A novel approach to classify natural grasp actions by estimating muscle activity patterns from EEG signals

TL;DR

This paper introduces a new EEG-based method that estimates muscle activity patterns to classify natural grasp actions, achieving higher accuracy than existing methods and enabling improved brain-computer interface applications.

Contribution

The study proposes a novel approach that estimates muscle activity from EEG signals to enhance classification accuracy of grasp actions, outperforming competitive methods.

Findings

Average classification accuracy of 63.89% for actual movement

Accuracy of 46.96% for motor imagery

Method outperforms existing models by 21.59% and 5.66% respectively

Abstract

Developing electroencephalogram (EEG) based brain-computer interface (BCI) systems is challenging. In this study, we analyzed natural grasp actions from EEG. Ten healthy subjects participated in this experiment. They executed and imagined three sustained grasp actions. We proposed a novel approach which estimates muscle activity patterns from EEG signals to improve the overall classification accuracy. For implementation, we have recorded EEG and electromyogram (EMG) simultaneously. Using the similarity of the estimated pattern from EEG signals compare to the activity pattern from EMG signals showed higher classification accuracy than competitive methods. As a result, we obtained the average classification accuracy of 63.89($\pm$7.54)% for actual movement and 46.96($\pm$15.30)% for motor imagery. These are 21.59% and 5.66% higher than the result of the competitive model, respectively. This result is encouraging, and the proposed method could potentially be used in future applications, such as a BCI-driven robot control for handling various daily use objects.