miranda focused his work on the field of cations. In the reactive mode, brain activity is indirectly musical creation based on EEG signals processing, modified by the user in order to control an application; allowing composers to modulate time and musical and in the passive mode, brain signals are generated dynamics, thus consolidating the Brain-Computer CONTACT Héctor Fabio Torres-Cardona hector.torres_c@ucaldas.edu.co Music Department, Universidad de Caldas, Caldas, Colombia This paper is part of the Doctoral Research Project ”Emotion induction and recognition system based on brain-computer interfaces using sound stimulation”, funded by the Ministry of Science of the Colombian Government, National Doctorate - 757 © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. BRAIN-COMPUTER INTERFACES 227 Table 1. Definition of guiding bibliometric questions based on Music Interface field of research (Brain-Computer the bibliometric techniques used. Music Interface, BCMI) [11], and within our previous Bibliometric works ‘Interpolation’ a model for sound representation techniques Guiding bibliometric analysis questions System [17]. Citation Who are the experts in the BCMI research field? On the subject of BCMI, the purpose of this Co-citation Who are the central, peripheral, or bridging researchers in the BCMI research field? article is to analyze the research area through How has the research structure of the BCMI field a bibliometric study using the Scimat and developed over time? Coauthors Are the BCMI researchers interdisciplinary, or do they VOSviewer platforms that allow the application of belong to the same research field? scientific mapping techniques (Zupic & Carter, [18] What is the social structure of the BCMI research field? Keywords Co- What are the dynamics of the conceptual structure of 2015) so as to identify, classify and summarize the occurrence the BCMI research field? main authors, research trends, main publication What are the topics associated with the BCMI research field? sources, and signal processing techniques. 2. Materials and methods 2.1. Bibliometric research design A bibliometric study was first mentioned in Pritchard’s The research design consists of defining the guiding seminal work, ‘Statistical Bibliography or Bibliometrics’ questions and interpreting the bibliometric analysis in 1969 [19]. It was defined as the application of quan- techniques (Table 1), which are subsequently used on titative methods that examine a scientific communica- the selected documents, as to identify the research fields tion process by measuring and analyzing various aspects and trends, as well as the intellectual network of of published research work. Since then, bibliometric researchers in the BCMI area. studies have gained great importance and became a tool for science policy and research management [20]. What impacts its growth is its relevancy in the 2.2. Compilation of articles evaluation of authorship and publication patterns of The process in which Articles have been compiled is communication in any chosen field of research. The based on a literature bibliographic search under principal aim of is to increase the understanding of a protocol based on keywords, time window, and doc- such growth of literature and research patterns over umentary taxonomy, this process allows information of time and take these forward [21]. Several studies have authors, research areas, type of publications, the evolu- already demonstrated the usefulness of bibliometric tion of the subject, networks of researchers, among studies to evaluate scientific productivity in different others to be accessible. The bibliographic search was fields of research. Hamadicharef [22] used bibliometrics carried out in the SCOPUS platform of the Scimago in order to study the status of BCI research. This study Research Group and Web of Science (WOS) of took into account scientific progress and productivity by Clarivate Analytics, establishing the search parameters analyzing the growth of literature, authorship patterns, described in Table 2. and citation rates [23]. Glanzel used bibliometrics to The detailed search in both databases gave as a result study authorship patterns and the relationship between a total of 309 documents, 220 in SCOPUS and 89 in productivity and citation characteristics of scientific WOS, where 35 articles from non-indexed sources, research between 1980 and 1998 [24]. Many biblio- workshops, master’s dissertations, doctoral theses, and metric studies adopt Lotka’s Law of 1926 on scientific book chapters were excluded. In addition, the results of productivity to analyze scientific productivity [25,26]. It both databases were filtered, eliminating 47 duplicate is an inverse square law , which proposes that only 6% of authors in any given field of research produce more than 10 published research articles [22,27]. Similarly, this study will rely on Lotka’s law to deter- mine the scientific productivity of BCMI literature. Table 2. Search parameters. The bibliometric study of the BCMI area is based on Search compilation parameters Search terms Brain Computer Interface mapping-oriented scientific methods proposed by Zupic Brain Computer Interfaces and Carter in 2015 (Zupic & Carter, 2015). Such analysis BCI of the literature is divided into two steps: the first step Music Time window 2005–2021 corresponds to the design of the research and document Kinds of documents Research papers collection, and the second step corresponds to the ana- Conference papers Review papers lysis, visualization, and interpretation of the results. 228 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES Figure 1. Outline of the literature search process for Scopus database, and classification paper scheme. Table 4. Institutions or organizations with the highest number results, giving a total of 227 splits into 7 review papers, of publications in the BCMI field between 2005 and 2021. 115 research papers, and 105 conference papers. Organizations Number of documents (Figure 1). Nanyang Technological University 9 Information on all selected studies used in this bib- University of California, San Diego 9 liometric review can be found in Table A1. University of Plymouth 8 Ministry of Education China 6 University of Toronto 6 Institute for Neural Computation 6 3. Results Technical University of Berlin 5 Aristotle University of Thessaloniki 5 3.1. Countries, institutions, and sources of Bloorview Kids Rehab 5 Technische Universitat Graz 5 publications The 227 articles selected for the study purpose belong to 56 different countries. Most of the studies were con- Table 5. Publication sources with the highest citations number in the BCMI field between 2005 and 2021. ducted in the United States of America followed by the Number of Number of Impact United Kingdom, China, and Japan. Table 3 shows the Sources of Publication citations documents Factor 10 countries with the highest number of publications in Frontiers in Human Neuroscience 354 7 3209 the time frame. Although the search methodology does Lectures Notes in Computer Science 170 8 1170 Journal of Neural Engineering 166 5 4551 not limit the language of the text, all the collected studies PLoS One 108 5 2776 were published in English. Likewise, these articles were IEEE Transactions on Biomedical 97 2 4780 Engineering published by more than 100 institutions or organiza- IEEE Transactions on Neural Systems 65 2 3478 tions worldwide such as universities, hospitals, labora- and Rehabilitation Engineering tories, and government entities. Table 4 shows the 10 Frontiers in Neuroscience 47 2 3566 Conference on Human Factors in 46 3 - institutions with the highest number of documents pub- Computing Systems – Proceedings lished in the BCMI area between 2005 and 2021. Neurocomputing 44 3 4072 Leonardo 36 2 70 Table 3. Number of documents published by country or These 227 articles were published in different territory. indexed sources, including research articles, conference Countries or territories Number of documents articles, and review articles. Table 5 shows the top 10 