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

• Published in: PeerJ. Computer science Vol. 7; p. e374
• Main Authors: Rashid, Mamunur, Bari, Bifta Sama, Hasan, Md Jahid, Razman, Mohd Azraai Mohd, Musa, Rabiu Muazu, Ab Nasir, Ahmad Fakhri, P.P. Abdul Majeed, Anwar
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 02.03.2021; PeerJ, Inc; PeerJ Inc
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Brain-Computer Interface; Brain-computer interface (BCI); Cable television broadcasting industry; Classification; Common spatial pattern (CSP); Communication; Competition; Data Mining and Machine Learning; Datasets; Deep learning; Discriminant analysis; Electroencephalography; Electroencephalography (EEG); Ensemble learning; Feature extraction; Feature selection; Human-Computer Interaction; Human-computer interface; Image classification; Image enhancement; Machine learning; Medical imaging; Medical research; Motor imagery; Nervous system; Nervous system diseases; Neural networks; Neurological diseases; Random forest; Spatial data; Subspaces; Support vector machines; Wavelet transforms; United States; Brain-computer interface (BCI); Motor imagery; Random forest; Electroencephalography (EEG); Ensemble learning; Common spatial pattern (CSP)
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.374

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.