Assessing Drivers’ Fatigue State Under Real Traffic Conditions Using EEG Alpha Spindles

The effectiveness of EEG alpha spindles, defined by short narrowband bursts in the alpha band, as an objective measure for assessing driver fatigue under real driving conditions was examined using an algorithm for the identification of alpha spindles. The method is applied to data recorded under real traffic conditions and compared with the performance of the traditional EEG fatigue measure alpha band power. Statistical analysis revealed significant increases from the first to the last driving section of alpha band power; with larger effect sizes for the alpha spindle based measures. An increased level of fatigue for drop-outs, as compared to participants who did not abort the drive, was observed only by means of alpha spindle parameters. EEG alpha spindle parameters increase both fatigue detection sensitivity and specificity as compared to EEG alpha band power. It is demonstrated that alpha spindles are superior to EEG band power measures for assessing driver fatigue under real traffic conditions

EEG Alpha Spindles as Indicators for Prolonged Brake Reaction Time During Auditory Secondary Tasks in a Real Road Driving Study

Driver distraction accounts for a substantial number of traffic accidents. Therefore, the impact of auditory secondary tasks on driving performance was examined. In addition to performance measures, i.e. reaction time on emergency brakings of a leading vehicle, mental driver states were described by electroencephalographic (EEG: alpha spindles, alpha band power) as well as cardiac activity (ECG: heart rate variability). Results show that brake reaction time (RT) increased with time-on-task during all conditions (p<.001), and was significantly higher while performing the secondary task (p<.001). Physiological measures showed similar effects. Alpha spindle rate, alpha band power as well as heart rate variability (HRV) increased with time-on-task and were significantly different during the secondary task, indicating inhibited visual information processing and reduced concentration ability. This study shows that reduced driving performance measured by means of prolonged brake reactions during increased cognitive load elicited by auditory secondary tasks is indicated by EEG measures as well as cardiac activity, enabling the direct quantification of driver distraction in experiments during real road driving

Assessing Drivers’ Vigilance State During Monotonous Driving

The differential effects of three hours of monotonous daytime driving on subjective (sleepiness, inattention, monotony), performance (choice reaction time), and physiological (EEG alpha power, P300-amplitude, heart rate) vigilance measures were examined. A linear degradation of drivers’ subjective state, mean long reaction times (as opposed to short ones), P300-amplitude and parietal alpha power with time spent on the highway was identified. An improvement of the subjective measures towards the end of the driving task was not accompanied by any improvement in performance or physiological measures. This dissociation of self-assessment and objective vigilance measures has important implications for the design of modern driver assistant systems that aim to adapt to the driver’s state

Abstracts from the 20th International Symposium on Signal Transduction at the Blood-Brain Barriers

https://deepblue.lib.umich.edu/bitstream/2027.42/138963/1/12987_2017_Article_71.pd

Confidence Interval of Single Dipole Locations Based on EEG Data

Noise in EEG and MEG measurements leads to inaccurate localizations of the sources. A confidence volume is used to describe the amount of localization error. Previous methods to estimate the confidence volume proved insufficient. Thus a new procedure was introduced and compared with previous ones. As one procedure, Monte Carlo simulations (MCS) were performed. The confidence volume was also estimated using two methods with different assumptions about a linear transfer function between source location and the distribution of the potential. One method used variable (LVM) and the other fixed dipole orientations (LFM). Finally, the confidence volume was estimated through a procedure in which there was no linearization of the transfer functions. This procedure scans the confidence volume by varying the dipole location in multiple directions. Confidence volumes were calculated for simulated distributions of the electrical potential and for experimental data including somatosensory evoked responses to stimulation of lower lip, thumb, and little finger. Results from simulated data indicated that confidence volumes calculated with the MCS method were largest, and those calculated with the LFM method were smallest. For dipole locations close to the brain surface, the confidence volume was smaller than for a central deeper source. An increase in electrode density resulted in smaller confidence volumes. When the noiose was correlated, only the method using the MCS produced acceptable results. Since the noise in experimental data is highly correlated, only the MCS method would appear to be useful in estimating the size of the confidence volume of the dipole locations. Thus, using real data with the MCS method, we easily distinguished separate and distinct representations of the thumb, little finger, and lower lip in the somatosensory cortex (SI). It was concluded that adequate estimation of confidence volumes is useful for localizing neural activity. On a practical level, this information can be used prior to an experiment for determining the conditions necessary to distinguish between different dipole sources, including the required signal to noise ratio and the minimum eletrode density

Modeling Extended Sources of Event-related Potentials Using Anatomical and Physiological Constraints

For the study of functional organization and reorganization of the human cortex by means of electromagnetic source imaging, a measure of the location and spatial extent of neural sources is of interest. This study evaluates the cortical patch method (CPM), in iterative procedure introduced by Lütkenhöner et al. (1995) that models EEG/MEG activity by means of extended cortical patches. Anatomical information is used to constrain estimates of location and extent of neural sources that generate the measured evoked potential. Whereas minimum norm approaches use mathematical constraints to solve the ambiguity of the inverse problem, the CPM introduces constraints based on anatomical and physiological knowledge about neural mass activity. In order to test the proposed method, the simulated activity in an artificial sulcus was subjected to the CPM. The results show that even activity on opposing walls of a sulcus can be well reconstructed. The simulations demonstrate the usefulness and limits of the CPM in estimating the spatial extent of neural sources in the cerebral cortex. As an example, an application of the method on experimental somatosensory evoked potentials is presented in the Appendix