ArticleScientific reports2026
SHAP analysis of an improved EEG-based mental workload classification framework: utilizing data augmentation and explainable AI.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- An Interpretable Framework for Identifying EEG Spectral Biomarkers of Pathological Brain Aging.Diagnostics (Basel, Switzerland) · 2026Article
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Abstract
Mental workload (MWL) classification using electroencephalogram (EEG) signals is crucial for cognitive neuroscience and is also a challenging research area in brain-computer interface (BCI). Since the EEG signals fluctuate a lot across sessions and individuals, there is a need for a robust classification model that generalizes well for real-world applications. In this work, we used the publicly available dataset "An EEG dataset for cross-session mental workload estimation: passive BCI competition of the Neuroergonomics Conference 2021", and the standard EEGNet model to classify the MWL into three classes (Low, Med, and High). To improve the performance of the model, a synthetic minority oversampling technique (SMOTE) was used by creating synthetic EEG samples, and key hyperparameters (F
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