Enhancement of Classification Accuracy of a Time-frequency Approach for an EEG-based Brain-computer Interface - Yamawaki, N.; Wilke, C.; Hue, L.; Liu, Z.; He, B. (Methods of Information in Medicine, 2007)
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%.
Keywords: Time-frequency analysis; EEG; brain-computer interface; motor imagery