Analysing SCI Patients’ EEG Signal Using Manifold Learning Methods For Triple Command BCI Design

This study analyzed EEG signals from patients with spinal cord injuries by examining five different hand-wrist movements. The signal processing steps designed for an automatic and robust EEG-based BCI system were applied sequentially. Initially, 37 different features in the time, frequency, and time-frequency domains were extracted from the preprocessed signal. After trying widely used Manifold Learning (ML) methods in the literature, including t-Distributed Stochastic Neighbor Embedding (t-SNE), Local Linear Embedding (LLE), Multi-Dimensional Scaling (MDS), and ISOmetric Mapping (ISOMAP), we attempted the Spectral Embedding method, which has not yet been utilized in EEG signal analysis. The signals were then classified using three different machine-learning algorithms. The study compared classification performance using the accuracy metric. A multi-class classification method was employed specifically the triple classification method. The most successful performance was achieved by using the ISOMAP machine learning method and kNN classifier for the Pronation-Palmar Grasp-Hand Open combination, with an accuracy of 0.993 ± 0.016. Other methods used were t-SNE, MDS, LLE, and Spectral Embedding, respectively. Regarding classifiers, the kNN, SVM, and Naive Bayes algorithms were found to be successful in that order. Based on these results, we propose a suitable methodology for designing a robust BCI system.