Hybrid deep learning for mental workload classification using EEG with enhanced preprocessing and interpretability

by Osama Abdelrahman, Chew XinYing, Esraa Faisal Malik, Khaw Khai Wah, Cheong Zhi Lin, Teoh Wei Lin Mental workload classification is critical in safety-sensitive fields such as healthcare and aviation. However, electroencephalography-based approaches still face challenges with generalizability, noise robustness, and interpretability.

In this study, we propose an integrated hybrid deep learning framework to address these limitations and enable robust, interpretable electroencephalography-based mental workload classification. The proposed approach uses a Variational Autoencoder to enhance noise reduction and feature extraction from band-wise topographical videos, a Convolutional Block Attention Module to adaptively focus on important spatial-channel Electroencephalogram features, and a Bidirectional Long Short-Term Memory network to capture complex temporal dependencies under leave-one-subject-out cross-validation.

We conducted ablation studies to identify each architecture component’s contribution and sensitivity analyses to determine the optimal parameters. The model achieved the highest overall accuracy among the baselines and reached an average accuracy of 83.9% across subjects for classifying four mental workload levels.

Ablation studies confirmed the added value of all three architecture components for improving performance.