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EEG pattern decoding of rhythmic individual finger imaginary movements of one hand

Content Provider	Springer Nature Link
Author	Stankevich, L. A. Sonkin, K. M. Shemyakina, N. V. Nagorva, Zh. V. Khomenko, J. G. Perets, D. S. Koval, A. V.
Copyright Year	2016
Abstract	The results of four-class classification of the motor imagery EEG patterns corresponding to the right hand finger movements (little finger, thumb, index and middle fingers) of eight healthy subjects are presented in this study. The motor imagery of individual right-hand finger movements was executed by the subjects in a prescribed rhythm and the trials contained no external stimuli. Classification was performed by means of a specially developed two-level committee of classifiers on the basis of support vector machine and artificial neural networks at the first level and by generalizing an artificial neural network at the second level. The area under the EEG signal curve and the curve length calculated in a sliding time window for sites F 3, C 3, and Cz of the International 10?20 System were selected as the key features of signals from the sensorimotor and adjoining frontal cortical areas contralateral to the movements. The average accuracy of four-class singletrial classification for all subjects was 50 ± 7 [SD] (maximum, 58%) for the pair of sites F 3–C 3 and 46 ± 11% [SD] (maximum 62%) for the pair of sites C 3–Cz with a theoretical guessing level 25%.
Starting Page	32
Ending Page	42
Page Count	11
File Format	PDF
ISSN	03621197
Journal	Human Physiology
Volume Number	42
Issue Number	1
e-ISSN	16083164
Language	English
Publisher	Pleiades Publishing
Publisher Date	2016-03-23
Publisher Place	Moscow
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	electroencephalography motor imagery fingers of one hand committee of classifiers artificial neural networks support vector machine Life Sciences Human Physiology Biomedicine general
Content Type	Text
Resource Type	Article
Subject	Physiology Physiology (medical)

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