United States of America 31 sources of publications ordered according to the num- United Kingdom 19 China 17 ber of citations. Furthermore, this table includes the Japan 15 number of published documents and the impact factor Taiwan 15 Germany 13 these sources had. Table 5 indicates that ‘Frontiers in Canada 12 Human Neuroscience’ is the open-access journal with India 11 Singapore 11 the highest citations index, with 7 publications in the Italy 8 BCMI field and a total of 354 citations. In addition, this BRAIN-COMPUTER INTERFACES 229 table demonstrates that the journal ‘IEEE transactions researcher in this area of research, even though he is not on biomedical engineering’ is the source with the high- the most cited author and has only 70 citations within est academic prestige in the BCMI research field due to the documents analyzed at the search period. This find - its high impact factor, which indicates that this source ing might have come up as a result of the author explor- has had the highest citation rate during the analyzed ing different ways of integrating interpretation, musical time frame. Additionally, this table depicts the evident creation, and art with new technologies, experimenting significance of the conference papers, highlighting the with different kinds of sensors and devices, among sources ‘Lectures Notes in Computer Science’ of the which we find the BCI. On the other hand, when con- German publisher Springer and ‘Conference on ducting a longitudinal analysis of the base documents, it Human Factor in Computing Systems – Proceedings’ was found that Sourina, O., Falk, T., Lin, Y., and Tseng, by ACM. These show that research results can have high K. are currently the most relevant authors in the BCMI visibility through publications at symposiums and con- research field. ferences. Finally, it was observed that the publications of When mapping the co-authorship analysis and mak- the BCMI field in the art area are made mainly in the ing a strategic authorship diagram through the use of journal ‘Leonardo’ of the MIT Press Publishing house. the Scimat mapping tool (Figure 2), it was confirmed The following is the impact factor of the 10 most that the author with the highest development in the relevant journals from 2020 to 2021: Frontiers in research area is Miranda E. The authors who have Human Neuroscience 3169, Sensors 3576, Etri Journal worked on BCMI in an detached way are Edlinger, G., 1347, Frontiers in Neuroinformatics 4081, Frontiers in Ito, S, Fernandez-Soto, A., Naraballobh, J., and Hsu, Neuroscience 4677, Frontiers in Psychology 2990, IEEE J. One of the main emerging authors is Fedotchev, Transactions on Affective Computing 10,506, Journal of A. and the authors with transversal research topics in Ambient Intelligence and Humanized Computing 7104, BCMI are Sourina, O., Falk, T., Lin Y., Adamos, D., Journal of Neural Engineering 5379, and Journal of The Chen, K., and Bai, L. It was noted that the author who Acoustical Society of Korea 1854. According to the is currently working on the motor issues in the area of above, it can be deduced that IEEE Transactions on BCMI is Jin, J. Affective Computing is the most cited in the 2020– To determine the intellectual structure in the field 2021 time period. of BCMI and its evolution over time, a co-authorship and co-citation analysis was made applying the VOSviewer platform. A total of 28 research networks 3.2. Analysis of authors were found, where seven networks, in which only Through citation analysis, 21 researchers in the BCMI field were identified (Table 6). consequently, this analy- sis also showed that Eduardo Miranda was the pioneer Table 6. Researchers with the highest citation rate in the BCMI research field. Author Citations Index H Liu, Yisi 230 16 Sourina, Olga 230 18 Nguyen, Minh Khoa 205 8 Chau, Tom 199 41 Falk, Tiago H. 191 25 Lin, Yuanpin 83 16 Jung, Tzyyping 74 54 Miranda, Eduardo Reck 70 16 Lin, Borshy Shyh 34 17 Tseng, Kevin 34 10 Wang, Qiang 32 6 Williams, Duncan 30 8 Daly, Ian 28 18 Leslie, Grace 27 3 Adamos, Dimitrios A 22 6 Li, Yuanqing 22 26 Folgieri, Raffaella 20 8 Chen, Kun 9 5 Guan, Cuntai 9 44 Liu, Quan 9 18 Figure 2. Strategic diagram of authors in the time frame of Fedotchev, Alexander Ivanovitch 7 7 2005–2021. 230 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES Figure 3. Visualization of research networks and their evolution over time. Co-authorship analysis (Figure generated in VOSviewer). one author participated, were excluded. These To establish the distribution of the authors according research networks have made it possible to identify to the research area, a co-citation analysis was carried collaborative communities. Figure 3 presents the co- out in VOSviewer software (Figure 4), finding four net- authorship analysis mapping developed on the works of authors. The networks of authors around VosViewer platform. In this image, it can be seen Blankertz, B. (green network) and Pfurtscheller, that there is only collaboration between two groups G. (blue network), include researchers who worked in of authors through the Daly I. Researcher. This col- the field of augmented cognition., where sound and laboration is evidenced in Table 7 through 1 and 2 music were used as control elements for external appli- networks and it is also observed that the other cations. The network, whose axis is Miranda E (yellow authorship groups are isolated. network), focuses its work on the integration of the BCI with the creation of music and sound in different areas of knowledge. The Jung T.P. network (red network), reflects the use of sound as a stimulus in applications Table 7. Researcher networks in the research field at BCMI. with BCI based on the field of cognitive neuroscience. Network Researchers 1 Brouse, A.; Daly, I.; Eaton, J.; Hwang, F.; Kirke, A.; Malik, A.; Miranda, E.; Nasuto, S. J.; Weaver, J; Williams, D 3.3. Analysis of research areas and research 2 Ai, Q.; Chen, K.; Cichocki, A.; Daly, I.; Jin, J.; Wang, X.: Zhang, Y. keywords 3 Chau, T; Falk, T. H.; Gupta, R.; Kushki, A.; Power, S. D.; Tiwari, A. 4 Bai, L.; Cheng, G. Guan, C.; Li, Y.; Yu, T. _ To determine the dynamics of the research areas and 5 Edlinger, G.; Groenegress, C.; Guger, C.; Holzner, C; Slater, M. 6 Fujisawa, S.; Fukumi, M.; Ito, S; Mitsukura, Y.; Sato, K.; topics, a publication analysis according to their topics 7 Jacob, R.; Leslie, G.; Makeig, S; Nijholt, A.; Yuksel, B. and a keyword co-occurrence was conducted. 8 Chiu, Y.; Hsu, J.; Lin, T; Zhen, Y. The analysis of research areas, in the time period 9 Liu, Y.; Nguyen, M; Sourina; O.; Wang, G. 10 Chinrungrueng, J.; Naraballobh, J.; Nishihara, A; Thanapatay, D. from 2005 to 2021 (Figure 5), showed that the predo- 11 Jung, T.; Lin, Y.; Yang, Y. minant research areas in the field of BCMI were com- 12 Fernandez-Caballero, A.; Martínez-Rodrigo, A.; Moncho-Bogani, J. puter science, engineering, neuroscience, and medicine, 13 Fedotchev, A.; Polevaya, S.; Velikova, S. with published articles showing percentages of 31,0%, 14 Adamos, D.; Laskaris, N. 15 Goh, S.; Tan, L. 19,3%, 11,8%, and 8% respectively. Areas of arts and 16 Fels, S.; Lyons, M. humanities were left behind with a publication rate of 17 Park, S.; Sim, K. 18 Lin, B; Tseng, K. 3,7%, after confirming the results found in the analysis 19 Pinegger, A; Wriessnegger, S. of published sources. In addition, it was observed that 20 Scherer, R.; Zander, T. a wide variety of areas of knowledge, such as 21 Folgieri, R.; Zichella, M. BRAIN-COMPUTER INTERFACES 231 Figure 4. Map of authors obtained from the co-citation analysis. Figure 5. Analysis of the thematic areas of the BCMI research field in the period 2005–2021. mathematics, biochemistry, genetics and molecular techniques, and communication interfaces, among biology, psychology, decision sciences, physics, and others. The second group (green network) refers to the astronomy, worked in the field of BCMI, reflecting the human being and to the configuration of the experi- interdisciplinarity of this field of research. mentation protocol, where the gender of the