Feasibility of Motor State Differentiation Using a Cost-Effective 8-Channel EEG

Abstract

Current trends in neurotechnology point toward more accessible, cost-effective, and portable solutions, particularly in the context of neurorehabilitation. This study investigates whether motor execution (ME) and motor imagery (MI) can be reliably differentiated using a cost-effective 8- channel EEG. Data were recorded from 12 participants performing hand-clenching tasks and subsequently analyzed, focusing on event-related spectral perturbations (ERSP) within the u and beta frequency bands. Results indicate that while u- desynchronization serves as a stable feature in both conditions, it is significantly more pronounced during physical execution. In the beta band, the mean power decrease across the activation window provided superior discriminative power compared to transient peak values, whereas the Peak-Approach better characterized transient B-rebounds by capturing instantaneous extrema. Overall, these findings suggest that combined spectral analysis supports differentiation between ME and MI, even with reduced electrode configurations. This highlights the potential of low-cost, efficient systems for practical brain-computer interface applications in everyday settings.