Key result
The rhythm-dependent multilayer brain network (RDMB network) achieved an average accuracy of 95.28% for detecting driving fatigue using 5-second EEG samples, outperforming compared methods.
Why the study?
Fatigue driving significantly impacts automobile accidents, making the recognition of driving fatigue an important approach to addressing this issue.
Does the RDMB network improve the detection of driving fatigue using EEG signals?
Does the RDMB network improve the detection of driving fatigue using EEG signals?
A novel rhythm-dependent multilayer brain network using EEG signals can accurately detect driving fatigue.
RDMB network enables raw EEG fatigue detection; leaves open real-world validation before any clinical use.
Fatigue driving has attracted a great deal of attention for its huge influence on automobile accidents. Recognizing driving fatigue provides a primary but significant way for addressing this problem. In this paper, we first conduct the simulated driving experiments to acquire the EEG signals in alert and fatigue states. Then, for multi-channel EEG signals without pre-processing, a novel rhythm-dependent multilayer brain network (RDMB network) is developed and analyzed for driving fatigue detection. We find that there exists a significant difference between alert and fatigue states from the view of network science. Further, key sub-RDMB network based on closeness centrality are extracted. We calculate six network measures from the key sub-RDMB network and construct feature vectors to classify the alert and fatigue states. The results show that our method can respectively achieve the average accuracy of 95.28% (with sample length of 5 s), 90.25% (2 s), and 87.69% (1 s), significantly higher than compared methods. All these validate the effectiveness of RDMB network for reliable driving fatigue detection via EEG.
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Dang et al. (2020) studied Driving fatigue. Rhythm-dependent multilayer brain network (RDMB network) vs. Compared methods was evaluated on Average accuracy for classifying alert and fatigue states. The rhythm-dependent multilayer brain network (RDMB network) achieved an average accuracy of 95.28% for detecting driving fatigue using 5-second EEG samples, outperforming compared methods.
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