Enhancement of classification accuracy of a time-frequency approach for an EEG-based brain-computer interface.

Yamawaki, N;Wilke, C;Hue, Louis;Liu, Z.;He, B
(2007) Methods of information in medicine — Vol. 46, n° 2, p. 155-159 (2007)

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Authors
  • Yamawaki, N
    Author
  • Wilke, C
    Author
  • Hue, LouisUCLouvain
    Author
  • Liu, Z.
    Author
  • He, B
    Author
Abstract
OBJECTIVES: The aim of this paper is to develop a new algorithm to enhance the performance of EEG-based brain-computer interface (BCI). METHODS: We improved our time-frequency approach of classification of motor imagery (MI) tasks for BCI applications. The approach consists of Laplacian filtering, band-pass filtering and classification by correlation of time-frequency-spatial patterns. RESULTS AND CONCLUSIONS: Through off-line analysis of data collected during a "cursor control" experiment, we evaluated the capability of our new method to reveal major features of the EEG control for enhancement of MI classification accuracy. The pilot results in a human subject are promising, with an accuracy rate of 96.1%.
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Yamawaki, N., Wilke, C., Hue, L., Liu, Z., & He, B. (2007). Enhancement of classification accuracy of a time-frequency approach for an EEG-based brain-computer interface. Methods of information in medicine, 46(2), 155-159. https://hdl.handle.net/2078.5/55399 (Original work published 2007)