The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN
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 techn...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/30965/ http://umpir.ump.edu.my/id/eprint/30965/1/The%20classification%20of%20motor%20imagery%20response-%20an%20accuracy%20enhancement.pdf |
| _version_ | 1848823648243154944 |
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| author | Rashid, Mamunur Bari, Bifta Sama Hasan, Md Jahid Mohd Azraai, Mohd Razman Rabiu Muazu, Musa Ahmad Fakhri, Ab. Nasir Anwar, P. P. Abdul Majeed |
| author_facet | Rashid, Mamunur Bari, Bifta Sama Hasan, Md Jahid Mohd Azraai, Mohd Razman Rabiu Muazu, Musa Ahmad Fakhri, Ab. Nasir Anwar, P. P. Abdul Majeed |
| author_sort | Rashid, Mamunur |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:00:28Z |
| format | Article |
| id | ump-30965 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:00:28Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-309652021-03-22T08:59:05Z http://umpir.ump.edu.my/id/eprint/30965/ The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN Rashid, Mamunur Bari, Bifta Sama Hasan, Md Jahid Mohd Azraai, Mohd Razman Rabiu Muazu, Musa Ahmad Fakhri, Ab. Nasir Anwar, P. P. Abdul Majeed TK Electrical engineering. Electronics Nuclear engineering 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. 2021-03-02 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/30965/1/The%20classification%20of%20motor%20imagery%20response-%20an%20accuracy%20enhancement.pdf 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) https://doi.org/10.7717/peerj-cs.374 https://doi.org/10.7717/peerj-cs.374 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Rashid, Mamunur Bari, Bifta Sama Hasan, Md Jahid Mohd Azraai, Mohd Razman Rabiu Muazu, Musa Ahmad Fakhri, Ab. Nasir Anwar, P. P. Abdul Majeed The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN |
| title | The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN |
| title_full | The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN |
| title_fullStr | The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN |
| title_full_unstemmed | The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN |
| title_short | The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN |
| title_sort | classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-nn |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/30965/ http://umpir.ump.edu.my/id/eprint/30965/ http://umpir.ump.edu.my/id/eprint/30965/ http://umpir.ump.edu.my/id/eprint/30965/1/The%20classification%20of%20motor%20imagery%20response-%20an%20accuracy%20enhancement.pdf |