A novel hybrid deep learning scheme for four-class motor imagery classification.
Journal:
Journal of neural engineering
Published Date:
Oct 16, 2019
Abstract
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.