An EEG-based analysis of the effects of different music genres on driving stress.

INTRODUCTION: Sudden road conditions can trigger drivers' psychological stress, increasing the risk of traffic accidents. Music, as an emotion regulation tool, effectively alleviates stress and enhances psychological health. However, the effects of different genres of music on drivers' stress remain understudied.

METHODS: To address this, the present study collected 120 EEG recordings from 60 drivers in a standardized simulated driving environment and developed a classification model based on EEG signals to recognize emotions. By integrating time-frequency domain features (mean, variance, skewness, kurtosis, and power spectral density) with classification algorithms, the model accurately identified slight, moderate, and severe stress states in drivers, achieving an accuracy of 90%.

RESULTS: Furthermore, the study evaluated the intervention effects of four types of music (joyful, sorrowful, exhilarating, and gentle) on stress using EEG signals and subjective stress ratings. The results showed that gentle music had the best stress-relieving effect in both slight and severe stress states, reducing stress by 41.67% and 45%, respectively, whereas joyful music was most effective in relieving moderate stress, reducing moderate stress by 50%. In contrast, exhilarating and sorrowful music had weaker effects. Additionally, the asymmetry of frontal pole EEG signals was found to be significantly negatively correlated with stress levels.

DISCUSSION: This finding further supports the accuracy of the emotion recognition model and the potential effectiveness of the music intervention strategy. The study demonstrates that personalized music intervention strategies can help alleviate drivers' stress, thereby improving psychological health, enhancing driving safety, and increasing driving comfort.