Fig. 1From: Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural networkThe procedure of our proposed methods. Our proposed method is comprised of four progressive stages of signal processing and machine learning on EEG signals: (1) a filter bank comprising multiple Butterworth band-pass filters to extract frequency features, (2) a CSP algorithm is used to extract spatial features, (3) a sliding window cropping strategy is applied to crop time slices to model the sequential relationships of spatial-frequency features, (4) classification of the spatial-frequency-sequential relationships on time slices by a deep RNN architecture. In the deep RNN architecture, two different memory units, LSTM unit and GRU, are included to compare classification performance and robustnessBack to article page