Robust classification of motor imagery EEG signals using statistical time–domain features
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Date
2013-08-24
Journal Title
Journal ISSN
Volume Title
Publisher
IOP Publishing
Abstract
The tradeoff between computational complexity and speed, in addition to
growing demands for real-time BMI (brain–machine interface) systems, expose
the necessity of applying methods with least possible complexity. Willison
amplitude (WAMP) and slope sign change (SSC) are two promising time–
domain features only if the right threshold value is defined for them. To
overcome the drawback of going through trial and error for the determination
of a suitable threshold value, modified WAMP and modified SSC are proposed
in this paper. Besides, a comprehensive assessment of statistical time–domain
features in which their effectiveness is evaluated with a support vector machine
(SVM) is presented. To ensure the accuracy of the results obtained by the
SVM, the performance of each feature is reassessed with supervised fuzzy
C-means. The general assessment shows that every subject had at least one
of his performances near or greater than 80%. The obtained results prove
that for BMI applications, in which a few errors can be tolerated, these
combinations of feature–classifier are suitable. Moreover, features that could
perform satisfactorily were selected for feature combination. Combinations of
the selected features are evaluated with the SVM, and they could significantly
improve the results, in some cases, up to full accuracy.
Description
Keywords
Electroencephalogram, Brain–machine interface, Feature extraction, Support vector machine, Fuzzy C-means, Mutual information
Citation
Khorshidtalab, A., Salami, M. J. E., & Hamedi, M. (2013). Robust classification of motor imagery EEG signals using statistical time–domain features. Physiological measurement, 34(11), 1563.