Single trial EEG classification of lower-limb movements using improved regularized common spatial pattern

Yudu Li, Yu Sun, Fumihiko Taya, Haoyong Yu, Nitish Thakor, Anastasios Bezerianos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Brain computer interface (BCI) is a direct communication pathway between the human central nervous system and external devices primarily aiming at restoring damaged functions such as sight, hearing and movement. Although great achievements have been made for the development of reliable BCI systems to assist people with upper-limb disabilities, researches on BCI development related to lower-limb are still rudimentary. In the current study, based on the regularized common spatial pattern analysis (R-CSP) method and statistical dependency, we have developed an improved feature selection method for lower-limb movement pattern classification. High-resolution electroencephalogram (EEG) signals were recorded from four healthy male subjects undergoing real lower-limb movements. Compared to the conventional CSP, R-CSP, and PCA methods, the proposed method achieved the best average accuracy of 83.5% for single trial classification of left and right lower-limb movement. Our findings thereby have insightful implications for developing practical BCI systems for lower-limb movement.

Original languageEnglish (US)
Title of host publication2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
PublisherIEEE Computer Society
Pages1056-1059
Number of pages4
ISBN (Electronic)9781467363891
DOIs
StatePublished - Jul 1 2015
Event7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 - Montpellier, France
Duration: Apr 22 2015Apr 24 2015

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2015-July
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
Country/TerritoryFrance
CityMontpellier
Period4/22/154/24/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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