Rashid, Mamunur and Bari, Bifta Sama and Hasan, Md Jahid and Mohd Azraai, Mohd Razman and Rabiu Muazu, Musa and Ahmad Fakhri, Ab. Nasir and Anwar, P. P. Abdul Majeed (2021) The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN. PeerJ Computer Science. pp. 1-31. ISSN 2376-5992. (Published)
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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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Electroencephalography (EEG); Brain-computer interface (BCI); Motor imagery; Random forest; Ensemble learning; Common spatial pattern (CSP) |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology Faculty of Manufacturing and Mechatronic Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 22 Mar 2021 08:59 |
| Last Modified: | 22 Mar 2021 08:59 |
| URI: | http://umpir.ump.edu.my/id/eprint/30965 |
| Download Statistic: | View Download Statistics |
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