Abstract:
Brain-machine interfaces (BMIs) have significantly advanced neuro-rehabilitation by enhancing motor control. However, accurately decoding continuous grasp force remains a challenge, limiting the effectiveness of BMI applications for fine motor tasks. Current models tend to prioritise algorithmic complexity rather than incorporating neurophysiological insights into force control, which is essential for developing effective neural engineering solutions. To address this, we propose EEGForceMap, an EEG-based methodology that isolates signals from the premotor-parietal region and extracts task-specific components. We construct three distinct time-frequency feature sets, which are validated by comparing them with prior studies, and use them for force prediction with linear, non-linear, and deep learning-based regressors. The performance of these regressors was evaluated on the WAY-EEG-GAL dataset that includes 12 subjects. Our results show that integrating EEGForceMap approach with regressor models yields a 61.7% improvement in subject-specific conditions (R-squared = 0.815) and a 55.7% improvement in subject-independent conditions (R-squared = 0.785) over the state-of-the-art kinematic decoder models. Furthermore, an ablation study confirms that each preprocessing step significantly enhances decoding accuracy. This work contributes to the advancement of responsive BMIs for stroke rehabilitation and assistive robotics by improving EEG-based decoding of dynamic grasp force.