A novel hybrid deep learning scheme for four-class motor imagery classification
Objective . Learning the structures and unknown correlations of a motor imagery electroencephalogram
(MI-EEG) signal is important for its classification. It is also a major challenge to obtain good
classification accuracy from the increased number of classes and increased variability from
different people. In this study, a four-class MI task is investigated. Approach . An end-to-end
novel hybrid deep learning scheme is developed to decode the MI task from EEG data. The proposed
algorithm consists of two parts: a . A one-versus-rest filter bank common spatial pattern is adopted
to preprocess and pre-extract the features of the four-class MI signal. b . A hybrid deep network
based on the convolutional neural network and long-term short-term memory network is proposed to
extract and learn the spatial and temporal features of the MI signal simultaneously. Main results .
The main contribution of this paper is to propose a hybrid deep network framework t...