EEG-based Motion Intention Recognition via Multi-task RNNs.

Recognition of human intention based on Electroen-cephalography (EEG) signals attracts strong research interest in pattern recognition because of its promising applications that enable non-muscular communications and controls. Over the past few years, most EEG-based recognition works make significant efforts to learn ex-tracted features to explore specific patterns between a segment of EEG signals and the corresponding activi-ties. Unfortunately, vectorization-based feature repre-sentations, either vector-like or matrix-like ones, suffer from massive signal noise and difficulties of exploiting signal correlations between adjacent sensors of EEG sig-nals. Most importantly, EEG signals are represented by one unique frequency and then fed into the subse-quent learning model. Neglecting different frequencies of EEG signals can be detrimental to activity recogni-tion because a particular frequency of EEG signals is more helpful to recognize some activities. Inspired by this idea, we propose to extract EEG signals with different frequencies and introduce a novel Multi-task deep learning model to learn the human intentions. We have conducted extensive experiments on a publicly avail-able EEG benchmark dataset and compared our method with many state-of-the-art algorithms. The experimen-tal results demonstrate that the proposed Multi-task deep recurrent neural network outperforms all the com-pared methods in a multi-class scenario.