users, their When a keyword co-occurrence analysis was carried age ranges, type of stimuli, type of experiment (con- out, a total of 5 general clusters of topics were found, all trolled or uncontrolled), and validation techniques such connected by the term ‘Brain-Computer Interface’ as as questionnaires were identified. The third group iden- shown in Figure 6. The first group of terms (red net- tified (blue network) represents the brain functions work) covers the mechanisms for measuring physiolo- considered in the research, such as sound imagination gical signals according to the sound response including processes, musical learning techniques, task develop- types of devices and physiological signals, feedback ment through BCI interfaces, and sound navigation 232 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES Figure 6. Visualization of keyword analysis in the area of brain-computer Music interfaces in the time period 2005–2021. systems, among others. The fourth group (yellow net- cognitive process studied. Likewise, the main applica- work) identifies types of brain signals such as event- tions referenced by the authors, including modulation related potentials (ERP), visual evoked potentials of compositional patterns, classification of mental states, (SSVEP), and brain rhythms. Finally, the last group and patient rehabilitation, are listed in Table 8. found (violet network), relates the techniques of extrac- tion and classification of patterns based on machine 4. Discussion learning techniques, necessary in research processes based on BCI, which require the application of classifi - Because of the work done by Adrian and Matthews in cation methods based on artificial intelligence, which 1934, several researchers from different disciplines have allow better control of external applications or devices conducted a variety of studies relating human brain through the interface. activity to sound and music in various types of applica- In addition, within the analysis of co-occurrence of tions, using brain-computer interfaces as keywords (Table 8), the acquisition, processing and a communication channel between the brain and the classification techniques used by the authors were external device. Based on this is how this research pro- found, as well as the applications, brain regions and vides the intellectual structure, the trends in research, cognitive processes analyzed by the authors. In terms the main sources of publication, the academic origin, of signal acquisition techniques, it is remarkable how and the structures of authors in the BCMI research field, the NIRS technique has been adapted together with the according to the scientific mapping-oriented methods BCI interfaces. In terms of pattern extraction techni- proposed by Zupic and Carter in 2015 (Zupic & Carter, ques, the use of spectral and temporal techniques is 2015). The search performed with the parameters pre- evidenced, as well as the implementation of sound char- sented in Table 2, yielded a total of 220 documents, acterization techniques for the processes involved in the where after applying the inclusion and exclusion cri- creation and control of external systems. As evidenced teria, 187 documents remained, with which the corre- by the keyword network analysis (Figure 6), several sponding bibliometric analysis was performed. supervised classification techniques were found, ranging The results of the analysis of organizations and pub- from artificial neural networks and linear discriminant lication origins showed that more than 100 worldwide networks to deep learning techniques. Table 8 also pro- organizations, including universities, hospitals, labora- vides evidence of the brain regions involved in the tories, research centers, and others, have published in studies, including motor, auditory and visual processing the BCMI area in the time frame between 2005 and areas, depending on the type of stimulation and the 2021. In addition, it was found that 17% of the works BRAIN-COMPUTER INTERFACES 233 Table 8. Keyword metadata analysis. Systems and Rehabilitation Engineering, continue to Target Technique have a high publication rate, because of their trajectory Signal Acquisition ● Functional Near Infra-Red Spectrometer in the research area. This analysis also showed that the Techniques (fNIRS) journal ‘Leonardo’ was the only indexed art and music Electroencephalography (EEG) Brain Computer Interface (BCI) journal that has publications in the BCMI research field. Pattern extraction and Spectrogram analysis This result allows two hypotheses to be formulated. The characterization Zhao-Atlas marks distributions Hilbert – Huang Spectrum (HHS) first hypothesis suggests that a limited number of musi- EEG rhythm analysis cians and composers have the support of an interdisci- Asymmetric hemisphere response Spectral Power Density plinary research group that allows them to explore new Canonical Correlation Analysis (CCA) compositional techniques based on the control of sound Event Related Potentials (P300 from physiological signals; the second hypothesis sug- component) Event – Related Desynchronization gests that there seems to be a low interest of traditional (ERD) musicians and composers in this type of research. These Cronbach’s alpha coefficients Wavelet – based peak detections hypotheses are supported by the results obtained in the Cross frequency coupling (CFC) analysis of research areas, where it was observed that Spatial and temporal dynamic analysis Independent component analysis only 4% of the publications belong to the research field Principal component analysis of arts and humanities, while about 61% of the papers Machine Learning Conditional Transfer Learning (cTL) Techniques Markov model -based classifier cover the areas of computer science, engineering, and Augmented Transitions Networks medicine. Regularized Linear Discriminant analysis Within the methodology of the analysis presented in (RLDA) classifier Convolutional neural network (CNN) this article, it was proposed to determine who were the Long-short term memory network main experts and what was the intellectual and social (LSTM) Artificial Neural Networks (ANN) structure of authors in the area of BCMI, through cita- Brain Regions Pre-frontal cortex (PFC) tion, co-citation, and co-authorship analysis. The results Anterior Cingulate Cortex (ACC) Motor Cortex (MC) of this analysis showed that the most representative Temporoparietal cortex researchers are E., Sourina, O., Falk, T, Lin Y, Tseng, Visual Cortex (VC) Cognitive Process Motor Imagery K., Jong K. S., Chau T., Nguyen M. K., Liu Y. And Power Stimuli Sound stimuli S. D. The results of the co-authorship analysis showed Music stimuli ● that the research networks with the highest number of Visual stimuli General Application Modulate music composition patterns authors are networks 1, 2 and 3 (Table 7), and it was Classifications of mental states found that networks 1 and 2 are connected to each other Control external devices and systems Music composition by author Daly I., which allows inferring that there are Enhance Music production several research collaborations between these groups. Rehabilitation and disease treatment This co-authorship analysis also revealed that most of the research networks are isolated from each other, either because of their geographic location or the research focus of these groups. were published in North America, followed by the Finally, the analysis of co-occurrence of keywords United Kingdom, China, Japan, and Germany. This showed that most of the publications have a similar demonstrates that although this field of research is of experimental methodological structure, composed by high global interest from a technological, therapeutic, the acquisition of the brain signal, the processing and and commercial perspective, first-world countries con- extraction of signal patterns and the classification of the tinue leading the development of this research field. signal according to the purpose of the study carried out. The analysis of publication sources showed that the