Configurable Modular EEG Classification Framework with Multiscale Features and Ensemble Learning: A Reproducible Evaluation for Schizophrenia Detection.

Overview

1. Department of Speech-Language-Hearing Sciences, University of Minnesota, Minneapolis, MN 55455, USA
2. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
3. Wayzata High School, 4955 Peony Ln N, Plymouth, MN 55446, USA
4. Speech-Language-Hearing Center, School of Foreign Languages, Shanghai Jiao Tong University, Shanghai 200240, China
5. National Research Centre for Language and Well-Being, Shanghai 200240, China
6. Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, USA

Abstract

EEG-based classification of mental disorders has increasingly relied on deep learning models, which are computationally intensive and difficult to interpret, limiting reproducibility and clinical deployment in resource-constrained or cross-site settings. We propose a configurable and modular machine learning framework for EEG-based classification that emphasizes interpretability, flexibility, and rigorous evaluation using schizophrenia detection as a representative use case. Our framework integrates standardized preprocessing, multiscale feature extraction, minimum redundancy–maximum relevance feature selection, and configurable ensemble learning. It also supports multiple validation strategies, including random splits, k-fold cross-validation, and leave-one-subject-out (LOSO), enabling systematic assessment of subject-level generalization. We evaluated the framework on two open EEG datasets: Warsaw IPN (Institute of Psychiatry and Neurology, 19 channels, 250 Hz; 28 subjects) and a Moscow adolescent cohort (16 channels, 128 Hz; 84 subjects). Results show that validation strategy strongly affects model performance. While K-fold validation yielded epoch-level accuracies of 98.06% and 91.47%, LOSO results were much lower: 76.12% (epoch-level) and 82.14% (subject-level) for Dataset 1, and 70.71% (epoch-level) and 77.38% (subject-level) for Dataset 2. These findings demonstrate the risk of overestimated performance due to data leakage and underscore the importance of subject-independent evaluation. Our proposed framework provides a low-complexity, interpretable, and extensible benchmark for reproducible EEG-based machine learning, with interpretable feature representations linked to EEG dynamics and potential applicability to broader neuroengineering and clinical decision-support systems.

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Data Availability Statement

The code used for EEG preprocessing, feature extraction, and machine learning analyses in this study is publicly available on GitHub at https://github.com/zhang470 (accessed on 2 April 2026). The two EEG datasets analyzed in this study are publicly available from their original sources and are cited in the manuscript.

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

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Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 2 funders, 110 references.

Cite

This paper

Han, X., Emara, Y., Zhang, A., Lin, Y., & Zhang, Y. (2026). Configurable Modular EEG Classification Framework with Multiscale Features and Ensemble Learning: A Reproducible Evaluation for Schizophrenia Detection. Bioengineering (Basel, Switzerland), 13(4), 430. https://doi.org/10.3390/bioengineering13040430

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