Classification of the Attempted Arm and Hand Movements of Patients with Spinal Cord Injury Using Deep Learning Approach

The primary objective of this research is to improve the average classification performance for specific movements in patients with cervical spinal cord injury (SCI). The study utilizes a low-frequency multi-class electroencephalography (EEG) dataset obtained from the Institute of Neural Engineering at Graz University of Technology. The research combines convolutional neural network (CNN) and long-short-term memory (LSTM) architectures to uncover strong neural correlations between temporal and spatial aspects of the EEG signals associated with attempted arm and hand movements. To achieve this, three different methods are used to select relevant features, and the proposed models robustness against variations in the data is validated using 10-fold cross-validation (CV). Furthermore, the study explores the potential for subject-specific adaptation in an online paradigm, extending the proof-of-concept for classifying movement attempts. In summary, this research aims to make valuable contributions to the field of neuro-technology by developing EEG-controlled assistive devices using a generalized brain-computer interface (BCI) and deep learning (DL) framework. The focus is on capturing high-level spatiotemporal features and latent dependencies to enhance the performance and usability of EEG-based assistive technologies.