Furthermore, this analysis showed that although the journal ‘Lecture Notes of computer science’ is the source search was focused on analysis of BCI and Music or with the highest number of publications, being this BCMI, where EEG signals are processed, it was found a reference for conference and symposium papers, fol- that some authors complemented their studies with lowed by the open-access journal ‘Frontier in Human multimodal acquisition processes by adapting the Neuroscience’, evidencing the impact that this publica- NIRS technique within their signal acquisition pro- tion source has on researchers, due to their high rate of cesses. In addition, it was observed that the authors knowledge dissemination. Nevertheless, it should be used supervised learning techniques, which encompass noted that sources such as IEEE transactions on biome- traditional techniques such as artificial neural networks, dical engineering and IEEE transactions on Neural to deep learning techniques such as convolutional 234 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES neural networks. Moreover, in the keyword co- Science of the Colombian Government, National Doctorate - 757Doctorado Nacional, Gobierno de Colombia (CO); occurrence analysis it was noted that the studies are Doctorado Nacional, Gobierno de Colombia (CO) [757]; divided into two main groups, the first group seeks to evaluate the brain response of users to sound or musical stimuli in medical applications, whereas the second ORCID group looks to control musical or sound systems based Héctor Fabio Torres-Cardona http://orcid.org/0000-0001- on the human physiological response in augmented 9758-4038 cognition applications. 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Authors Year Source Title Miranda E.R., Brouse A. 2005 Leonardo Interfacing the brain directly with musical systems: On developing systems for making music with brain signals Zhan X.D., Kang T., Choi H.R. 2005 Journal of Mechanical Science and Technology An approach for pattern recognition of hand activities based on EEG and fuzzy neural network Miranda E.R., Brouse A., Boskamp B., Mullaney H. 2005 International Computer Music Conference, ICMC Plymouth brain-computer music interface 2005 project: Intelligent assistive technology for music-making Miranda E.R. 2006 International Journal on Disability and Human Brain-Computer music interface for composition Development and performance Huang Y.-C. 2006 CAADRIA 2006 – The Association for Computer- A space make you lively: A brain-computer Aided Architectural Design Research in Asia: interface approach to smart space Rhythm and Harmony in Digital Space Teo E., Huang A., Lian Y., Guan C., Li Y., Zhang H. 2006 Annual International Conference of the IEEE Media communication center using brain- Engineering in Medicine and Biology – computer interface Proceedings Lachaux J.-P., Jerbi K., Bertrand O., Minotti L., 2007 PLoS ONE A Blueprint for Real-Time Functional Mapping Hoffmann D., Schoendorff B., Kahane P. via Human Intracranial Recordings Zhao H.-B., Wang H. 2007 Xitong Fangzhen Xuebao/Journal of System Research of brain-computer interface based on Simulation PSD and ANN Khachab M., Kaakour S., Mokbel C. 2007 2007 4th IEEE International Symposium on Brain imaging and support vector machines for Biomedical Imaging: From Nano to Macro – brain computer interface Proceedings Solis-Escalante T., Gentiletti G.G., Yanez-Suarez 2007 Proceedings of the 3rd International IEEE EMBS Detection of steady-state visual evoked O. Conference on Neural Engineering potentials based on the multisignal classification algorithm Swift B., Sheridan J., Zhen Y., Gardner H.J. 2007 Australasian Computer-Human Interaction Mind-modulated music in the Mind Attention Conference, OZCHI’07 Interface Veekmans K., Ressel L., Mueller J., Vischer M., 2009 Audiology and Neurotology Comparison of music perception in bilateral and Brockmeier S.J. unilateral cochlear implant users and normal- hearing subjects Edlinger G., Holzner C., Guger C., Groenegress C., 2009 2009 4th International IEEE/EMBS Conference on Brain-computer interfaces for goal orientated Slater M. Neural Engineering, NER ‘09 control of a virtual smart home environment Antonietti A. 2009 Studies in Health Technology and Informatics Why is music effective in rehabilitation? Jovanovic A., Klonowski W., Duch W., Perovic A. 2009 Computational Intelligence and Neuroscience Some computational aspects of the brain computer interfaces based on inner music Edlinger G., Krausz G., Groenegress C., Holzner 2009 IFMBE Proceedings Brain-Computer Interfaces for Virtual C., Guger C., Slater M. Environment Control Miranda E.R., Matthias J. 2009 Leonardo Music neurotechnology for sound synthesis: Sound synthesis with spiking neuronal networks Ito S.-I., Mitsukura Y., Sato K., Fujisawa S., 2009 IECON Proceedings (Industrial Electronics A study on relationship between personal Fukumi M. Conference) feature of EEG and human’s characteristic for BCI based on mental state Power S.D., Falk T.H., Chau T. 2010 Journal of Neural Engineering Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near- infrared spectroscopy Hamadicharef B., Xu M., Aditya S. 2010 Proceedings – 2010 International Conference on Brain-Computer Interface (BCI) based musical Cyberworlds, CW 2010 composition Looney D., Park C., Xia Y., Kidmose P., Ungstrup 2010 Proceedings of the International Joint Toward estimating selective auditory attention M., Mandic D.P. Conference on Neural Networks from EEG using a novel time-frequency- synchronization framework Ito S.-I., Mitsukura Y., Sato K., Fujisawa S., 2010 Study on relationship between personality and Proceedings – 2010 IEEE Region 8 International Fukumi M. individual characteristic of EEG for Conference on Computational Technologies personalized BCI in Electrical and Electronics Engineering, SIBIRCON-2010 Rached T.S., De S. Santos D.F., Perkusich A., 2010 2010 International Conference on Information BCI-aware pervasive multimedia for motor Almeida H., De Almeida Holanda M.M. Society, i-Society 2010 disabled people Park S.-M., Park J.-H., Sim K.-B. 2010 SCIS and ISIS 2010 – Joint 5th International A study on brain information processing Conference on Soft Computing and mechanism for music genre distinction Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems Liu Y., Sourina O., Nguyen M.K. 2011 Lecture Notes in Computer Science (including Real-time EEG-based emotion recognition and subseries Lecture Notes in Artificial its applications Intelligence and Lecture Notes in Bioinformatics) (Continued) BRAIN-COMPUTER INTERFACES 237 Table A1. (Continued). Authors Year Source Title Falk T.H., Guirgis M., Power S., Chau T.T. 2011 IEEE Transactions on Neural Systems and Taking NIRS-BCIs outside the lab: Toward Rehabilitation Engineering achieving robustness against environment noise Makeig S., Leslie G., Mullen T., Sarma D., Bigdely- 2011 Lecture Notes in Computer Science (including First demonstration of a musical emotion BCI Shamlo N., Kothe C. subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vlek R.J., Schaefer R.S., Gielen C.C.A.M., Farquhar 2011 Journal of Neural Engineering Sequenced subjective accents for brain- J.D.R., Desain P. computer interfaces Sourina O., Wang Q., Liu Y., Nguyen M.K. 2011 BIOSIGNALS 2011 – Proceedings of the A real-time fractal-based brain state recognition International Conference on Bio-Inspired from EEG and its applications Systems and Signal Processing Sourina O., Liu Y., Wang Q., Nguyen M.K. 2011 Lecture Notes in Computer Science (including EEG-based personalized digital experience subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Park S.-M., Sim K.-B. 2011 Proceedings – 2011 8th International A study on the analysis of auditory cortex active Conference on Fuzzy Systems and Knowledge status by music genre: Drawing on EEG Discovery, FSKD 2011 Chew Y.C., Caspary E. 2011 Conference on Human Factors in Computing MusEEGk: A brain computer musical interface Systems – Proceedings Sourina O., Wang Q., Liu Y., Nguyen M.K. 2011 Communications in Computer and Information Fractal-Based Brain State Recognition from EEG Science in Human Computer Interaction Huang Y.