Comprehensive benchmarking and explainable machine learning analysis of EEG imagery activity recognition.

Overview

1. Department of Avionics Engineering, Aviation and Aerospace University, Bangladesh, Dhaka, 1215 Bangladesh
2. Department of Computer Science Engineering, BRAC University, Dhaka, 1212 Bangladesh
3. James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ UK
4. School of Computing, Engineering and Physical Sciences, University of the West of Scotland, High St, Paisley, PA1 2BE UK

Abstract

Motor imagery (MI)-based brain–computer interfaces (BCIs) enable users to control external devices using EEG signals, offering great potential in assistive and rehabilitation technologies. However, MI recognition remains challenging due to EEG’s low signal-to-noise ratio (SNR), inter-subject variability, and complex spatiotemporal patterns. Existing approaches often suffer from limited accuracy, high computational cost, and poor interpretability. In response to these challenges, we present the first comprehensive benchmarking of the publicly available EEG-hand movement (EEG-HM) dataset. Our study aims to establish a standardized performance baseline, guide the selection of optimal models by jointly considering accuracy, prediction time, and explainability, and ultimately accelerate progress in MI-BCI development. We have proposed a two-stage optimization of machine learning models that employs both feature selection and hyperparameter tuning. We exploit five feature selection algorithms for selecting the best set of EEG electrodes and frequency bands, while Bayesian optimization is exploited for machine learning model optimization through hyperparameter tuning. Furthermore, to validate the neurophysiological basis of our model’s decisions, we leverage explainable AI (XAI) algorithms—LIME and SHAP—quantifying the contributions of specific EEG electrodes and frequency bands to interpret its decision-making process. Through extensive simulations, the proposed two-stage optimization of the machine learning model demonstrates a superior performance in terms of accuracy, precision, and recall. This method outperforms the existing methods by 21.47% in accuracy with competitive prediction time. Its performance is further evaluated on the PhysioNet MI dataset, achieving a 4.67% accuracy improvement over state-of-the-art methods. Through LIME and SHAP, we provide the local and global explanations for no activity, left-hand, and right-hand imagery movements. Additionally, we analyze how various EEG frequency bands and electrode locations interact during the performance of different motor imagery hand movements.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• doi:10.17632/dpmtgrn8d8.4, at the source; found in “Data availability”

Data availability

1. Eeg hand movement/user prediction data are publicly available on Kaggle Notebook (2025). Avaiable at: https://www.kaggle.com/code/gcdatkin/eeg-hand-movement-user-prediction/input 2. Data are publicly available on Mendeley Data: Physionet EEGMMIDB in MATLAB structure and CSV files to leverage accessibility and exploitation (Version 4; published 13 May 2024). Data are available in the following URL: https://doi.org/10.17632/dpmtgrn8d8.4

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 8 keywords, 9 MeSH terms, 19 references.

Cite

This paper

Siam, M. J. N., Showrov, T. A., Hossain, M. S., Ayaan, N. S., Bari, S. M. S., Tariq, F., & Mahmud, A. A. (2026). Comprehensive benchmarking and explainable machine learning analysis of EEG imagery activity recognition. Scientific reports, 16(1), 22313. https://doi.org/10.1038/s41598-026-50997-y

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