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

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

doi:10.7717/peerj-cs.374

Mamunur Rashid, Ahmad Fakhri Ab Nasir + Show 5 more

Open Access

https://doi.org/10.7717/peerj-cs.374

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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.

Highlights

The process of communication and control in human beings is largely dependent upon the peripheral nerves and muscles
In data-1, the lowest accuracy, which is 95.8% has been observed by the linear discriminant analysis (LDA), whereas the highest accuracy, 99.21% has been recorded by the ERS- k-nearest neighbour (k-NN)
The primary consumer of brain-computer interface (BCI) technologies is the people who are severely affected by neuromuscular disorders, and commercialisation is the only way to spread this form of technology

Summary

Introduction

The process of communication and control in human beings is largely dependent upon the peripheral nerves and muscles. When a healthy individual intends to do something, signals from a specific part of the brain area are sent via the peripheral nerves system to the corresponding muscles, which in turn perform the intended task. Many neurological disorders, which include stroke of the brain, injury to the spinal cord, cerebral palsy, muscle dystrophies, multiple sclerosis and amyotrophic lateral sclerosis amongst others, may impair the regular communication pathways of the signals (Bamdad, Zarshenas & Auais, 2015). If such neural disorders affect individuals considerably, the individuals may partly or generally begin to lose their voluntary motor control. The BCI technologies are currently being extended from the known traditionally related medical areas to non-medical applications such as virtual reality and games (Rashid et al, 2020c)

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