Multi-class motor imagery EEG classification method with high accuracy and low individual differences based on hybrid neural network
Objective. Most current methods of classifying different patterns for motor imagery EEG signals
require complex pre-processing and feature extraction steps, which consume time and lack
adaptability, ignoring individual differences in EEG signals. It is essential to improve algorithm
performance with the increased classes and diversity of subjects. Approach. This study introduces
deep learning method for end-to-end learning to complete the classification of four-class MI tasks,
aiming to improve the recognition rate and balance the classification accuracy among different
subjects. A new one-dimensional input data representation method is proposed. This representation
method can increase the number of samples and ignore the influence of channel correlation. In
addition, a cascade network of convolutional neural network and gated recurrent unit is designed to
learn time-frequency information from EEG data without extracting features manually, this model can
capture t...