Comparative Analysis of EEGNet, EEG Conformer, and Spiking Neural Networks for Mental Workload Classification
Year: 2026
Type: preprint
Abstract: Mental workload assessment is important for intelligent systems designed to monitor human cognitive state. This study presents a comparative analysis of EEGNet, EEG Conformer, and Spiking Neural Networks (SNNs) for binary mental workload classification using the Simultaneous Task EEG Workload (STEW) dataset. EEG signals were processed using fixed-length segmentation, artifact detection, subject-wise data partitioning, and training-set-based Z-score normalization. All three architectures were evaluated under a consistent preprocessing and evaluation framework using accuracy, precision, recall, F1-score, and ROC-AUC. EEGNet achieved the highest performance with 94.14% accuracy, followed by EEG Conformer at 62.61% and SNN at 53.60%. The findings indicate that the compact EEGNet architecture provided stronger generalization under the experimental conditions of this study.
Author: Riaz, Humaima
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7208736274
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