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DOI: 10.1055/s-0038-1625398
Enhancement of Classification Accuracy of a Time-frequency Approach for an EEG-based Brain-computer Interface
Publication History
Publication Date:
11 January 2018 (online)
Summary
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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