-C. 2011 Advanced Materials Research How human-computer interface redefines original lifestyle in architecture? Zander T.O., Klippel M.D., Scherer R. 2011 ICMI’11 – Proceedings of the 2011 ACM Toward multimodal error responses: A passive International Conference on Multimodal BCI for the detection of auditory errors Interaction Chew Y.C.D., Caspary E. 2011 C and C 2011 – Proceedings of the 8th ACM MusEEGk: Design of a BCMI Conference on Creativity and Cognition Dobriyal M., Yilmazer N., Challoo R. 2011 Conference Proceedings – IEEE International Performance analysis of spectral estimation Conference on Systems, Man and Cybernetics techniques for steady State Visual Evoked Potentials (SSVEPs) based Brain Computer Interfaces (BCIs) Fels S., Lyons M. 2011 SIGGRAPH Asia 2011 Courses, SA’11 Advances in new interfaces for musical expression Hadjidimitriou S.K., Hadjileontiadis L.J. 2012 IEEE Transactions on Biomedical Engineering Toward an EEG-based recognition of music liking using time-frequency analysis Power S.D., Kushki A., Chau T. 2012 BMC Research Notes Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: Toward a three- state NIRS-BCI Sourina O., Liu Y., Nguyen M.K. 2012 Journal on Multimodal User Interfaces Real-time EEG-based emotion recognition for music therapy Liu Y., Sourina O. 2012 Proceedings – IEEE International Conference on EEG-based dominance level recognition for Multimedia and Expo emotion-enabled interaction Moghimi S., Kushki A., Guerguerian A.M., Chau T. 2012 Neuroscience Letters Characterizing emotional response to music in the prefrontal cortex using near infrared spectroscopy Folgieri R., Zichella M. 2012 Computers in Entertainment A BCI-based application in music: Conscious playing of single notes by brainwaves Tseng K.C., Wang Y.-T., Lin B.-S., Hsieh P.H. 2012 Proceedings of the 2012 8th International Brain computer interface-based multimedia Conference on Intelligent Information Hiding controller and Multimedia Signal Processing, IIH-MSP Zhao L., Guo X. 2012 Proceedings – 5th International Conference on EEG control of music player Intelligent Networks and Intelligent Systems, ICINIS 2012 Folgieri R., Zichella M. 2012 Computers in Entertainment A BCI-based application in music: Conscious playing of single notes by brainwaves Kakegawa M., Komiyama R., Masakura Y., Kikuchi 2012 6th International Conference on Soft Computing Analysis of music appreciation by Kansei M. and Intelligent Systems, and 13th evaluation and brain activity International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012 Aquino R.J., Battad J.R., Ngo C.F., Uy G., Trogo R., 2012 Lecture Notes in Computer Science (including Toward providing music for academic and Legaspi R., Suarez M.T. subseries Lecture Notes in Artificial leisurely activities of computer users Intelligence and Lecture Notes in Bioinformatics) Blain-Moraes, S; Chesser, S; Kingsnorth, S; 2013 Augmentative and Alternative Communication Biomusic: A Novel Technology for Revealing the Mckeever, P; Biddiss, E Personhood of People with Profound Multiple Disabilities (Continued) 238 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES Table A1. (Continued). Authors Year Source Title Moon, J; Kim, Y; Lee, H; Bae, C; Yoon, WC 2013 ETRI Journal Extraction of User Preference for Video Stimuli Using EEG-Based User Responses Friedrich E.V.C., Scherer R., Neuper C. 2013 Clinical Neurophysiology Stability of event-related (de-) synchronization during brain-computer interface-relevant mental tasks Tseng K.C., Lin B.-S., Han C.-M., Wang P.-S. 2013 ICOT 2013–1st International Conference on Emotion recognition of EEG underlying favorite Orange Technologies music by support vector machine Leslie G., Ojeda A., Makeig S. 2013 Proceedings – 2013 Humaine Association Toward an affective brain-computer interface Conference on Affective Computing and monitoring musical engagement Intelligent Interaction, ACII 2013 Lyons M., Fels S. 2013 SIGGRAPH Asia 2013 Courses, SA 2013 Creating New Interfaces for Musical Expression Morita Y., Huang H.-H., Kawagoe K. 2013 2013 IEEE/ACIS 12th International Conference Toward Music Information Retrieval driven by on Computer and Information Science, ICIS EEG signals: Architecture and preliminary 2013 – Proceedings experiments Uma M., Sridhar S.S. 2013 2013 International Conference on Human A feasibility study for developing an emotional Computer Interactions, ICHCI 2013 control system through brain computer interface Kasabov N., Hu J., Chen Y., Scott N., Turkova Y. 2013 Lecture Notes in Computer Science (including Spatio-temporal EEG data classification in the subseries Lecture Notes in Artificial NeuCube 3D SNN environment: Methodology Intelligence and Lecture Notes in and examples Bioinformatics) Effelsberg W. 2013 ACM Transactions on Multimedia Computing, A personal look back at twenty years of research Communications and Applications in multimedia content analysis Soriano D., Silva E.L., Slenes G.F., Lima F.O., Uribe 2013 ISSNIP Biosignals and Biorobotics Conference, Music versus motor imagery for BCI systems L.F.S., Coelho G.P., Rohmer E., Venancio T.D., BRC a study using fMRI and EEG: Preliminary Beltramini G.C., Campos B.M., Anjos C.A.S., results Suyama R., Li L.M., Castellano G., Attux R. Milsap G., Fifer M., Crone N., Thakor N. 2013 International IEEE/EMBS Conference on Neural Listening to the music of the brain: Live analysis Engineering, NER of ECoG recordings using digital audio workstation software Dhital A., Banic A.U. 2013 Proceedings – IEEE Virtual Reality Navigation in a virtual environment by dichotic listening: Simultaneous audio cues for user- directed BCI classification Lyons M.J., Tomonaga T. 2013 SIGGRAPH Asia 2013 Posters, SA 2013 Enactive Mandala: Sonigraphical brainwave display Jovanović A., Perović A. 2013 SISY 2013 – IEEE 11th International Symposium Structural features in brain signals and weak on Intelligent Systems and Informatics, brain connectivity Proceedings Tan L.-F., Goh S.-Y. 2013 Proceedings of the IADIS International Mental training on Brain-Computer interface Conferences – Interfaces and Human users Computer Interaction 2013, IHCI 2013 and Game and Entertainment Technologies 2013, GET 2013 Rui X., Li Y., Li D. 2013 2013 ICME International Conference on Complex Looking at beauties – Another possibility to Medical Engineering, CME 2013 brain computer interface? Dhital A., Banic A. 2013 2013 1st Workshop on Virtual and Augmented Navigation path differences for dichotic Assistive Technology, VAAT 2013; Co-located listening BCI in virtual environments with the 2013 Virtual Reality Conference – Proceedings Wang, XW; Nie, D; Lu, BL 2014 18th International Conference on Neural Emotional state classification from EEG data Information Processing (ICONIP), using machine learning approach Neurocomputing Jie X., Cao R., Li L. 2014 Bio-Medical Materials and Engineering Emotion recognition based on the sample entropy of EEG Liu, TM; Hu, XT; Li, XJ; Chen, M; Han, JW; Guo, L 2014 IEEE Transactions on Human-Machine Systems Merging Neuroimaging and Multimedia: Methods, Opportunities, and Challenges Lin Y.-P., Yang Y.-H., Jung T.-P. 2014 Frontiers in Neuroscience Fusion of electroencephalographic dynamics and musical contents for estimating emotional responses in music listening Tan L.-F., Dienes Z., Jansari A., Goh S.-Y. 2014 Consciousness and Cognition Effect of mindfulness meditation on brain- computer interface performance Treder M.S., Purwins H., Miklody D., Sturm I., 2014 Journal of Neural Engineering Decoding auditory attention to instruments in Blankertz B. polyphonic music using single-trial EEG classification Gibson R.M., Chennu S., Owen A.M., Cruse D. 2014 Clinical Neurophysiology Complexity and familiarity enhance single-trial detectability of imagined movements with electroencephalography Charland-Verville V., Lesenfants D., Sela L., 2014 Frontiers in Human Neuroscience Detection of response to command using Noirhomme Q., Ziegler E., Chatelle C., Plotkin voluntary control of breathing in disorders of A., Sobel N., Laureys S. consciousness (Continued) BRAIN-COMPUTER INTERFACES 239 Table A1. (Continued). Authors Year Source Title Folgieri R., Zampolini R. 2014 Computers in Entertainment Bci promises in emotional involvement in music and games Kroupi E., Vesin J.