Access the full text.
Sign up today, get DeepDyve free for 14 days.
J. Sorinas, María Grima, J. Ferrández, E. Fernández (2019)
Identifying Suitable Brain Regions and Trial Size Segmentation for Positive/Negative Emotion RecognitionInternational journal of neural systems, 29 2
V. Menon (2011)
Large-scale brain networks and psychopathology: a unifying triple network modelTrends in Cognitive Sciences, 15
Nicholas Rule, J. Freeman, N. Ambady (2011)
Brain, Behavior, and Culture: Insights from Cognition, Perception, and Emotion
A. Schatzberg, C. Nemeroff (2017)
The American Psychiatric Association Publishing Textbook of Psychopharmacology
Fushun Wang, Jiongjiong Yang, F. Pan, R. Ho, Jason Huang (2020)
Editorial: Neurotransmitters and EmotionsFrontiers in Psychology, 11
A. Fornito, A. Zalesky, M. Breakspear (2015)
The connectomics of brain disordersNature Reviews Neuroscience, 16
C. Meng, F. Brandl, M. Tahmasian, Junming Shao, A. Manoliu, M. Scherr, D. Schwerthöffer, J. Bäuml, Hans Förstl, C. Zimmer, A. Wohlschläger, V. Riedl, C. Sorg (2014)
Aberrant topology of striatum's connectivity is associated with the number of episodes in depression.Brain : a journal of neurology, 137 Pt 2
M. Popoli, Zhen Yan, B. McEwen, G. Sanacora (2011)
The stressed synapse: the impact of stress and glucocorticoids on glutamate transmissionNature Reviews Neuroscience, 13
Yuvaraj Muthulakshmi, M. Murugappan, N. Ibrahim, K. Sundaraj, M. Omar, Khairiyah Mohamad, R. Palaniappan, M. Satiyan (2015)
Inter-hemispheric EEG coherence analysis in Parkinson’s disease: Assessing brain activity during emotion processingJournal of Neural Transmission, 122
B Haider, A Duque (2006)
Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibitionJournal of Neuroscience
Qi Liu, Hongguang Liu (2020)
Criminal psychological emotion recognition based on deep learning and EEG signalsNeural Computing and Applications, 33
S. Goyal, P. Hernández, G. Martínez-Cánovasz, F. Moisan, Manuel Muñoz-Herrera, Ángel Sánchez (2017)
Integration and Segregation
A. Zalesky, A. Fornito, E. Bullmore (2012)
On the use of correlation as a measure of network connectivityNeuroImage, 60
Yi Liu, Jingping Zhao, Wenbin Guo (2018)
Emotional Roles of Mono-Aminergic Neurotransmitters in Major Depressive Disorder and Anxiety DisordersFrontiers in Psychology, 9
Stamos Katsigiannis, N. Ramzan (2018)
DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf DevicesIEEE Journal of Biomedical and Health Informatics, 22
R. Beaty, S. Kaufman, M. Benedek, R. Jung, Yoed Kenett, E. Jauk, A. Neubauer, P. Silvia (2015)
Personality and complex brain networks: The role of openness to experience in default network efficiencyHuman Brain Mapping, 37
HG Ruhé, NS Mason, AH Schene (2007)
Mood is indirectly related to serotonin, norepinephrine and dopamine levels in humans: a meta-analysis of monoamine depletion studiesMolecular Psychiatry
M. Schurz, Lara Maliske, Philipp Kanske (2020)
Cross-network interactions in social cognition: A review of findings on task related brain activation and connectivityCortex, 130
C. Yan, Biao Gong, Yuxuan Wei, Yue Gao (2020)
Deep Multi-View Enhancement Hashing for Image RetrievalIEEE Transactions on Pattern Analysis and Machine Intelligence, 43
C. Yan, T. Teng, Yutao Liu, Yongbing Zhang, Haoqian Wang, Xiangyang Ji (2021)
Precise No-Reference Image Quality Evaluation Based on Distortion IdentificationACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 17
C. Yan, Zhisheng Li, Yongbing Zhang, Yutao Liu, Xiangyang Ji, Yongdong Zhang (2020)
Depth Image Denoising Using Nuclear Norm and Learning Graph ModelACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 16
D. Bassett, E. Bullmore (2009)
Human brain networks in health and diseaseCurrent Opinion in Neurology, 22
S. Aydın (2019)
Deep Learning Classification of Neuro-Emotional Phase Domain Complexity Levels Induced by Affective Video Film ClipsIEEE Journal of Biomedical and Health Informatics, 24
Yelena Tonoyan, D. Looney, D. Mandic, M. Hulle (2016)
Discriminating Multiple Emotional States from EEG Using a Data-Adaptive, Multiscale Information-Theoretic ApproachInternational journal of neural systems, 26 2
O. Sporns (2018)
Graph theory methods: applications in brain networksDialogues in Clinical Neuroscience, 20
M. Schölvinck, Karl Friston, G. Rees (2012)
