The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

Mamunur Rashid; Bifta Sama Bari; Md Jahid Hasan; Mohd Azraai Mohd Razman; Rabiu Muazu Musa; Ahmad Fakhri Ab Nasir; Anwar P P Abdul Majeed

doi:10.7717/peerj-cs.374

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

PeerJ Comput Sci. 2021 Mar 2:7:e374. doi: 10.7717/peerj-cs.374. eCollection 2021.

Authors

Mamunur Rashid¹, Bifta Sama Bari¹, Md Jahid Hasan², Mohd Azraai Mohd Razman², Rabiu Muazu Musa³, Ahmad Fakhri Ab Nasir^{2

4}, Anwar P P Abdul Majeed^{2

4}

Affiliations

¹ Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia.
² Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia.
³ Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia.
⁴ Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia.

Abstract

Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier's performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.

Keywords: Brain-computer interface (BCI); Common spatial pattern (CSP); Electroencephalography (EEG); Ensemble learning; Motor imagery; Random forest.

Grants and funding

This work was supported by the Universiti Malaysia Pahang through the research grant RDU180321. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.