image: The 4 subtasks include a math calculation task (task-1), a clock angle discrimination task (task-2), a number sequence recognition task (task-3), and a mobile phone task (task-4).
Credit: Wei Guan, Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University.
A research paper by scientists at Beijing Jiaotong University proposed an electrophysiological analysis-based brain network method for the augmented recognition of different types of distractions during driving.
The new research paper, published on Jul. 04 in the journal Cyborg and Bionic Systems, designed and conducted a simulated experiment comprising 4 distracted driving subtasks. Three connectivity indices, including both linear and nonlinear synchronization measures, were chosen to construct the brain network. By computing connectivity strengths and topological features, we explored the potential relationship between brain network configurations and states of driver distraction.
Driver distractions, such as cognitive processing and visual disruptions during driving, lead to distinct alterations in the electroencephalogram (EEG) signals and the extracted brain networks. We designed and conducted a simulated experiment comprising 4 distracted driving subtasks. Three connectivity indices, including both linear and nonlinear synchronization measures, were chosen to construct the brain network. “By computing connectivity strengths and topological features, we explored the potential relationship between brain network configurations and states of driver distraction.” explained study author Wei Guan, a professor at Beijing Jiaotong University. Statistical analysis of network features indicates substantial differences between normal and distracted states, suggesting a reconfiguration of the brain network under distracted conditions. Different brain network features and their combinations are fed into varied machine learning classifiers to recognize the distracted driving states. The results indicate that XGBoost demonstrates superior adaptability, outperforming other classifiers across all selected network features. For individual networks, features constructed using synchronization likelihood (SL) achieved the highest accuracy in distinguishing between cognitive and visual distraction. The optimal feature set from 3 network combinations achieves an accuracy of 95.1% for binary classification and 88.3% for ternary classification of normal, cognitively distracted, and visually distracted driving states. “The proposed method could accomplish the augmented recognition of distracted driving states and may serve as a valuable tool for further optimizing driver assistance systems with distraction control strategies, as well as a reference for future research on the brain-computer interface in autonomous driving.” said study authors.
The aim of this study was to establish an augmented recognition framework of distracted driving states by leveraging varied synchronization indicators in brain networks. “A simulated carfollowing experiment containing 4 distraction subtasks was designed to encompass the cognitive distraction and visual distraction states. Tree connectivity indices including synchronization likelihood (SL), phase locking value (PLV), and coherence indicator were selected to construct functional brain networks. The connectivity strength as well as 4 global topological features were calculated to explore the potential relationship between the configuration of the brain network and the occurrence of driving distraction. Subsequently, the machine learning classifiers were trained and implemented to recognize the different distracted driving states based on brain network features.” said Geqi Qi.
The main contributions of the paper are listed as follows: a. The configuration of the functional brain network during distracted driving is constructed through electrophysiological analysis using 3 synchronization indicators as network edges and 4 global topological features as network properties. b. The performance of different synchronization indicators in brain networks is compared and the SL presents optimal recognition capability in distinguishing between normal and distracted driving states using single brain network knowledge. c. The augmented framework of recognizing normal, visual distraction, and cognitive distraction states is proposed, and the best classification performance is achieved by utilizing the combined global topological features of the 3 varied brain networks characterized by different synchronization indicators. Totally, such electrophysiological analysis of the brain network will provide a foundation for the advancement of driver assistance systems with distraction control strategies and the development of brain-controlled systems, in both conventional human driving scenarios and autonomous driving contexts.
Authors of the paper include Geqi Qi, Rui Liu, Wei Guan, Ailing Huang
This work was supported by the the National Natural Science Foundation of China (grant nos. 72101014 and 72271018) and the Key Laboratory of Brain-Machine Intelligence for Information Behavior, Ministry of Education, China (2023JYBKFKT009).
The paper, “Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network” was published in the journal Cyborg and Bionic Systems on Jul 04, 2024, at DOI: 10.34133/cbsystems.0130.
Journal
Cyborg and Bionic Systems