EEG-Based Driver Fatigue Monitoring within a Human-Ship-Environment System: Implications for Ship Braking Safety

Area of Science:

• Maritime Safety
• Human Factors Engineering
• Neuroscience

Background:

• Current ship safety braking relies heavily on operator performance, posing risks due to fatigue.
• Operator fatigue can significantly compromise navigation safety and lead to accidents.

Purpose of the Study:

• To reduce navigation safety risks by mitigating the impact of operator fatigue.
• To develop a predictive model for driver fatigue integrated into ship braking systems.

Main Methods:

• Established a human-ship-environment monitoring system with a focus on brain fatigue detection.
• Used electroencephalography (EEG) and the Stroop task to monitor and induce driver fatigue.
• Applied principal component analysis (PCA) for feature extraction (CF, PSE) and ridge regression for fatigue level scoring.

Main Results:

• Identified key EEG features (CF, PSE) correlated with driver fatigue severity.
• Developed a fatigue scoring model using PCA-extracted features and ridge regression.
• Validated the effectiveness of the integrated system in enhancing ship braking safety.

Conclusions:

• The proposed human-ship-environment monitoring system and fatigue prediction model improve ship braking controllability.
• Real-time driver fatigue monitoring enables timely interventions, ensuring navigation safety and operator health.