Driver Distraction Detection Based on EEG Feature Fusion Using Random Forest

EasyChair Preprint 8441

Abstract

Driver distraction has been one of the primary causes of traffic accidents. Electroencephalography (EEG), a record of the electric potential from the scalp, is considered as a reliable indicator of brain activities. It has been widely used to detect driver distraction. Previous studies have analyzed driver distraction based on time and frequency domain features of EEG. However, challenges still exist in manifesting the distraction information of EEG which contains a large amount of complex information about driver distraction in realistic driving scenarios from the perspective of complexity. In this paper, we propose a driver distraction detection framework using Random Forest (RF) based on the complexity feature fusion of EEG in real driving environment. Five complexity-based features of EEG are firstly extracted with a sliding window. Then, an RF classifier is trained with the extracted features to detect driver distraction. Our results show that differential entropy (DE) with an accuracy of 72.9% achieves the best result while single type feature is applied to detect distraction. The classifier’s accuracy is further increased by about 7% using fused multiple features compared with the highest accuracy obtained by single type feature. In terms of feature contribution, we found that the feature with the best distraction detection result by using single type features may not contribute the most when using fused multiple features.

Keyphrases: EEG, Entropy, Random Forest, driver distraction, feature fusion

@booklet{EasyChair:8441,
  author    = {Xin Zuo and Chi Zhang and Jian Zhao and Timo Hämäläinen and Fengyu Cong},
  title     = {Driver Distraction Detection Based on EEG Feature Fusion Using Random Forest},
  howpublished = {EasyChair Preprint 8441},
  year      = {EasyChair, 2022}}