Deep RNN learning for EEG based functional brain state inference

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Use of deep learning for sequence dependent task classification has shown considerable improvement in recent past. This paper is on task identification from the associated electroencephalograph (EEG) signal. This paper deals with extraction of EEG sub-band features by using wavelet transform followed through classification by means of recurrent neural network (RNN) and deep learning. The methodology is based on comparison of correlation among various EEG spectral components by using a holdup RNN architecture and winner-takes-all logistic regression approach to identify the associated task. The bottleneck of current EEG processing approaches is signal power getting biased due to noisy trials which deteriorates the analysis in the manifold space. This study uses a similarity or coherence analysis between sub-band spectrum of individual trial which invalidates the effect of noise. To assess the proposed method, a dataset, containing 5 subjects performing four mental tasks, is utilized. The results show that in the multiclass scenario the proposed algorithm performs at par with state-of-art results, whereas in two class scenario, the proposed algorithm outperforms most of the reported results.