Spiking Neural Network Approach for Binary Classification of Hand Movements of Spinal Cord Injured Patients

Accurate decoding of hand movement intension from motor imagery (MI) EEG signal of patients with spinal cord injury (SCI) is crucial for developing effective brain-computer interfaces (BCIs) and neurorehabilitation tools. But accurate decoding of hand movement intention is still a great challenge due to inherent complexity and noise existing in EEG signals, particularly in SCI patients. In this study, we have proposed spiking neural network (SNN) approach with multiband dispersion entropy (DE) feature for classifying MI EEG signal of SCI patients to enhance the classification accuracy. An online dataset has been used which contains MI EEG signals of 10 SCI patients performing hand supination, pronation, open, palmar grasp and lateral grasp. After preprocessing the EEG signal, it was decomposed into delta, theta, alpha and beta bands. DE features were computed from each band using data of five selected channels and feature dimensionality reduction was performed by principal component analysis (PCA). SNN was then applied for binary classifications (supination vs pronation, supination vs open, supination vs palmar grasp, and supination vs lateral grasp). Our proposed approach has achieved an average binary classification accuracy of 89.62% which demonstrates the efficacy of this proposed approach. Besides, the energy efficient property of SNN can offer extended the battery life for BCI devices. Therefore, this study may contribute in the development of advanced BCI systems to improve the quality of life of SCI patients.