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

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Yamawaki, N Wilke, C Hue, Louis [UCL] Liu, Z. He, B

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%.

Document type Article de périodique (Journal article) – Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S.
Publication date 2007
Journal information "Methods of information in medicine" - Vol. 46, no. 2, p. 155-9 (2007)
Peer reviewed yes
issn 0026-1270
Publication status Publié
Affiliation UCL - MD/BICL - Département de biochimie et de biologie cellulaire
MESH Subject Algorithms ; Brain - physiology ; Electrodes, Implanted ; Electroencephalography - instrumentation ; Hand - physiology ; Humans ; Movement - physiology ; Pilot Projects ; Signal Processing, Computer-Assisted ; Time ; User-Computer Interface
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Bibliographic reference Yamawaki, N ; Wilke, C ; Hue, Louis ; Liu, Z. ; He, B. Enhancement of classification accuracy of a time-frequency approach for an EEG-based brain-computer interface.. In: Methods of information in medicine, Vol. 46, no. 2, p. 155-9 (2007)
Permanent URL http://hdl.handle.net/2078.1/25890