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Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG-based BCI systems
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Santamaria, Lorena and James, Christopher J. (2018) Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG-based BCI systems. Healthcare Technology Letters, 5 (3). pp. 88-93. doi:10.1049/htl.2017.0049 ISSN 2053-3713 .
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Official URL: https://doi.org/10.1049/htl.2017.0049
Abstract
Phase synchronisation between different neural groups is considered an important source of information to understand the underlying mechanisms of brain cognition. This Letter investigated phase-synchronisation patterns from electroencephalogram (EEG) signals recorded from ten healthy participants performing motor imagery (MI) tasks using schematic emotional faces as stimuli. These phase-synchronised states, named synchrostates, are specific for each cognitive task performed by the user. The maximum and minimum number of occurrence states were selected for each subject and task to extract the connectivity network measures based on graph theory to feed a set of classification algorithms. Two MI tasks were successfully classified with the highest accuracy of 85% with corresponding sensitivity and specificity of 85%. In this work, not only the performance of different supervised learning techniques was studied, as well as the optimal subset of features to obtain the best discrimination rates. The robustness of this classification method for MI tasks indicates the possibility of expanding its use for online classification of the brain-computer interface (BCI) systems.
Item Type: | Journal Article | ||||||
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Subjects: | B Philosophy. Psychology. Religion > BF Psychology | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Library of Congress Subject Headings (LCSH): | Cognition -- Research, Electroencephalography, Graph theory, Algorithms | ||||||
Journal or Publication Title: | Healthcare Technology Letters | ||||||
Publisher: | The Institution of Engineering and Technology | ||||||
ISSN: | 2053-3713 | ||||||
Official Date: | 7 March 2018 | ||||||
Dates: |
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Volume: | 5 | ||||||
Number: | 3 | ||||||
Page Range: | pp. 88-93 | ||||||
DOI: | 10.1049/htl.2017.0049 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 11 September 2018 | ||||||
Date of first compliant Open Access: | 11 September 2018 | ||||||
RIOXX Funder/Project Grant: |
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