A Novel EEG-Based Assessment of Distraction in Simulated Driving under Different Road and Traffic Conditions

Ronca, Vincenzo; Brambati, Francois; Napoletano, Linda; Marx, Cyril; Trösterer, Sandra; Vozzi, Alessia; Aricò, Pietro; Giorgi, Andrea; Capotorto, Rossella; Borghini, Gianluca; Babiloni, Fabio; Di Flumeri, Gianluca

doi:10.3390/brainsci14030193

Cited by 7 publications

(1 citation statement)

References 61 publications

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“…Ronca et al. [ 23 ] developed an EEG-based distraction index by quantifying the driver’s mental workload and attention using the average power of different bands and channels. Considering the intricate interactions among different brain regions with distinct structures and functions, much attention has also been devoted to investigating brain networks [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network

Qi,

Liu,

Guan

et al. 2024

Cyborg Bionic Syst

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In this study, we propose an electrophysiological analysis-based brain network method for the augmented recognition of different types of distractions during driving. 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. 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.

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Section: Introductionmentioning

confidence: 99%

Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network

Qi,

Liu,

Guan

et al. 2024

Cyborg Bionic Syst

View full text Add to dashboard Cite

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Cognitive load and task switching in drivers: Implications for road safety in semi-autonomous vehicles

Xu,

Fard,

Zhang

et al. 2024

Transportation Research Part F: Traffic Psychology and Behaviou

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o-CLEAN: a novel multi-stage algorithm for the ocular artifacts’ correction from EEG data in out-of-the-lab applications

Ronca,

Flumeri,

Giorgi

et al. 2024

J. Neural Eng.

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In the context of Electroencephalographic (EEG) signal processing, artifacts generated by ocular movements, such as blinks, are significant confounding factors. These artifacts overwhelm informative EEG features and may occur too frequently to simply remove affected epochs without losing valuable data. Correcting these artifacts remains a challenge, particularly in out-of-lab and online applications using wearable EEG systems (i.e. with low number of EEG channels, without any additional channels to track EOG). Objective: the main objective of the present work consisted in validating a novel ocular blinks artefacts correction method, named o-CLEAN (multi-stage OCuLar artEfActs deNoising algorithm), suitable for online processing with minimal EEG channels. Approach: the research was conducted considering one EEG dataset collected in highly controlled environment, and a second one collected in real environment. The analysis was performed by comparing the o-CLEAN method with previously validated state-of-art techniques, and by evaluating its performance along two dimensions: a) the ocular artefacts correction performance (IN-Blink), and b) the EEG signal preservation when the method was applied without any ocular artefacts occurrence (OUT-Blink). Main results: results highlighted that i) o-CLEAN algorithm resulted to be, at least, significantly reliable as the most validated approaches identified in scientific literature in terms of ocular blink artifacts correction, ii) o-CLEAN showed the best performances in terms of EEG signal preservation especially with a low number of EEG channels. Significance: the testing and validation of the o-CLEAN addresses a relevant open issue in bioengineering EEG processing, especially within out-of-the-lab application. In fact, the method offers an effective solution for correcting ocular artifacts in EEG signals with a low number of available channels, for online processing, and without any specific template of the EOG. It was demonstrated to be particularly effective for EEG data gathered in real environments using wearable systems, a rapidly expanding area within applied neuroscience.

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A Novel EEG-Based Assessment of Distraction in Simulated Driving under Different Road and Traffic Conditions

Cited by 7 publications

References 61 publications

Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network

Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network

Cognitive load and task switching in drivers: Implications for road safety in semi-autonomous vehicles

o-CLEAN: a novel multi-stage algorithm for the ocular artifacts’ correction from EEG data in out-of-the-lab applications

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