-M., Ebrahimi T. 2014 Brain-Computer Interfaces Implicit affective profiling of subjects based on physiological data coupling Bulaj G. 2014 Frontiers in Neurology Combining non-pharmacological treatments with pharmacotherapies for neurological disorders: A unique interface of the brain, drug-device, and intellectual property Daly I., Williams D., Hwang F., Kirke A., Malik A., 2014 Brain-Computer Interfaces Investigating music tempo as a feedback Roesch E., Weaver J., Miranda E., Nasuto S.J. mechanism for closed-loop BCI control Chen K., Liu Q., Ai Q.S. 2014 Applied Mechanics and Materials Multi-channel SSVEP pattern recognition based on MUSIC Cádiz R.F., De La Cuadra P. 2014 Proceedings – 40th International Computer Kara: A BCI approach to composition Music Conference, ICMC 2014 and 11th Sound and Music Computing Conference, SMC 2014 – Music Technology Meets Philosophy: From Digital Echos to Virtual Ethos Rosenboom D. 2014 Frontiers in Neuroscience Active imaginative listening-a neuromusical critique Christopher K.R., Kapur A., Carnegie D.A., 2014 Proceedings – 40th International Computer A history of emerging paradigms in EEG for Grimshaw G.M. Music Conference, ICMC 2014 and 11th Sound music and Music Computing Conference, SMC 2014 – Music Technology Meets Philosophy: From Digital Echos to Virtual Ethos Vamvakousis Z., Ramirez R. 2014 Proceedings – 40th International Computer P300 harmonies: A Brain-Computer Musical Music Conference, ICMC 2014 and 11th Sound Interface and Music Computing Conference, SMC 2014 – Music Technology Meets Philosophy: From Digital Echos to Virtual Ethos Bai L., Yu T., Li Y. 2014 Proceedings of the International Joint Explorer based on brain computer interfac Conference on Neural Networks Liberati, G; Federici, S; Pasqualotto, E 2015 Neurorehabilitation Extracting neurophysiological signals reflecting users’ emotional and affective responses to BCI use: A systematic literature review Naseer N., Hong K.-S. 2015 Frontiers in Human Neuroscience fNIRS-based brain-computer interfaces: A review Cordes J.S., Mathiak K.A., Dyck M., Alawi E.M., 2015 Frontiers in Behavioral Neuroscience Cognitive and neural strategies during control of Gaber T.J., Zepf F.D., Klasen M., Zvyagintsev the anterior cingulate cortex by fMRI M., Gur R.C., Mathiak K. neurofeedback in patients with schizophrenia Kovacevic N., Ritter P., Tays W., Moreno S., 2015 PLoS ONE ‘My virtual dream’: Collective neurofeedback in McIntosh A.R. an immersive art environment Bai L., Yu T., Li Y 2015 Journal of Neuroscience Methods A brain computer interface-based explorer Fouad M.M., Amin K.M., El-Bendary N., Hassanien 2015 Intelligent Systems Reference Library Brain computer interface: A review A.E. Wadeson A., Nijholt A., Nam C.S. 2015 Brain-Computer Interfaces Artistic brain-computer interfaces: state-of-the- art control mechanisms Tseng K.C., Lin B.-S., Wong A.M.-K., Lin B.-S. 2015 Sensors (Switzerland) Design of a mobile brain computer interface- based smart multimedia controller Mahajan R., Bansal D. 2015 International Journal of Biomedical Engineering Depression diagnosis and management using and Technology EEG-based affective brain mapping in real time Eaton J., Williams D., Miranda E. 2015 Brain-Computer Interfaces The Space Between Us: Evaluating a multi-user affective brain-computer music interface Lin Y.-P., Hsu S.-H., Jung T.-P. 2015 Lecture Notes in Computer Science (including Exploring day-to-day variability in the relations subseries Lecture Notes in Artificial between emotion and EEG signals Intelligence and Lecture Notes in Bioinformatics) Lancioni G.E., Simone I.L., De Caro M.F., Singh N. 2015 NeuroRehabilitation Assisting persons with advanced amyotrophic N., O’Reilly M.F., Sigafoos J., Ferlisi G., Zullo V., lateral sclerosis in their leisure engagement Schirone S., Denitto F., Zonno N. and communication needs with a basic technology-aided program Hsu J.-L., Zhen Y.-L., Lin T.-C., Chiu Y.-S. 2015 Proceedings – 2014 IEEE International Personalized music emotion recognition using Symposium on Multimedia, ISM 2014 electroencephalography (EEG) Naraballobh J., Thanapatay D., Chinrungrueng J., 2015 2015 6th International Conference on EEG-based analysis of auditory stimulus in Nishihara A. Information and Communication Technology a brain-computer interface for Embedded Systems, IC-ICTES 2015 Pinegger A., Wriessnegger S.C., Muller-Putz G.R. 2015 Proceedings of the Annual International Sheet music by mind: Toward a brain-computer Conference of the IEEE Engineering in interface for composing Medicine and Biology Society, EMBS (Continued) 240 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES Table A1. (Continued). Authors Year Source Title Naraballobh J., Thanapatay D., Chinrungrueng J., 2015 ECTI-CON 2015–2015 12th International Effect of auditory stimulus in EEG signal using Nishihara A. Conference on Electrical Engineering/ a Brain-Computer Interface Electronics, Computer, Telecommunications and Information Technology Politis D., Tsaligopoulos M., Kyriafinis G. 2015 Proceedings of 2014 International Conference Dialectic & reconstructive musicality: Stressing on Interactive Mobile Communication the brain-computer interface Technologies and Learning, IMCL 2014 Matossian V., Gehlhaar R. 2015 Annual Review of CyberTherapy and Human instruments: Accessible musical Telemedicine instruments for people with varied physical ability Bansal D., Sarkar A. 2015 11th IEEE India Conference: Emerging Trends HMM based predictive model of brain computer and Innovation in Technology, INDICON 2014 interface Crowley K., McDermott J. 2015 Proceedings of the 12th International Mapping brain signals to music via executable Conference in Sound and Music Computing, graphs SMC 2015 Tavares T.F., Rimoldi G., Pontes V.E., Manzolli J. 2015 Proceedings of the 12th International Cooperative musical creation using kinect, Conference in Sound and Music Computing, WiiMote, Epoc and microphones: A case study SMC 2015 with MinDSounDS De Quay Y., Beira J. 2015 41st International Computer Music Conference, Brain-computer interfaces and their application ICMC 2015: Looking Back, Looking Forward – as an audiovisual instrument Proceedings Hu J., Mu Z., Yin J. 2015 Metallurgical and Mining Industry Framework of music Controller Based on Brain Computer interface Chen H.-M., Chen S.-Y., Jheng T.-J., Chang S.-C. 2015 Lecture Notes in Electrical Engineering Design of a mobile brain-computer interface system with personalized emotional feedback Blankertz, B; Acqualagna, L; Dahne, S; Haufe, S; - 2016 Frontiers in Neuroscience The Berlin Brain-Computer Interface: Progress Kraft, MS; Sturm, I; Uscumlic, M; Wenzel, MA; Beyond Communication and Control Curio, G; Muller, KR Yuksel B.F., Oleson K.B., Harrison L., Peck E.M., 2016 Conference on Human Factors in Computing Learn piano with BACh: An adaptive learning Afergan D., Chang R., Jacob R.J.K. Systems – Proceedings interface that adjusts task difficulty based on brain state Morillo, LMS; Alvarez-Garcia, JA; Gonzalez-Abril, 2016 3rd International Work-Conference on Discrete classification technique applied to TV L; Ramirez, JAO Bioinformatics and Biomedical Engineering advertisements liking recognition system (IWBBIO). Biomedical Engineering Online based on low-cost EEG headsets Gupta R., ur Rehman Laghari K., Falk T.H. 2016 Neurocomputing Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization Daly I., Williams D., Kirke A., Weaver J., Malik A., 2016 Journal of Neural Engineering Affective brain-computer music interfacing Hwang F., Miranda E., Nasuto S.J. Norman S.L., Dennison M., Wolbrecht E., Cramer 2016 IEEE Transactions on Neural Systems and Movement Anticipation and EEG: Implications S.C., Srinivasan R., Reinkensmeyer D.J. Rehabilitation Engineering for BCI-Contingent Robot Therapy