Optimization and validation of multiscale feature selection for EEG-based recognition of drivers' negative emotions

Negative emotions, such as stress and anger, are significant factors leading to dangerous driving behavior. Investigating the impact of these emotions on driving safety is crucial for effective traffic injury prevention.

Electroencephalography (EEG) is a valuable tool for detecting emotional neural activity due to its high temporal resolution and noninvasive characteristics. This study systematically analyzes the multiscale characteristics of EEG signals across different frequency bands, brain regions, and feature domains to elucidate the neural mechanisms of stress and anger states during driving. EEG signals were collected from 32 participants in a driving simulation environment under both emotional (stress and anger) and nonemotional conditions. First, time domain features (mean, variance, skewness, kurtosis), frequency domain features (power spectral density, Shannon entropy), and spatial domain features (common spatial patterns) were extracted from five key brain regions-frontal pole, frontal, central, parietal, and occipital-and across five frequency bands ranging from delta to gamma. Second, feature selection was performed through correlation analysis, and a support vector machine classifier was utilized for machine learning validation.

The results indicate that the frontal pole region exhibited the highest frequency of neural activity, while frequency band analysis revealed that beta wave activity occurred most frequently. Furthermore, common spatial patterns demonstrated a significant ability to differentiate between stress and anger emotions. The optimal mixed feature set achieved classification accuracies of 85% for stress and 80% for anger, representing improvements of 20% to 55% and 7% to 55%, respectively, over the unselected feature set, as well as enhancements of 10% to 25% and 10% to 30%, respectively, compared to single-domain features.

These findings provide a neurophysiological basis for developing a wearable EEG-based driver emotion recognition system that has the potential to enhance traffic safety and reduce the risk of accidents by enabling timely interventions based on drivers' emotional states.