Comparing fingers and gestures for bci control using an optimized classical machine learning decoder

TL;DR

This study demonstrates that optimized classical machine learning models can effectively decode finger and hand gestures from ECoG data, supporting their use in brain-computer interfaces for communication in impaired individuals.

Contribution

It compares the decodability of finger and hand gestures using optimized classical machine learning, showing high accuracy and robustness for BCI applications.

Findings

All finger and hand gestures are decodable with >98% accuracy.

Finger flexion outperforms hand gestures in multi-class decoding.

Hand movements involving index finger flexion are promising for brain-clicks.

Abstract

Severe impairment of the central motor network can result in loss of motor function, clinically recognized as Locked-in Syndrome. Advances in Brain-Computer Interfaces offer a promising avenue for partially restoring compromised communicative abilities by decoding different types of hand movements from the sensorimotor cortex. In this study, we collected ECoG recordings from 8 epilepsy patients and compared the decodability of individual finger flexion and hand gestures with the resting state, as a proxy for a one-dimensional brain-click. The results show that all individual finger flexion and hand gestures are equally decodable across multiple models and subjects (>98.0\%). In particular, hand movements, involving index finger flexion, emerged as promising candidates for brain-clicks. When decoding among multiple hand movements, finger flexion appears to outperform hand gestures (96.2\% and 92.5\% respectively) and exhibit greater robustness against misclassification errors when all hand movements are included. These findings highlight that optimized classical machine learning models with feature engineering are viable decoder designs for communication-assistive systems.