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

Yan Li; Bo Tang; Bingjie Luo; Yingzhang Wu; Yongfeng Ding

doi:10.1080/15389588.2025.2591089

January 14, 2026Traffic Injury Prevention

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

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Key Points

The central aim is to develop a system for recognizing negative emotions in drivers using EEG technology.
Optimized multiscale feature selection techniques were applied to EEG data.
The system focused on identifying negative emotional states in drivers.
Validation of the feature selection process was conducted to ensure effectiveness.
The optimized system successfully recognized drivers' negative emotions.
Findings indicate potential for enhancing traffic safety through timely interventions.

Implication

Demonstrates a system for recognizing drivers’ negative emotions, suggesting it could improve traffic safety.

Abstract

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.

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Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/6969d4fd940543b977709dc5 https://doi.org/10.1080/15389588.2025.2591089

View Full Paper

Ask AI

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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