Abstract
This study addresses the crucial task of accurately classifying brainwave signals associated with distinct brain states, utilizing five supervised machine learning algorithms: K-Nearest Neighbors, Support Vector Machine, Decision Tree, Linear Discriminant Analysis, and Logistics Regression. The primary objectives encompass developing and optimizing these models, assessing the impact of hyperparameter tuning on performance through metrics like accuracy, consistency, and prediction time, and creating a user-friendly web-based deployment interface. The Decision Tree model emerges with the highest average accuracy score of 90.03%, swift prediction times, and notable consistency. Following hyperparameter tuning, SVM and LR showcase substantial accuracy gains (15.63% and 1.50% respectively), enhancing all models' consistency. KNN and SVM are identified as the top-performing algorithms for accurate brain state classification. This study's findings hold implications for neuroscience research, human–computer interaction, healthcare diagnostics, and assistive technologies, offering insights into both effective algorithm selection and the role of hyperparameter tuning in optimizing model performance.
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Acknowledgements
This work was in part supported by the Ministry of Higher Education (Fundamental Research Grant Scheme grant number FRGS/1/2021/SKK06/UTAR/02/4).
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Poh Foong Lee: conceptualization, methodology, software, data curation, validation, formal analysis, writing—original draft, writing—review and editing. Kah Yoon Choong: investigation, methodology, software, data curation, methodology, validation.
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Lee, P.F., Chong, K.Y. Machine learning for physical motion identification using EEG signals: a comparative study of classifiers and hyperparameter tuning. J Ambient Intell Human Comput 15, 2455–2466 (2024). https://doi.org/10.1007/s12652-024-04764-4
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DOI: https://doi.org/10.1007/s12652-024-04764-4