Functional connectivity analysis of distracted drivers based on the wavelet phase coherence of functional near-infrared spectroscopy signals

Abstract

The present study aimed to evaluate the functional connectivity (FC) in relevant cortex areas during simulated driving with distraction based on functional near-infrared spectroscopy (fNIRS) method. Twelve subjects were recruited to perform three types of driving tasks, namely, straight driving, straight driving with secondary auditory task, and straight driving with secondary visual vigilance task, on a driving simulator. The wavelet amplitude (WA) and wavelet phase coherence (WPCO) of the fNIRS signals were calculated in six frequency intervals: I, 0.6–2 Hz; II, 0.145–0.6 Hz; III, 0.052–0.145 Hz; IV, 0.021–0.052 Hz; and V, 0.0095–0.021 Hz, VI, 0.005–0.0095Hz. Results showed that secondary tasks during driving led to worse driving performance, brain activity changes, and dynamic configuration of the connectivity. The significantly lower WA value in the right motor cortex in interval IV, and higher WPCO values in intervals II, V, and VI were found with additional auditory task. Significant standard deviation of speed and lower WA values in the left prefrontal cortex and right prefrontal cortex in interval VI, and lower WPCO values in intervals I, IV, V, and VI were found under the additional visual vigilance task. The results suggest that the changed FC levels in intervals IV, V, and VI were more likely to reflect the driver’s distraction condition. The present study provides new insights into the relationship between distracted driving behavior and brain activity. The method may be used for the evaluation of drivers’ attention level.

Effective Connectivity in Response to Posture Changes in Elderly Subjects as Assessed Using Functional Near-Infrared Spectroscopy

“…The pre-processing method for fNIRS data, which has been described in detail in previous studies (Han et al, 2014 ; Xu et al, 2017 ), was conducted as follows. First, moving average method was applied to remove the noise-like abrupt spikes caused by movement artifacts or background light.…”

Alterations in the coupling functions between cerebral oxyhaemoglobin and arterial blood pressure signals in post-stroke subjects

“…First, the moving average method was applied to remove the noise-like abrupt spikes caused by the movement artifacts or background light, involving an algorithm based on moving standard and spine interpolation routines[ 47 , 48 ]. After the removal of abnormal points, the delta [HbO 2 ] and MAP signals were band-pass filtered with a Butterworth filter at a low cut-off frequency of 0.005 Hz to remove extremely slow variations and a high cutoff frequency of 2 Hz to remove the uncorrelated noise components.…”

Diffuse optical assessment of cerebral‐autoregulation in older adults stratified by cerebrovascular risk

“…Although the origin of LFOs remains controversial in the literature, studies have found that the endothelial, neurogenic, and myogenic controls are the main mechanisms responsible for maintaining CBF constant during blood pressure fluctuations [22]. Previous studies have classified LFOs into four frequency intervals: I (0.005-0.0095 Hz), II (0.0095-0.02 Hz), III (0.02-0.07 Hz) and IV (0.07-0.2 Hz) [23][24][25][26][27][28][29]. Interval-I and interval-II reflect respectively nitric oxide (NO) dependent and independent endothelial metabolic activities [24,26,27].…”

“…Previous studies have classified LFOs into four frequency intervals: I (0.005-0.0095 Hz), II (0.0095-0.02 Hz), III (0.02-0.07 Hz) and IV (0.07-0.2 Hz) [23][24][25][26][27][28][29]. Interval-I and interval-II reflect respectively nitric oxide (NO) dependent and independent endothelial metabolic activities [24,26,27]. Interval-III and interval-IV correspond, respectively, to neurogenic and myogenic related metabolic activities [23,28,29].…”

“…Cerebral hemodynamic data were continuously collected at rest, during, and after 70 HUT. LFO intensities were extracted from the measured variables (MAP, CBF, [HbO 2 ] and [Hb]) using power spectral analyses in four frequency intervals (interval-I -interval-IV) [23][24][25][26][27][28][29]. The LFO transfer function gains were quantified by transfer function analyses with MAP as the input and cerebral hemodynamic variables (CBF, [HbO 2 ] and [Hb]) as the outputs [22].…”