Fusion of visible light and EEG signals for a driving distraction and road rage detection system

When handling with detection in driving distraction and road rage, the object detection model using visible light like image often suffers from the difficulty of environmental disturbance while the detection model using EEG signal often faces the difficulty of inconspicuous classification and inconvenient collection. In this paper, a novel and improved model fusing visible light and EEG signals is proposed, which the ECA-YOLOv8 model based on the ECANet is used for deep learning to identify the current state of the driver for the visible light images and next the EEG signal spectrum analysis technology is used to identify it again if the output threshold is lower than 0.6. Additionally, a simple and practical detection system is set up. Compared with single source detection, fusing visible light and EEG signals shows an effective increase in accuracy and stability.