Finger flexion imagery: EEG classification through physiologically-inspired feature extraction and hierarchical voting

Daniel Furman, Roi Reichart, Hillel Pratt

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Accurate electroencephalography (EEG) classification of finger flexion imagery would endow non-invasive brainmachine interfaces (BMIs) with a much richer control repertoire. Traditionally, it has been assumed that non-invasive methods lack the resolution required for success on such a fine discrimination task. In this study, we challenged this assumption. EEG was acquired while subjects imagined performing individual and bimanual finger flexions. A new method of spatiotemporal and spectral feature extraction was applied, and multi-class support vector machine (SVM) classifiers were trained. Predictions and probabilities then served as inputs to a novel voting scheme, which output the system decision. The present approach achieved a mean population (n=15) accuracy of 30.86±1.76%, nearly twice the chance guessing level (16.71±1.68%) for the six-class task evaluated. Finger imagery is thus shown to be classifiable through EEG analysis alone.

Original languageEnglish
Title of host publication4th International Winter Conference on Brain-Computer Interface, BCI 2016
ISBN (Electronic)9781467378413
DOIs
StatePublished - 20 Apr 2016
Event4th International Winter Conference on Brain-Computer Interface, BCI 2016 - Gangwon Province, Korea, Republic of
Duration: 22 Feb 201624 Feb 2016

Publication series

Name4th International Winter Conference on Brain-Computer Interface, BCI 2016

Conference

Conference4th International Winter Conference on Brain-Computer Interface, BCI 2016
Country/TerritoryKorea, Republic of
CityGangwon Province
Period22/02/1624/02/16

Keywords

  • BMI
  • Decision system
  • EEG
  • Motor imagery
  • Multi-class SVM
  • Spatiotemporal features
  • Spectral features

ASJC Scopus subject areas

  • Human-Computer Interaction

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