%0 Journal Article
%A Jiang, Ruonan
%A Fan, Shiqi
%T Seafarer Mental Workload Assessment Using a Hybrid Deep Learning Model
%J TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation
%V 19
%N 1
%P 55-60
%D 2025
%U ./Article_Seafarer_Mental_Workload_Assessment_Jiang,73,1477.html
%X Human errors in maritime operations are closely linked to seafarers' mental workload; however, traditional assessment methods lack real-time neurocognitive resolution. This study introduces a novel psychophysiological framework that integrates electroencephalography (EEG) analysis with deep learning to objectively quantify seafarers' mental workload during onboard operations. A high-fidelity bridge simulator was utilized to generate critical maritime scenarios, including ship encounters, narrow channel navigation, poor visibility, and emergency responses. High-density EEG signals were analyzed to extract spectral features (Gamma, Beta, Alpha, Theta, Delta). A hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model was proposed to classify workload states of seafarers, combining Convolutional Neural Network (CNN)-extracted frequency patterns with Bidirectional Long Short-Term Memory (Bi-LSTM)-captured temporal dynamics, which achieves 96% accuracy. Furthermore, SHAP interpretability analysis indicated that Theta and Alpha frequencies are key indicators in distinguishing between high and low workloads for seafarers. These results provide a quantitative tool for cognitive assessment of seafarers in maritime training and serve as a guideline for workload allocation in ship bridge teams for shipping companies and maritime authorities.
%@ 2083-6473
%R 10.12716/1001.19.01.07