Adamos D.A., Dimitriadis S.I., Laskaris N.A. 2016 Information Sciences Toward the bio-personalization of music recommendation systems: A single-sensor EEG biomarker of subjective music preference Zhou S., Allison B.Z., Kübler A., Cichocki A., Wang 2016 Frontiers in Computational Neuroscience Effects of background music on objective and X., Jin J. subjective performance measures in an auditory BCI Huang M., Daly I., Jin J., Zhang Y., Wang X., 2016 Cognitive Neurodynamics An exploration of spatial auditory BCI paradigms Cichocki A. with different sounds: music notes versus beeps Hossan A., Chowdhury A.M.M. 2016 2016 5th International Conference on Real time EEG based automatic brainwave Informatics, Electronics and Vision, ICIEV 2016 regulation by music Kalaganis F., Adamos D.A., Laskaris N. 2016 IFIP Advances in Information and A consumer BCI for automated music evaluation Communication Technology within a popular on-demand music streaming service ‘taking listener’s brainwaves to extremes’ Zhang J., Huang X., Yang L., Nie L. 2016 Neurocomputing Bridge the semantic gap between pop music acoustic feature and emotion: Build an interpretable model Chavan D.R., Kumbhar M.S., Chavan R.R. 2016 2016 International Conference on Computation The human stress recognition of brain, using of Power, Energy, Information and music therapy Communication, ICCPEIC 2016 Chen K., Liu Q., Ai Q., Zhou Z., Xie S.Q., Meng W. 2016 Australasian Physical and Engineering Sciences A MUSIC-based method for SSVEP signal in Medicine processing Zerafa R., Camilleri T., Falzon O., Camilleri K.P. 2016 IFMBE Proceedings A real-time SSVEP-based brain-computer interface music player application Sugiono S., Rudy S., Denny W. 2016 Acta Neuropsychologica Investigating the impact of environment noise and music on the human brain by using a brain-computer interface (BCI) (Continued) BRAIN-COMPUTER INTERFACES 241 Table A1. (Continued). Authors Year Source Title Ali A.H., Al-Musawi R.S.H. 2016 Al-Sadiq International Conference on Investigating the possibility of using a single Multidisciplinary in IT and Communication electrode brain-computer interface device for Techniques Science and Applications, AIC- human machine interaction by means of MITCSA 2016 cluster analysis Melinscak F., Montesano L. 2016 Journal of Neuroscience Methods Beyond p-values in the evaluation of brain- computer interfaces: A Bayesian estimation approach Cincuegrani S.M., Jordà S., Väljamäe A. 2016 ACM Transactions on Computer-Human Physiopucks: Increasing user motivation by Interaction combining tangible and implicit physiological interaction Chen K., Xu F., Liu Q., Liu H., Zhang Y., Ma L., Ai 2016 Journal of Computing and Information Science SSVEP Recognition by Using Higher Harmonics Q. in Engineering Based on Music Eaton J., Miranda E.R. 2016 Lecture Notes in Computer Science (including The hybrid brain computer music interface – subseries Lecture Notes in Artificial Integrating brainwave detection methods for Intelligence and Lecture Notes in extended control in musical performance Bioinformatics) systems Lin Y.-P., Jung T.-P. 2017 Frontiers in Human Neuroscience Improving EEG-based emotion classification using conditional transfer learning Folgieri, R 2017 Biolaw Journal-Rivista di Biodiritto Brain computer interface and transcranical stimulation: frontiers, reliability, safety and threats Heo J., Baek H.J., Hong S., Chang M.H., Lee J.S., 2017 Computers in Biology and Medicine Music and natural sounds in an auditory steady- Park K.S. state response based brain–computer interface to increase user acceptance Zerafa, R; Camilleri, T; Bartolo, K; Camilleri, KP; 2017 Biomedical Physics & Engineering Express Reducing the training time for the SSVEP-based Falzon, O music player application Yanagimoto M., Sugimoto C. 2017 2016 IEEE 9th International Workshop on Recognition of persisting emotional valence Computational Intelligence and Applications, from EEG using convolutional neural IWCIA 2016 – Proceedings networks Pinegger A., Hiebel H., Wriessnegger S.C., 2017 PLoS ONE Composing only by thought: Novel application Müller-Putz G.R. of the P300 brain-computer interface Lin Y.-P., Jao P.-K., Yang Y.-H. 2017 Frontiers in Computational Neuroscience Improving cross-day EEG-based emotion classification using robust principal component analysis Martínez-Rodrigo A., Fernández-Sotos A., Miguel 2017 Frontiers in Neuroinformatics Neural correlates of phrase rhythm: An EEG Latorre J., Moncho-Bogani J., Fernández- study of bipartite vs. Rondo sonata form Caballero A. Ortiz-Rosario, A; Adeli, H; Buford, JA 2017 Behavioral Brain Research MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates Deuel T.A., Pampin J., Sundstrom J., Darvas F. 2017 Frontiers in Human Neuroscience The encephalophone: A novel musical biofeedback device using conscious control of electroencephalogram (EEG) Fedotchev A.I., Parin S.B., Polevaya S.A., Velikova 2017 Sovremennye Tehnologii v Medicine Brain–computer interface and neurofeedback S.D. technologies: Current state, problems and clinical prospects (review) Krol L.R., Freytag S.-C., Zander T.O. 2017 ICMI 2017 – Proceedings of the 19th ACM Meyendtris: A hands-free, multimodal tetris International Conference on Multimodal clone using eye tracking and passive BCI for Interaction intuitive neuroadaptive gaming Bajoulvand A., Zargari Marandi R., Daliri M.R., 2017 Applied Mathematics and Computation Analysis of folk music preference of people from Sabzpoushan S.H. different ethnic groups using kernel-based methods on EEG signals Zioga P., Chapman P., Ma M., Pollick F. 2017 Digital Creativity Enheduanna–A Manifesto of Falling: first demonstration of a live brain-computer cinema performance with multi-brain BCI interaction for one performer and two audience members Arora B., Choudhury T., Kumar P., Mukherjee 2017 Proceedings on 2016 2nd International An intelligent way to play music by brain activity Conference on Next Generation Computing using brain computer interface Technologies, NGCT 2016 Gang N., Kaneshiro B., Berger J., Dmochowski J.P. 2017 Proceedings of the 18th International Society for Decoding neurally relevant musical features Music Information Retrieval Conference, using canonical correlation analysis ISMIR 2017 Tiwari A., Tiwari R. 2017 Proceeding – IEEE International Conference on Design of a brain computer interface for stress Computing, Communication and Automation, removal using Yoga a smartphone application ICCCA 2017 Straticiuc V., Nicolae I.E. Strungaru R., Vasile T.M., 2017 Proceedings of the 8th International Conference A preliminary study on the effects of music on Bajenaru O.A., Ungureanu G.M. on Electronics, Computers and Artificial human brainwaves Intelligence, ECAI 2016 (Continued) 242 H. F. TORRES-CARDONA AND C. AGUIRRE-GRISALES Table A1. (Continued). Authors Year Source Title Uma M., Sheela T. 2017 Intelligent Automation and Soft Computing Analysis of Collaborative Brain Computer Interface (BCI) based Personalized GUI for Differently Abled Chen M.L., Yao L., Jiang N. 2017 Lecture Notes in Computer Science (including Music imagery for brain-computer interface subseries Lecture Notes in Artificial control Intelligence and Lecture Notes in Bioinformatics) Nuyujukian P., Albites Sanabria J., Saab J., 2018 PLoS ONE Cortical control of a tablet computer by people Pandarinath C., Jarosiewicz B., Blabe C.H., with paralysis Franco B., Mernoff S.T., Eskandar E.N., Simeral J.D., Hochberg L.R., Shenoy K.V., Henderson J. M. Uma, M; Sheela, T 2018 Intelligent Automation and Soft Computing Analysis of Collaborative Brain Computer Interface (BCI) based Personalized GUI for Differently Abled Iscan, Z; Nikulin, VV 2018 PLoS ONE Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations de Cheveigné A., Wong D.E., Di Liberto G.M., 2018 NeuroImage Decoding the auditory brain with canonical Hjortkjær J., Slaney M., Lalor E. component analysis Fernández-Soto A., Martínez-Rodrigo A., 2018 International Journal of Neural Systems Neural Correlates of Phrase Quadrature Moncho-Bogani J., Latorre J.M., Fernández- Perception in Harmonic Rhythm: An EEG Caballero A. Study Using a Brain-Computer Interface Leite H.M.D.A., Carvalho S.N.D., Costa T.B.D.S., 2018 Computational Intelligence and Neuroscience Analysis of User Interaction with a Brain- Attux R., Hornung H.H., Arantes D.S. Computer Interface Based on Steady-State Visually Evoked Potentials: Case Study of a Game Hsu J.