The influence of spontaneous activity on stimulus processing in primary visual cortexNeuroimage, 59
M. Alavash, Philipp Doebler, H. Holling, C. Thiel, C. Gießing (2015)
Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?NeuroImage, 108
A. Zuberer, A. Kucyi, A. Yamashita, Charley Wu, M. Walter, E. Valera, M. Esterman (2021)
Integration and segregation across large-scale intrinsic brain networks as a marker of sustained attention and task-unrelated thoughtNeuroImage, 229
(2015)
Topography. https:// doi
S. Aydın, Ç. Güdücü, F. Kutluk, A. Öniz, M. Özgören (2019)
The impact of musical experience on neural sound encoding performanceNeuroscience Letters, 694
A Schaefer, F Nils (2010)
Assessing the effectiveness of a large database of emotion-eliciting films, A new tool for emotion researchersCognition and Emotion, 24
M. Pereira, L. Oliveira, F. Erthal, M. Joffily, Izabela Mocaiber, E. Volchan, L. Pessoa (2010)
Emotion affects action: Midcingulate cortex as a pivotal node of interaction between negative emotion and motor signalsCognitive, Affective, & Behavioral Neuroscience, 10
Christoffer Hatlestad-Hall, R. Bruña, M. Syvertsen, Aksel Erichsen, V. Andersson, F. Vecchio, F. Miraglia, P. Rossini, H. Renvall, E. Taubøll, F. Maestú, Ira Haraldsen (2020)
Source-level EEG and graph theory reveal widespread functional network alterations in focal epilepsyClinical Neurophysiology, 132
(2012)
Cortical Connectivity: Brain Stimulation for Assessing and Modulating Cortical Connectivity and FunctionCortical Connectivity
M. Rubinov, O. Sporns (2010)
Complex network measures of brain connectivity: Uses and interpretationsNeuroImage, 52
L. Aftanas, N. Lotova, V. Koshkarov, V. Pokrovskaja, S. Popov, V. Makhnev (1997)
Non-linear analysis of emotion EEG: calculation of Kolmogorov entropy and the principal Lyapunov exponentNeuroscience Letters, 226
Crystal Gabert-Quillen, E. Bartolini, Benjamin Abravanel, C. Sanislow (2015)
Ratings for emotion film clipsBehavior Research Methods, 47
Aruna Chakraborty, A. Konar (2009)
Emotional Intelligence: A Cybernetic Approach, 238
Junxiu Liu, Guopei Wu, Yuling Luo, Senhui Qiu, Su Yang, Wei Li, Yifei Bi (2020)
EEG-Based Emotion Classification Using a Deep Neural Network and Sparse AutoencoderFrontiers in Systems Neuroscience, 14
Zhaleh Alipour, S. Mohammadkhani, R. Khosrowabadi (2019)
Alteration of perceived emotion and brain functional connectivity by changing the musical rhythmic patternExperimental Brain Research, 237
M. Chowdary, Tu Nguyen, D. Hemanth (2021)
Deep learning-based facial emotion recognition for human–computer interaction applicationsNeural Computing and Applications, 35
R. Franciotti, N. Falasca, D. Arnaldi, F. Famà, C. Babiloni, M. Onofrj, F. Nobili, L. Bonanni (2018)
Cortical Network Topology in Prodromal and Mild Dementia Due to Alzheimer’s Disease: Graph Theory Applied to Resting State EEGBrain Topography, 32
Michael Okun, I. Lampl (2008)
Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activitiesNature Neuroscience, 11
Maham Saeidi, W. Karwowski, F. Farahani, K. Fiok, R. Taiar, P. Hancock, Awad Al-Juaid (2021)
Neural Decoding of EEG Signals with Machine Learning: A Systematic ReviewBrain Sciences, 11
S. Aydın, Serdar Demirtas, Kahraman Ates, M. Tunga (2016)
Emotion Recognition with Eigen Features of Frequency Band Activities Embedded in Induced Brain Oscillations Mediated by Affective PicturesInternational journal of neural systems, 26 3
Jon Morris (1995)
Observations: SAM: The Self-Assessment Manikin An Efficient Cross-Cultural Measurement Of Emotional Response 1Journal of Advertising Research
B. Haider, A. Duque, A. Hasenstaub, D. McCormick
Behavioral/systems/cognitive Neocortical Network Activity in Vivo Is Generated through a Dynamic Balance of Excitation and Inhibition
F. Miraglia, F. Vecchio, P. Rossini (2018)
Brain electroencephalographic segregation as a biomarker of learningNeural networks : the official journal of the International Neural Network Society, 106
K. Fu, J. Qu, Y. Chai, Yong Dong (2014)
Classification of seizure based on the time-frequency image of EEG signals using HHT and SVMBiomed. Signal Process. Control., 13
A. Roos, J. Fouche, Dan Stein (2016)
Brain network connectivity in women exposed to intimate partner violence: a graph theory analysis studyBrain Imaging and Behavior, 11
