Inter-hemispheric electroencephalography coherence analysis: Assessing brain activity during monotonous driving

Abstract

The current study investigated the effect of monotonous driving on inter-hemispheric electroencephalography (EEG) coherence. Twenty-four non-professional drivers were recruited to perform a fatigue instigating monotonous driving task while 30 channels of EEG were simultaneously recorded. The EEG recordings were then divided into 5 equal sections over the entire driving period for analysis. Inter-hemispheric coherence was computed from 5 homologous EEG electrode pairs (FP1-FP2, C3-C4, T7-T8, P7-P8, and O1-O2) for delta, theta, alpha and beta frequency bands. Results showed that frontal and occipital inter-hemispheric coherence values were significantly higher than central, parietal, and temporal sites for all four frequency bands (p<0.0001). In the alpha frequency band, significant difference was found between earlier and later driving sections (p=0.02). The coherence values in all EEG frequency bands were slightly increased at the end of the driving session, except for FP1-FP2 electrode pair, which showed no significant change in coherence in the beta frequency band at the end of the driving session.

Between-Frequency Topographical and Dynamic High-Order Functional Connectivity for Driving Drowsiness Assessment

“…Studies using spectral coherence also showed similar results during heightened fatigue [18], [19]. Significant increases of coherence values were observed in delta, theta, alpha, and beta bands [18] and the number of synchronized regions also increased [19]. In a study using spectral coherence and phase locking value (PLV), significant increases of PLV values in theta band occurred after a prolonged cognitive task, while PLV and spectral coherence values in beta band decreased significantly [22].…”

“…Significant increases of mean phase coherence (MPC) in delta and alpha bands were observed during the period of driving drowsiness and the number of connective functional units (FUs) also increased [16]. Studies using spectral coherence also showed similar results during heightened fatigue [18], [19]. Significant increases of coherence values were observed in delta, theta, alpha, and beta bands [18] and the number of synchronized regions also increased [19].…”

“…The low-order FC was constructed by computing the envelope correlation of EEG signals. The signals in each epoch were band-pass filtered to theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands, which were frequently reported in the previous studies as relevant to drowsiness [17], [18], [20], [21], [23]. The envelopes of the filtered signals were computed using the Hilbert transform.…”

Cross-conditional entropy and coherence analysis of pharmaco-EEG changes induced by alprazolam

“…Other authors have used different approaches to assess cerebral connectivity (David et al 2004;Stam 2005), and in this context cross-entropy measures have been proposed to characterize the probability of finding similar patterns between neural signals (Papadelis et al 2007;Zhang et al 2007;Xie et al 2010). Along with these innovative techniques, classical measures such as correlation, power spectral density and coherence have also been used recently (Franciotti et al 2006;Zavaglia et al 2008;Jap et al 2010;Alonso et al 2011).…”

Microsleeps and Their Detection From the Biological Signals

“…During the transitional phase to fatigue, there were no significant differences in the delta and alpha magnitudes across the entire brain [9]. The inter-hemispheric EEG coherence analysis was shown in the study of Jap et al [10], which revealed that the interhemispheric coherence level was significantly higher at the frontal and occipital sites compared to the central, parietal, and temporal sites throughout the driving sessions.…”