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Motor Imagery Classification based on Multi-Kernel CNN with the amalgamated Cross Entropy Loss

In this paper, we propose a novel motor imagery (MI) classification method using multi-kernel convolutional neural network (CNN) to optimize the subject dependent kernel size. We designed a multi-kernel CNN consisting of parallel sub-CNNs with different kernel sizes and a weight combiner with the soft-max activation to combine the sub-CNN features. In order to prevent over-fitting problem of the proposed structure, amalgamated cross entropy loss which is defined as the sum of the tentative cross entropy losses of each sub-CNN and the overall cross entropy loss is proposed. The performance of the proposed method is evaluated on BCI Competition IV dataset 2a. The results confirm performance improvement of the proposed method in comparison with existing parallel CNN-based MI classification methods.