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

L. A. Stankevich*, K. M. Sonkin, N. V. Shemyakina, Zh V. Nagornova, J. G. Khomenko, D. S. Perets, A. V. Koval

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 F3, C3, 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 F3–C3 and 46 ± 11% [SD] (maximum 62%) for the pair of sites C3–Cz with a theoretical guessing level 25%.

Original languageEnglish
Pages (from-to)32-42
Number of pages11
JournalHuman Physiology
Volume42
Issue number1
DOIs
StatePublished - 1 Jan 2016
Externally publishedYes

Keywords

  • artificial neural networks
  • committee of classifiers
  • electroencephalography
  • fingers of one hand
  • motor imagery
  • support vector machine

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