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N. Yamawaki, C. Wilke, L. Hue, Z. Liu, B. He

Enhancement of Classification Accuracy of a Time-frequency Approach for an EEG-based Brain-computer Interface

Keywords: Time-frequency analysis, EEG, brain-computer interface, motor imagery

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%.

Methods of Information in Medicine, Schattauer

Print ISSN: 0026-1270
Volume: 46, 01/2007
Pages: 155 - 159

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