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Comparison of multi-class motor imagery classification methods for EEG signals

    Ms. Nikita Sandeep Kumar Prabhakar Agarwal Manisha Bharti

International Research Journal on Advanced Science Hub, 2022, Volume 4, Issue 12, Pages 306-311
10.47392/irjash.2022.073

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Abstract

This paper presents a comparative study of EEG-based multiclass motor imagery classifiers based on Kullback-Leiber regularised Riemann Mean and support vector machine, hybrid one versus one classifier, linear discriminant analysis, and convolutional neural network. The paper is felt to be of inter- est to those researchers working in the motor imagery classification of EEG signals. The work presented in this paper helps to understand the basics of different multi-class motor imagery classifiers, their accuracy, and the number of channels involved.
Keywords:
    BrainComputer Interface Classification EEG Motorimagery
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(2022). Comparison of multi-class motor imagery classification methods for EEG signals. International Research Journal on Advanced Science Hub, 4(12), 306-311. doi: 10.47392/irjash.2022.073
Ms. Nikita; Sandeep Kumar; Prabhakar Agarwal; Manisha Bharti. "Comparison of multi-class motor imagery classification methods for EEG signals". International Research Journal on Advanced Science Hub, 4, 12, 2022, 306-311. doi: 10.47392/irjash.2022.073
(2022). 'Comparison of multi-class motor imagery classification methods for EEG signals', International Research Journal on Advanced Science Hub, 4(12), pp. 306-311. doi: 10.47392/irjash.2022.073
Comparison of multi-class motor imagery classification methods for EEG signals. International Research Journal on Advanced Science Hub, 2022; 4(12): 306-311. doi: 10.47392/irjash.2022.073
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