XGBoost Classification of Epileptic EEG Using Nonlinear Dynamical Features and SHAP.

Overview

1. School of Medical Information, Wannan Medical University, Wuhu 241002, China; (X.L.)
2. Anhui Province High-Quality Dataset Construction Base for Smart Healthcare, Wuhu 241002, China
3. Institute of Medical Artificial Intelligence, Wannan Medical University, Wuhu 241002, China

Abstract

To evaluate whether nonlinear descriptors of electroencephalogram (EEG) signals support interpretable XGBoost classification and to determine how analysis window duration affects performance. A secondary analysis of the public Bonn EEG dataset was performed. Nine nonlinear features were extracted from non-overlapping 1, 5, 10, and 20 s windows after an original-recording-level train/validation/test split, and a multiclass XGBoost model was interpreted with class-specific SHAP values. The model achieved 93.3% overall accuracy; the class-specific AUC values were 0.978 for Z/O, 0.978 for N/F, and 0.984 for S. Across the four fixed-split duration conditions, the 1 s condition had the lowest descriptive performance, whereas the 5, 10, and 20 s conditions were broadly comparable; no uniquely optimal duration was established. The nonlinear-feature/XGBoost framework provides interpretable benchmark segment classification evidence. Because EEG is modeled as a stochastic process and the dataset is small and heterogeneous, the SHAP attributions do not establish physiological causality or clinical diagnostic validity.

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

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Data

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Data Availability Statement

The EEG dataset analyzed in this study is publicly available at https://www.ukbonn.de/epileptologie/arbeitsgruppen/ag-lehnertz-neurophysik/downloads/ (accessed on 10 March 2020). The source code supporting the findings of this study is available from the corresponding author upon reasonable request.

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 4 keywords, 1 funder, 20 references.

Cite

This paper

Lu, X., Lou, H., Jin, X., & Zhang, B. (2026). XGBoost Classification of Epileptic EEG Using Nonlinear Dynamical Features and SHAP. Entropy (Basel, Switzerland), 28(8), 920. https://doi.org/10.3390/e28080920

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