AccScience Publishing / BH / Online First / DOI: 10.36922/bh.2819
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Recognition predictive modeling using electroencephalogram

S.K.B. Sangeetha1 ,  Sandeep Kumar Mathivanan2 ,  Saurav Mallik3,4* ,  Aimin Li5
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1 Department of Computer Science and Engineering, Faculty of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
2 Department of Computer Science and Engineering, School of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India
3 Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
4 Department of Pharmacology and Toxicology, The University of Arizona, Tucson, Arizona, United States of America
5 School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, Shaanxi, China
Brain & Heart 2024, 2(2), 2819 https://doi.org/10.36922/bh.2819
Submitted: 24 January 2024 | Accepted: 19 March 2024 | Published: 15 May 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

A machine learning model that operates on raw electroencephalogram (EEG) signals is essential for accurately discerning the user’s current thoughts. Given the difficulty of categorizing EEG signals for use in brain-computer interface (BCI) programs, we adopted a systematic approach in this study to select an optimal predictive model. To enhance the effectiveness of our systematic approach, we extracted features such as band powers, averages, and root-mean-squared values. K-nearest neighbor (KNN), principal component analysis, and dual-layer neural networks were employed to evaluate and validate the effectiveness of the extracted features. The BCI IV competition-I dataset was utilized for analysis and validation. KNN achieved an average classification success rate of 98.02% compared to other methods. Furthermore, our research extends the application of this approach using it to create, test, and evaluate human driving behavior as a case study.

Keywords
Brain-computer interface
Electroencephalogram
Feature extraction
Human-computer interface
Machine learning
Neural network
Funding
None.
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Conflict of interest
The authors declare that they have no competing interests.
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