Classification of EEG signals in individuals with spinal cord injury

An electroencephalogram (EEG) refers to the electrical activity of the brain, recorded through electrodes strategically placed on the scalp. EEG has diverse applications, including serving as a medium for establishing communication channels between individuals and their environment, aiding in understanding brain functions, and supporting both diagnostic and therapeutic interventions. Among the various EEG components, Movement-Related Cortical Potentials (MRCPs),neural signals associated with the planning and execution of voluntary movements play a pivotal role in advancing these applications.This research investigates the classification of EEG signals using two distinct feature extraction techniques: the Hadamard Basis Method and the Variance-Based MRCP Feature Extraction with Detrending (VB-MFED) approach.The Hadamard basis method utilizes orthogonal transformations to decompose EEG signals into unique basis functions, enabling the extraction of critical neural activity with enhanced precision. In contrast, VB-MFED represents a refined methodology specifically designed to improve the extraction of Movement-Related Cortical Potentials (MRCPs). It incorporates optimized preprocessing steps, such as adaptive filtering, baseline correction, and spatial filtering, to isolate and amplify neural signals relevant to movement-related tasks.This study focuses on classifying MRCPs associated with attempted movements, emphasizing distinct movement types such as hand opening, palmar grasp, lateral grasp, pronation, and supination in individuals with spinal cord injuries (SCI). Experimental results indicate that the VB-MFED approach achieves higher classification accuracy compared to traditional methods, highlighting its potential for real-time applications. The findings underscore the viability of utilizing MRCPs for advanced brain-computer interface (BCI) systems and lay the groundwork for innovative neurorehabilitation solutions tailored to individuals with motor impairments.