A. Schaefer, F. Nils, X. Sanchez, P. Philippot
Please Scroll down for Article Cognition & Emotion Assessing the Effectiveness of a Large Database of Emotion-eliciting Films: a New Tool for Emotion Researchers
M. Stanley, S. Simpson, D. Dagenbach, R. Lyday, J. Burdette, P. Laurienti (2015)
Changes in Brain Network Efficiency and Working Memory Performance in AgingPLoS ONE, 10
M. Heuvel, C. Stam, R. Kahn, H. Pol (2009)
Efficiency of Functional Brain Networks and Intellectual PerformanceThe Journal of Neuroscience, 29
Jiyoung Kim, Won Lee, Seongho Park, K. Park (2020)
Can we predict drug response by functional connectivity in patients with juvenile myoclonic epilepsy?Clinical Neurology and Neurosurgery, 198
R. Debnath, Haruhisa Takahashi (2002)
Learning Capability: Classical RBF Network vs. SVM with Gaussian Kernel
Vikrant Doma, Matin Pirouz (2020)
A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signalsJournal of Big Data, 7
Shihui Han, E. Pöppel (2011)
Culture and neural frames of cognition and communication
H. Ruhé (2008)
Dose-escalation in the picture : pharmacological and imaging studies in depression
Tianjiao Kong, Jie Shao, J. Hu, Xin Yang, Shiyiling Yang, R. Malekian (2021)
EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility GraphSensors (Basel, Switzerland), 21
V. Bajaj, R. Pachori (2013)
Automatic classification of sleep stages based on the time-frequency image of EEG signalsComputer methods and programs in biomedicine, 112 3
Chang-hyun Park, H. Lee, Y. Kweon, Chung‐Tai Lee, Ki-Tae Kim, Young-Joo Kim, Kyoung-Uk Lee (2015)
Emotion-Induced Topological Changes in Functional Brain NetworksBrain Topography, 29
G. Colombetti (2009)
From affect programs to dynamical discrete emotionsPhilosophical Psychology, 22
Ye Ren, F. Cong, T. Ristaniemi, Yuping Wang, Xiaoli Li, Ruihua Zhang (2019)
Transient seizure onset network for localization of epileptogenic zone: effective connectivity and graph theory-based analyses of ECoG data in temporal lobe epilepsyJournal of Neurology, 266
Henry Candra, M. Yuwono, R. Chai, Ardi Handojoseno, I. Elamvazuthi, H. Nguyen, S. Su (2015)
Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
C. Yan, Yiming Hao, Liang Li, Jian Yin, Anan Liu, Zhendong Mao, Zhenyu Chen, Xingyu Gao (2021)
Task-Adaptive Attention for Image CaptioningIEEE Transactions on Circuits and Systems for Video Technology, 32
F. Vecchio, C. Tomino, F. Miraglia, F. Iodice, C. Erra, R. Iorio, E. Judica, F. Alù, M. Fini, P. Rossini (2019)
Cortical connectivity from EEG data in acute stroke: A study via graph theory as a potential biomarker for functional recovery.International journal of psychophysiology : official journal of the International Organization of Psychophysiology
J. Shine, R. Poldrack (2017)
Principles of dynamic network reconfiguration across diverse brain statesNeuroImage, 180
Zhongmin Wang, Rui Zhou, Yan He, Xiaorong Guo (2023)
Functional Integration and Separation of Brain Network Based on Phase Locking Value During Emotion ProcessingIEEE Transactions on Cognitive and Developmental Systems, 15
Ramtin Mehraram, Marcus Kaiser, R. Cromarty, S. Graziadio, J. O'Brien, A. Killen, John-Paul Taylor, L. Peraza (2019)
Weighted network measures reveal differences between dementia types: An EEG studyHuman Brain Mapping, 41
G. Collin, M. Heuvel, L. Abramovic, A. Vreeker, M. Reus, N. Haren, M. Boks, R. Ophoff, R. Kahn (2016)
Brain network analysis reveals affected connectome structure in bipolar I disorderHuman Brain Mapping, 37
E. Ruiz-Padial, A. Ibáñez-Molina (2018)
Fractal dimension of EEG signals and heart dynamics in discrete emotional statesBiological Psychology, 137
S. Aydın, Serdar Demirtas, M. Tunga, Kahraman Ates (2018)
Comparison of hemispheric asymmetry measurements for emotional recordings from controlsNeural Computing and Applications, 30
A. Manelis, Jorge Almeida, R. Stiffler, J. Lockovich, H. Aslam, M. Phillips (2016)
Anticipation-related brain connectivity in bipolar and unipolar depression: a graph theory approach.Brain : a journal of neurology, 139 Pt 9
B. Schmidt, S. Hanslmayr (2009)
Resting frontal EEG alpha-asymmetry predicts the evaluation of affective musical stimuliNeuroscience Letters, 460
Aruna Chakraborty, A. Konar (2009)
Brain Imaging and Psycho-pathological Studies on Self-regulation of Emotion
J Wang, X Zuo, Y He (2010)
