EEG Channel Relevance Analysis Using Maximum Mean Discrepancy on BCI Systems
Brain-Computer Interfaces bridge the communication between brains and devices. Channel selection as a stage for developing BCI systems allows reducing costs and improve the overall performance. This paper proposes a relevance analysis based on the maximum mean discrepancy as the distance function between a pair of single-channel trials, termed rMMD. The proposed rMMD starts with a trial embedding that highlights temporal dynamics, and ends with a channel ranking according to a designed relevance function. The function relies on the within and between class distances to quantify the discrimination capability of each channel. We evaluate the rMMD on a bi-class motor-imagery (MI) dataset holding 64 channels and more than 40 subjects. In comparison with no channel selection and a heuristic approach, our proposed relevance analysis statistically improves the classification of MI tasks with a reduced set of channels.
KeywordsChannel selection Time-series relevance analysis Brain computer interface
This research was supported by the research project 36706 “BrainScore: Sistema compositivo, gráfico y sonoro creado a partir del comportamiento frecuencial de las señales cerebrales”, funded by Universidad de Caldas and Universidad Nacional de Colombia.
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