A novel frequency band selection method for Common Spatial Pattern in Motor Imagery based Brain Computer Interface

Gufei Sun, Takayuki Furuzuki, Gengfeng Wu

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

17 Citations (Scopus)

Abstract

Brain-Computer Interface (BCI) is a system provides an alternative communication and control channel between the human brain and computer. In Motor Imagery-based (MI) BCI system, Common Spatial Pattern (CSP) is frequently used for extracting discriminative patterns from the electroencephalogram (EEG). There are many studies have proven that the performance of CSP has a very important relation with the choice of operational frequency band. As the fact that the CSP features at different frequency bands contain discriminative and complementary information for classification, this paper proposes a new frequency band selection method to find the best frequency band set on which subject-specifics CSP are complementary for MI classification. Compared to the performance offered by the existing method based on frequency band partition, the proposed algorithm can yield error rate reductions of 49.70% for the same BCI competition dataset.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
DOIs
Publication statusPublished - 2010
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona
Duration: 2010 Jul 182010 Jul 23

Other

Other2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
CityBarcelona
Period10/7/1810/7/23

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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