Classifying EEG data from spinal cord injured patients using manifold learning methods for brain-computer interface-based rehabilitation

Abstract Spinal cord injuries (SCI) occur when the spinal cord is damaged due to any trauma. Treatment of this condition typically involves a long and challenging rehabilitation process. Brain-computer interface (BCI) controlled rehabilitation systems show promise for people with SCI, as they offer repetitive and controlled treatment in the home environment without requiring a specialist. There is a growing demand for electroencephalography (EEG)–based BCI rehabilitation systems, particularly for patients with SCI. In this study, EEG signals from ten SCI patients were analyzed. At the same time, they imagined performing rehabilitation movements for five different hand and arm actions (pronation, supination, palmar grasp, lateral grasp, and hand opening). The study tested manifold learning algorithms, feature extraction, and classification methods in EEG analysis to improve usability in real-time applications. Manifold learning algorithms were used to represent complex and high-dimensional data in a lower-dimensional space, allowing for better representation and separation of the temporal and spatial characteristics of brain activity. The spectral embedding algorithm was used in this study, which, to the best of our knowledge, is the first time this algorithm has been applied to the data of SCI patients. Additionally, we conducted a comparative analysis of commonly encountered methods in the literature, including multi-dimensional scaling (MDS), isometric feature mapping (ISOMAP), local linear embedding (LLE), and t-distributed stochastic neighbor embedding (t-SNE). Machine learning algorithms, such as k-nearest neighbor (k-NN), support vector machine (SVM), and Naive Bayes methods, were used to obtain classification results for both multi-class and binary classes. The combination of pronation and hand open was found to yield the best performance for binary classification of the movements. The study determined that the ISOMAP manifold learning method with the k-NN algorithm is the optimal method for processing times of both the train and the test. The method also demonstrated a high accuracy value of 0.967 and a short time of 0.088 units in multiple classifications, which is promising. The study utilized the spectral embedding method for the first time and achieved an accuracy rate of 0.649 in multi-class classification and 0.933 ± 0.049 in binary-class classification.