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Article

Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller

1
Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
2
Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan
*
Author to whom correspondence should be addressed.
Academic Editor: Jing Jin
Appl. Sci. 2021, 11(23), 11252; https://doi.org/10.3390/app112311252
Received: 3 October 2021 / Revised: 30 October 2021 / Accepted: 4 November 2021 / Published: 26 November 2021
Classification of brain signal features is a crucial process for any brain–computer interface (BCI) device, including speller systems. The positive P300 component of visual event-related potentials (ERPs) used in BCI spellers has individual variations of amplitude and latency that further changse with brain abnormalities such as amyotrophic lateral sclerosis (ALS). This leads to the necessity for the users to train the speller themselves, which is a very time-consuming procedure. To achieve subject-independence in a P300 speller, ensemble classifiers are proposed based on classical machine learning models, such as the support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbors (kNN), and the convolutional neural network (CNN). The proposed voters were trained on healthy subjects’ data using a generic training approach. Different combinations of electroencephalography (EEG) channels were used for the experiments presented, resulting in single-channel, four-channel, and eight-channel classification. ALS patients’ data represented robust results, achieving more than 90% accuracy when using an ensemble of LDA, kNN, and SVM on four active EEG channels data in the occipital area of the brain. The results provided by the proposed ensemble voting models were on average about 5% more accurate than the results provided by the standalone classifiers. The proposed ensemble models could also outperform boosting algorithms in terms of computational complexity or accuracy. The proposed methodology shows the ability to be subject-independent, which means that the system trained on healthy subjects can be efficiently used for ALS patients. Applying this methodology for online speller systems removes the necessity to retrain the P300 speller. View Full-Text
Keywords: brain–computer interface; EEG classification; ensemble learning; P300 speller brain–computer interface; EEG classification; ensemble learning; P300 speller
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MDPI and ACS Style

Mussabayeva, A.; Jamwal, P.K.; Akhtar, M.T. Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller. Appl. Sci. 2021, 11, 11252. https://doi.org/10.3390/app112311252

AMA Style

Mussabayeva A, Jamwal PK, Akhtar MT. Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller. Applied Sciences. 2021; 11(23):11252. https://doi.org/10.3390/app112311252

Chicago/Turabian Style

Mussabayeva, Ayana, Prashant K. Jamwal, and Muhammad T. Akhtar. 2021. "Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller" Applied Sciences 11, no. 23: 11252. https://doi.org/10.3390/app112311252

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