Machine Learning-Based Classification of Hybrid BCI Signals using Mayfly-Optimized Multiclass Weighted Random Forest

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Published: Sep 21, 2023
DOI: https://doi.org/10.17762/ijritcc.v12i1.7907
Keywords:
Brain-Computer Interface (BCI), Electroencephalography (EEG), Wiener filter (WF), discrete wavelet transform (DWT), mayfly optimized multiclass weighted random forest (MFO-MWRF)

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R. Shelishiyah
Research Scholar, Department of Biomedical Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, Tamilnadu, India
Thiyam Deepa Beeta
Associate Professor, Department of Biomedical Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, Tamilnadu, India
M. Jehosheba Margaret
Research Scholar, Department of Biomedical Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, Tamilnadu, India

Abstract

The Brain-Computer Interface (BCI) technologies have excellent clinical and non-clinical uses. Among the most popular imaging methods adopted in BCI technologies is electroencephalography (EEG). But EEG signals are typically quite complicated, so analyzing them necessitates a significant amount of effort. With the help of machine learning (ML), this research investigates the feasibility of a BCI platform based on the motor imagery (MI) concept. The steps of pre-processing, feature extraction and classification are the underpinning of any conventional ML model. To train such a model, however, a large amount of data is needed. To address this gap, this work introduces a new mayfly-optimized multiclass weighted random forest (MFO-MWRF) technique that uses retrieved features as input to mitigate the need for this supplementary data. In this study, we gather a dataset of hybrid EEG and fNIRS motor imagery that can be pre-processed using a Wiener filter (WF) to filter out noisier signals without affecting the high-quality images. The characteristics are extracted using the discrete wavelet transform (DWT). The research results indicate that the proposed approach achieves the best performance compared to existing approaches for classifying motor movement images.

Article Details

How to Cite
Shelishiyah, R. ., Beeta, T. D. ., & Margaret, M. J. . (2023). Machine Learning-Based Classification of Hybrid BCI Signals using Mayfly-Optimized Multiclass Weighted Random Forest. International Journal on Recent and Innovation Trends in Computing and Communication, 12(1), 29–35. https://doi.org/10.17762/ijritcc.v12i1.7907
Issue
Vol. 12 No. 1 (2024)
Section
Articles

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