Attempted movement classification of spinal cord injured patients using hybrid CNN‐LSTM networks

Abstract

AbstractIndividuals who are suffering from the most severe of motor disabilities can improve their quality of life by controlling and directing mechanical and electronic devices. As for Spinal Cord Injured (SCI) patients', attempted hand movements can be classified using electroencephalography (EEG). The research aims to develop a hybrid CNN‐LSTM (Convolutional Neural Network—Long Short Term Memory) architecture for multichannel EEG signal classification. It is a challenging task to classify real‐world multichannel EEG data from SCI patients. The proposed research preprocessed the EEG data to improve the signal‐to‐noise ratio and arranged for them to extract additional information from the data. The preprocessing step includes filtering, downsampling, and artifact removal, while the postprocessing step includes time‐frequency representation and spatial information encoding. A hybrid CNN‐LSTM is used for feature extraction and classification. The proposed method has been implemented on a dataset consisting of 5 different classes of attempted hand movements from 10 SCI patients. The average classification accuracy of 92.36% is achieved for 5‐class classification. To check the global validity of the proposed network, the BCI competition IV data is classified by the proposed method and has found 92.70% overall accuracy.