End-to-End EEG-Based Stress Detection: A Benchmark Using Repurposed DEAP and Russell’s Circumplex Model

Stress is a critical factor that increases the risk of traffic accidents and should be addressed in driver monitoring and intelligent transportation systems. Electroencephalography (EEG) offers a direct, non-invasive measure of neural activity, making it a promising modality for real-time stress recognition. However, most existing studies have primarily focused on binary classification within constrained experimental settings, which limits reproducibility and practical relevance. In this study, the DEAP dataset was repurposed for driver stress detection via a Russell’s circumplex model–based label transformation, yielding three stress levels (no, low, high). Four state-of-the-art EEG models—EEGNet, EEGNeX, FBCNet, and TSception—were benchmarked under a subject-balanced stratified split strategy, with automated hyperparameter optimization using Optuna’s TPE sampler and Focal Loss to mitigate class imbalance. Experimental results revealed clear trade-offs among accuracy, inference latency, and computational complexity. FBCNet achieved the lowest inference latency (2.1 ms, 60,523 samples/s) with the smallest model footprint, whereas EEGNet demonstrated balanced performance with 78.8% accuracy. EEGNeX attained 81.6% accuracy at the cost of increased complexity, while TSception achieved the highest accuracy (98.9%) and strong generalization, confirming its suitability for EEG-based stress detection. By combining dataset relabeling, reproducible evaluation protocols, and comprehensive benchmarking, this study suggests TSception as the most effective model for EEG-based stress recognition and provides practical guidance for integrating EEG-driven stress monitoring into driver safety and intelligent transportation systems.