Graph-based network analysis of resting-state functional MRIFrontiers in Systems Neuroscience
Chen Yan, Lixuan Meng, Liang Li, Jiehua Zhang, Zhan Wang, Jian Yin, Jiyong Zhang, Yaoqi Sun, Bolun Zheng (2022)
Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attentionACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 18
D. Ciraulo (2004)
The American Psychiatric Publishing Textbook of PsychopharmacologyJAMA
G. Tamburro, Selenia Fronso, C. Robazza, M. Bertollo, S. Comani (2020)
Modulation of Brain Functional Connectivity and Efficiency During an Endurance Cycling Task: A Source-Level EEG and Graph Theory ApproachFrontiers in Human Neuroscience, 14
Hailing Wang, Xia Wu, L. Yao (2020)
Identifying Cortical Brain Directed Connectivity Networks From High-Density EEG for Emotion RecognitionIEEE Transactions on Affective Computing, 13
Xuhong Liao, A. Vasilakos, Yong He (2017)
Small-world human brain networks: Perspectives and challengesNeuroscience & Biobehavioral Reviews, 77
S. Ostojic, N. Brunel, V. Hakim (2009)
How Connectivity, Background Activity, and Synaptic Properties Shape the Cross-Correlation between Spike TrainsThe Journal of Neuroscience, 29
Yong Shi, Zhao Lv, Ning Bi, Chao Zhang (2019)
An improved SIFT algorithm for robust emotion recognition under various face poses and illuminationsNeural Computing and Applications, 32
Frontiers in Systems Neuroscience Systems Neuroscience
Cristian Torres-Valencia, Mauricio Álvarez, Álvaro Orozco-Guitiérrez (2017)
SVM-based feature selection methods for emotion recognition from multimodal dataJournal on Multimodal User Interfaces, 11
M. Schölvinck, D. Leopold, M. Brookes, P. Khader (2013)
The contribution of electrophysiology to functional connectivity mappingNeuroImage, 80
J. Posner, J. Russell, B. Peterson (2005)
The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathologyDevelopment and Psychopathology, 17
Yuan-Pin Lin, J. Duann, Jyh-Horng Chen, T. Jung (2010)
Electroencephalographic dynamics of musical emotion perception revealed by independent spectral componentsNeuroReport, 21
A. Greco, G. Valenza, E. Scilingo (2018)
Brain Dynamics During Arousal-Dependent Pleasant/Unpleasant Visual Elicitation: An Electroencephalographic Study on the Circumplex Model of AffectIEEE Transactions on Affective Computing, 12
The present study shows new findings that reveal the high association between emotional arousal and neuro-functional brain connectivity measures. For this purpose, contrasting discrete emotional states (happiness vs sadness, amusement vs disgust, calmness vs excitement, calmness vs anger, fear vs anger) are classified by using Support Vector Machines (SVMs) driven by Graph Theoretical segregation (clustering coefficients, transitivity, modularity) and integration (global efficiency, local efficiency) measures of the brain network. Emotional EEG data mediated by short duration video film clips is downloaded from publicly available database called DREAMER. Pearson Correlation (PC) and Spearman Correlation have been examined to estimate statistical dependencies between relatively shorter (6 sec) and longer (12 sec) non-overlapped EEG segments across the cortex. Then the corresponding brain connectivity encoded as a graph is transformed into binary numbers with respect to two different thresholds (60%max and mean). Statistical differences between contrasting emotions are obtained by using both one-way Anova tests and step-wise logistic regression modelling in accordance with variables (dependency estimation, segment length, threshold, network measure). Combined integration measures provided the highest classification accuracies (CAs) (75.00% 80.65%) when PC is applied to longer segments in accordance with particular threshold as the mean. The segregation measures also provided useful CAs (74.13% 80.00%), while the combination of both measures did not. The results reveal that discrete emotional states are characterized by balanced network measures even if both segregation and integration measures vary depending on arousal scores of audio-visual stimuli due to neurotransmitter release during video watching.
Neuroinformatics – Springer Journals
Published: Oct 1, 2022
Keywords: EEG; Graph Theory; Functional connectivity; Emotion; Brain
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.