The Effect of Alpha Oscillation Network Decoding on Driver Alertness

The countermeasure of driver fatigue is valuable for reducing the risk of accidents caused by vigilance failure during prolonged driving. Listening to the radio (RADIO) has been proven to be a relatively effective “in-car” countermeasure. However, the connectivity analysis, which can be used to investigate its alerting effect, is subject to the issue of signal mixing. In this study, we propose a novel framework based on clustering and entropy to improve the performance of the connectivity analysis to reveal the effect of RADIO to maintain driver alertness. Instead of reducing signal mixing, we introduce clustering algorithm to classify the functional connections with their nodes into different categories to mine the effective information of the alerting effect. Differential entropy (DE) is employed to measure the information content in different brain regions after clustering. Compared with the Louvain-based community detection method, the proposed method shows superior ability to present RADIO effect in the confused functional connection matrices. Our experimental results reveal that the active connection clusters distinguished by the proposed method gradually move from the frontal region to the parieto-occipital region with the progress of fatigue, consistent with the alpha energy changes in the two brain areas. The active clusters in the parieto-occipital region significantly decrease and the most active clusters remain in the frontal region when RADIO is taken. The estimation results of DE confirm the significant change (p<0.05) of information content due to the cluster movements. Hence, preventing the movement of the active clusters from the frontal region to the parieto-occipital region may correlate with maintaining driver alertness. The revelation of the alerting effect is helpful for the targeted upgrade of the fatigue countermeasures.