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. 2019 Jan 15:12:532.
doi: 10.3389/fnhum.2018.00532. eCollection 2018.

EEG-Based Neurocognitive Metrics May Predict Simulated and On-Road Driving Performance in Older Drivers

Greg Rupp  1 , Chris Berka  1 , Amir H Meghdadi  1 , Marija Stevanović Karić  1 , Marc Casillas  1 , Stephanie Smith  1 , Theodore Rosenthal  2 , Kevin McShea  3 , Emily Sones  3 , Thomas D Marcotte  3
Affiliations

EEG-Based Neurocognitive Metrics May Predict Simulated and On-Road Driving Performance in Older Drivers

Greg Rupp et al. Front Hum Neurosci. .

Abstract

The number of older drivers is steadily increasing, and advancing age is associated with a high rate of automobile crashes and fatalities. This can be attributed to a combination of factors including decline in sensory, motor, and cognitive functions due to natural aging or neurodegenerative diseases such as HIV-Associated Neurocognitive Disorder (HAND). Current clinical assessment methods only modestly predict impaired driving. Thus, there is a need for inexpensive and scalable tools to predict on-road driving performance. In this study EEG was acquired from 39 HIV+ patients and 63 healthy participants (HP) during: 3-Choice-Vigilance Task (3CVT), a 30-min driving simulator session, and a 12-mile on-road driving evaluation. Based on driving performance, a designation of Good/Poor (simulator) and Safe/Unsafe (on-road drive) was assigned to each participant. Event-related potentials (ERPs) obtained during 3CVT showed increased amplitude of the P200 component was associated with bad driving performance both during the on-road and simulated drive. This P200 effect was consistent across the HP and HIV+ groups, particularly over the left frontal-central region. Decreased amplitude of the late positive potential (LPP) during 3CVT, particularly over the left frontal regions, was associated with bad driving performance in the simulator. These EEG ERP metrics were shown to be associated with driving performance across participants independent of HIV status. During the on-road evaluation, Unsafe drivers exhibited higher EEG alpha power compared to Safe drivers. The results of this study are 2-fold. First, they demonstrate that high-quality EEG can be inexpensively and easily acquired during simulated and on-road driving assessments. Secondly, EEG metrics acquired during a sustained attention task (3CVT) are associated with driving performance, and these metrics could potentially be used to assess whether an individual has the cognitive skills necessary for safe driving.

Keywords: EEG; HIV; driving; driving impairment test; event related potentials; neurodegeneration; on-road evaluation; sustained attention.

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Figures

Figure 1
Figure 1
STISIM M300WS console with participant (written informed consent obtained).
Figure 2
Figure 2
Example of a participant identifying correct, target circle.
Figure 3
Figure 3
The distribution of weighted scores (higher scores indicate worse performance) across all subjects who completed the simulated drive, with the red line showing the cut-off threshold of 35.
Figure 4
Figure 4
Comparison of F-measure for (A) Safe vs. Unsafe and (B) Good vs. Poor.
Figure 5
Figure 5
Secondary task performance and driving performance during the SuRT Easy, Medium, and Hard task. Participants performed worse, as indicated by all metrics except average speed, on the most difficult SuRT task.
Figure 6
Figure 6
Grand Average ERP plots (averaged across participants) for (A) Non-Target and (B) Target trials during 3CVT task plotted for Safe (blue)/Unsafe (red).
Figure 7
Figure 7
Topographical maps of (A) the average P200 component in Non-Target ERP trials (left panel) and (B) average LPP component in Target trials (right panel) plotted for all subgroups: Safe/HP, Safe/HIV+, Unsafe/HP and Unsafe/HIV+. In each panel, the difference plot between total Safe and Unsafe groups is shown on the right side. Channels with significant differences between the two groups (t-test, p < 0.05) are marked with a diamond sign.
Figure 8
Figure 8
Grand average ERP plots (averaged across participants) for (A) Non-Target and (B) Target trials during 3CVT task plotted for Good (blue)/Poor (red).
Figure 9
Figure 9
Topographical maps of (A) the average P200 component in Non-Target ERP trials (left panel) and (B) average LPP component in Target trials (right panel) plotted for all subgroups: Good/HP, Good/HIV+, Poor/HP and Poor/HIV+. In each panel the difference plot between total Good and Poor groups is shown on the right side. Channels with significant differences between the two groups (t-test, p < 0.05) are marked with a diamond shape.
Figure 10
Figure 10
Receiver operating curve (dotted line, simulator; solid line, 3CVT EEG ERP).
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