A probabilistic approach to spatio-spectral filters optimization in Brain-Computer Interface

EEG-based motor imagery classification has been widely studied for Brain-Computer Interfaces (BCIs) due to its asynchronous and continuous elicitation and its great potential to many applications. Many research groups have devoted their efforts to either the frequency band selection or optimal spatial filters learning via the Common Spatial Pattern (CSP) algorithm. However, since the spectral filtering and the spatial filtering are generally operated in order in a motor imagery classification system the optimization of the spatial filters and the spectral filters should be considered simultaneously in a unified framework. In this paper, we propose a novel probabilistic approach for the spatio-spectral filters optimization in an EEG-based BCI with a particle-filter algorithm and mutual information between feature vectors and class labels. There are two main contributions of the proposed method. The one is that it finds the optimal frequency bands that maximally discriminate the feature vectors of two classes in terms of an information theoretic approach. The other is that we construct a spectrally-weighted label decision rule by linearly combining the outputs from multiple SVMs, one for each frequency band, with the weight of the corresponding frequency band. From our experiments with two publicly available dataset, we confirm that the proposed method outperforms the other competing methods.