-L., Zhen Y.-L., Lin T.-C., Chiu Y.-S. 2018 Multimedia Systems Affective content analysis of music emotion through EEG Clerico A., Tiwari A., Gupta R., Jayaraman S., Falk 2018 Frontiers in Computational Neuroscience Electroencephalography amplitude modulation T.H. analysis for automated affective tagging of music video clips Ehrlich S., Guan C., Cheng G. 2018 Proceedings of the 2017 International A closed-loop brain-computer music interface Conference on Orange Technologies, ICOT for continuous affective interaction Liu C., Xie S., Xie X., Duan X., Wang W., 2018 2018 6th International Conference on Brain- Design of a video feedback SSVEP-BCI system for Obermayer K. Computer Interface, BCI 2018 car control based on improved MUSIC method Suto J., Oniga S. 2018 Elektronika ir Elektrotechnika Music stimuli recognition in electroencephalogram signal Cartocci G., Maglione A.G., Vecchiato G., Modica 2018 Acta Otorhinolaryngologica Italica Frontal brain asymmetries as effective E., Rossi D., Malerba P., Marsella P., Scorpecci parameters to assess the quality of A., Giannantonio S., Mosca F., Leone C.A., audiovisual stimuli perception in adult and Grassia R., Babiloni F. young cochlear implant users [Asimmetria nell’attività cerebrale frontale come parametro efficace della qualità percettiva degli stimoli audiovisivi in portatori di impianto cocleare giovani e adulti] Cibrian F.L., Mercado J., Escobedo L., Tentori M. 2018 ACM International Conference Proceeding Series A step toward identifying the sound preferences of children with autism Bairavi K., Sundhara Kumar K.B. 2018 ACM International Conference Proceeding Series EEG based emotion recognition system for special children Voznenko T.I., Dyumin A.A., Aksenova E.V., 2018 Procedia Computer Science The experimental study of ‘Unwanted Music’ Gridnev A.A., Delov V.A. noise pollution influence on command recognition by brain-computer interface Nayak L., Dasgupta A., Das R., Ghosh K., De R.K. 2018 Journal of Biosciences Computational neuroscience and neuroinformatics: Recent progress and resources Cheng S., Wei Q. 2018 ISS 2018 – Companion Proceedings of the 2018 Design preferred aesthetic user interface with ACM International Conference on Interactive eye movement and electroencephalography Surfaces and Spaces data Koudelková Z., Strmiska M. 2018 MATEC Web of Conferences Introduction to the identification of brain waves based on their frequency Safavi S.M., Lopour B., Chou P.H. 2018 IEEE Transactions on Biomedical Engineering Reducing the computational complexity of EEG source localization with cortical patch decomposition and optimal electrode selection Mohammadpour M., Alyannezhadi M.M., 2018 2017 IEEE 4th International Conference on Music Emotion Recognition based on Wigner- Hashemi S.M.R., Amiri Z. Knowledge-Based Engineering and Ville Distribution Feature Extraction Innovation, KBEI 2017 (Continued) BRAIN-COMPUTER INTERFACES 243 Table A1. (Continued). Authors Year Source Title Kalaganis F.P., Adamos D.A., Laskaris N.A. 2018 Neurocomputing Musical NeuroPicks: A consumer-grade BCI for on-demand music streaming services Bankar C., Bhide A., Kulkarni A., Ghube C., 2018 1st International Conference on Data Science Driving Control Using Emotion Analysis Via EEG Bedekar M. and Analytics, PuneCon 2018 – Proceedings Angeline R., Barhanpurkar M., Anand R., Singh D. 2018 Proceedings of the 3rd International Conference Brain Computer Interface: Music stimuli on Communication and Electronics Systems, recognition using Machine Learning and an ICCES 2018 Electroencephalogram Ramchurn R., Chamberlian A., Benford S. 2018 ACM International Conference Proceeding Series Designing musical soundtracks for brain controlled interface (BCI) systems Demirel C., Akkaya U.C., Yalçin M., Ince G. 2018 26th IEEE Signal Processing and Estimation of musical features using EEG signals Communications Applications Conference, [EEG işaretleri kullanarak müziksel özelliklerin SIU 2018 kestirimi] Desai B., Chen B., Sirocchi S., McMullen K.A. 2018 3rd International Conference on Digital Arts, Mindtrack: Using brain-computer interface to Media and Technology, ICDAMT 2018 translate emotions into music Nijholt A., Jacob R.J.K., Andujar M., Yuksel B.F., 2018 Conference on Human Factors in Computing Brain-computer interfaces for artistic expression Leslie G. Systems – Proceedings Castro R., Mejia A., Rojas D., Segovia A., Garcia C. 2018 2017 Congreso Internacional de Innovacion Music experiment to measure Colombian sense y Tendencias en Ingenieria, CONIITI 2017 – of belonging at catholic university of Conference Proceedings Colombia Koudelková Z., Strmiska M., Jašek R. 2018 International Journal of Biology and Biomedical Analysis of brain waves according to their Engineering frequency Levicán C., Belaúnde V., Vega A., Aparicio A., 2018 ICMC 2018 – Proceedings of the 2018 EnoBiO2OSC: Brain-computer interface for Cádiz R.F. International Computer Music Conference musical creation Ramirez R., Giraldo S., Vamvakousis Z. 2018 ICMC 2018 – Proceedings of the 2018 An expressive brain-computer music interface International Computer Music Conference for musical neurofeedback Langroudi G., Jordanous A., Li L. 2018 Proceedings of AISB Annual Convention 2018 Music emotion capture: Sonifying emotions in EEG data Sorbello R., Tramonte S., Calí C., Giardina M., 2018 Biologically Inspired Cognitive Architectures Embodied responses to musical experience Nishio S., Ishiguro H., Chella A. detected by human bio-feedback brain features in a Geminoid augmented architecture Soroush, MZ; Maghooli, K; Setarehdan, SK; 2018 Biomedical Engineering-Applications basis A novel method of EEG-based emotion Nasrabadi, AM Communications recognition using nonlinear features variability and Dempster-Shafer theory Rezazadeh Sereshkeh A., Yousefi R., Wong A.T., 2019 Journal of Neural Engineering Online classification of imagined speech using Chau T. functional near-infrared spectroscopy signals Alfano, V 2019 International Journal of Humanities and Arts Brain-Computer Interfaces and Art: Toward Computing-a Journal of Digital Humanities a Theoretical Framework Hong K.-S., Yaqub M.A. 2019 Journal of Innovative Optical Health Sciences Application of functional near-infrared spectroscopy in the healthcare industry: A review Morone G., Spitoni G.F., De Bartolo D., Ghanbari 2019 Expert Review of Medical Devices Rehabilitative devices for a top-down approach Ghooshchy S., Di Iulio F., Paolucci S., Zoccolotti P., Iosa M. Schemrbi P., Pelc M., Ma J. 2019 CHIRA 2019 – Proceedings of the 3rd The effect that an auditory distraction with International Conference on Computer- differing levels of intensity have on a visual Human Interaction Research and Applications P300 speller while utilizing low fidelity equipment: Alongside the development of a taxonomy Siddharth, S; Jung, TP; Sejnowski, TJ 2019 Scientific Reports Impact of Affective Multimedia Content on the Electroencephalogram and Facial Expressions Sasaki M., Iversen J., Callan D.E. 2019 Frontiers in Human Neuroscience Music Improvisation Is Characterized by Increase EEG Spectral Power in Prefrontal and Perceptual Motor Cortical Sources and Can be Reliably Classified From Non-improvisatory Performance Lee C.-S., Tsai Y.-L., Wang M.-H., Sekino H., 2019 Proceedings – 2019 International Conference on FML-based Machine Learning Tool for Human Huang T.-X., Hsieh W.-F., Sato-Shimokawara Technologies and Applications of Artificial Emotional Agent with BCI on Music E., Yamaguchi T. 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Journal

Brain-Computer InterfacesTaylor & Francis

Published: Oct 2, 2022

Keywords: Brain-Computer interface; Brain-Computer Music Interface; bibliometric analysis; metadata visualization

References