@article{Jiang_Fan_2025,
	author = {Jiang, Ruonan and Fan, Shiqi},
	title = {Seafarer Mental Workload Assessment Using a Hybrid Deep Learning Model},
	journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation},
	volume = {19},
	number = {1},
	pages = {55-60},
	year = {2025},
	url = {./Article_Seafarer_Mental_Workload_Assessment_Jiang,73,1477.html},
	abstract = {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.},
	doi = {10.12716/1001.19.01.07},
	issn = {2083-6473},
	publisher = {Gdynia Maritime University, Faculty of Navigation},
	keywords = {Mental Workload (MWL), Maritime Safety, Seafarers, Bridge Simulator, Cognitive Performance, Deep Learning, Human Factor (HF), Electroencephalography (EEG)}
}