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Support vector machine (SVM) classification of cognitive tasks based on electroencephalography (EEG) engagement index

Support vector machine (SVM) classification of cognitive tasks based on electroencephalography... AbstractHuman-system interaction may be improved by using proactive systems that detect, measure, and assess a user’s cognitive state in real time via diagnostic neurophysiological sensors and appropriate classification methods. The electroencephalography (EEG) task engagement index (TEI), a ratio of EEG power bands (beta/(alpha + theta)), may be used to track how cognitively engaged a person is in a task. In the present study, we performed statistical tests of significance on task engagement indices computed from EEG recorded from six healthy participants who performed five separate cognitive tasks. For all participants, we found a statistically significant difference in task engagement indices between the five cognitive tasks. Also, we used task engagement indices as inputs to support vector machines (SVMs) to allow identification and offline classification of cognitive engagement. We designed six separate multiclass SVMs to classify five cognitive tasks for the participants. The average classification accuracy across the six participants was 93.33 ± 8.16%. The results show that differences in cognitive task demand do elicit different degrees of cognitive engagement, which can be measured through the use of the TEI. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Support vector machine (SVM) classification of cognitive tasks based on electroencephalography (EEG) engagement index

Brain-Computer Interfaces , Volume 5 (1): 12 – Jan 2, 2018

Support vector machine (SVM) classification of cognitive tasks based on electroencephalography (EEG) engagement index

Abstract

AbstractHuman-system interaction may be improved by using proactive systems that detect, measure, and assess a user’s cognitive state in real time via diagnostic neurophysiological sensors and appropriate classification methods. The electroencephalography (EEG) task engagement index (TEI), a ratio of EEG power bands (beta/(alpha + theta)), may be used to track how cognitively engaged a person is in a task. In the present study, we performed statistical tests of significance on...
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Publisher
Taylor & Francis
Copyright
© 2017 Informa UK Limited, trading as Taylor & Francis Group
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2017.1338012
Publisher site
See Article on Publisher Site

Abstract

AbstractHuman-system interaction may be improved by using proactive systems that detect, measure, and assess a user’s cognitive state in real time via diagnostic neurophysiological sensors and appropriate classification methods. The electroencephalography (EEG) task engagement index (TEI), a ratio of EEG power bands (beta/(alpha + theta)), may be used to track how cognitively engaged a person is in a task. In the present study, we performed statistical tests of significance on task engagement indices computed from EEG recorded from six healthy participants who performed five separate cognitive tasks. For all participants, we found a statistically significant difference in task engagement indices between the five cognitive tasks. Also, we used task engagement indices as inputs to support vector machines (SVMs) to allow identification and offline classification of cognitive engagement. We designed six separate multiclass SVMs to classify five cognitive tasks for the participants. The average classification accuracy across the six participants was 93.33 ± 8.16%. The results show that differences in cognitive task demand do elicit different degrees of cognitive engagement, which can be measured through the use of the TEI.

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Jan 2, 2018

Keywords: Support vector machine (SVM); electroencephalography (EEG); task engagement index; short-term Fourier transform (STFT); cognitive tasks

References