An Ensemble Learning Based Classification of Individual Finger Movement from EEG

TL;DR

This study presents an ensemble learning approach using EEG data to classify individual finger movements, achieving around 74% accuracy for single finger detection and 60% for finger pair discrimination.

Contribution

It introduces a novel ensemble learning method with ECOC for multi-class finger movement classification from EEG signals.

Findings

Average accuracy of 74% for single finger classification

60% accuracy in discriminating between pairs of fingers

Maximum kappa value of 0.36 indicating moderate agreement

Abstract

Brain computer interface based assistive technology are currently promoted for motor rehabilitation of the neuromuscular ailed individuals. Recent studies indicate a high potential of utilising electroencephalography (EEG) to extract motor related intentions. Limbic movement intentions are already exhaustively studied by the researchers with high accuracy rate. But, capturing movement of fingers from EEG is still in nascent stage. In this study, we have proposed an ensemble learning based approach for EEG in distinguishing between movements of different fingers, namely, thumb, index, and middle. Six healthy subjects participated in this study. Common spatial patterns (CSP) were extracted as features to classify with the extra tree or extremely randomized tree binary classifier. The average classification accuracy of decoding a finger from rest condition was found to be $74\%$, wheres in discriminating of movement of pair of fingers average accuracy was $60\%$. Furthermore, error correcting output coding (ECOC) was added to the binary classifier to use it in multiclass classification. The proposed algorithm achieved a maximum kappa value of 0.36 among the subjects.