  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.
