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Citation |
Balasubramanian V, Bhardwaj R. Transp. Res. F Traffic Psychol. Behav. 2018; 58: 797-806. |
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Copyright |
(Copyright © 2018, Elsevier Publishing) |
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DOI |
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PMID |
unavailable |
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Abstract |
Electroencephalography (EEG) is a good measure of cognitive behavior. In this study, we have used a non-contact capacitive coupled ECG (cECG) technology as a surrogate of EEG to estimated driver fatigue. Thirty five male volunteers participated in this study. HRV components such as low frequency (LF), high frequency (HF), and LF/HF ratio were calculated using cECG system. EEG bands (beta, alpha and theta) and ratio index (α+θ)/β were also calculated using EEG system along with HRV components. To find out whether there is any interaction between EEG and cECG, we have estimated coherency, gain and phase shift between high frequency of RR interval and all bands of EEG frequency. Decreasing trends in LF and LF/HF ratio whereas increasing trend in HF with significant difference (p < 0.05) were observed during the course of 120 min of simulated driving. EEG results showed significant (p < 0.05) decrease in beta activity; and an increase in alpha and theta activity while driving. Coherencies between normalized high frequency HFnu and EEG band were high whereas the gain was the maximum for delta band followed by theta band. Phase shift between theta band and (HFnu) was 11.24 ± 13.67°. Cardiac vagal activity shows largely corresponding change in theta band by a phase shift subsequent to a lead of 5.35 ± 4.15 min. These results show that the cognitive fatigue can be estimated by using cECG based on coherence between EEG and cECG. |
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Keywords |
Capacitive ECG; Coherence analysis; Driver fatigue; EEG analysis; Heart rate variability; Static simulator |