Improving Continuous Grasp Force Decoding from EEG with Time-Frequency Regressors and Premotor-Parietal Network Integration

TL;DR

This paper introduces EEGForceMap, a neurophysiologically informed method that significantly improves continuous grasp force decoding from EEG signals, enhancing BMI applications for fine motor control and stroke rehabilitation.

Contribution

It presents a novel EEG-based approach combining region-specific signal extraction and time-frequency features, leading to substantial accuracy improvements over existing models.

Findings

61.7% improvement in subject-specific decoding accuracy

55.7% improvement in subject-independent decoding accuracy

Each preprocessing step significantly boosts decoding performance

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.