For the present work, 20 young subjects have been involved in a real driving task along urban roads, performed under different traffic conditions (rush hour and not) and going through different road types (main and secondary streets). Also, during the driving tasks specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, have been acted. During the experiments the drivers' brain activity, through EEG technique, and eye movements, through Eye-Tracking (ET) devices, have been collected. In addition, subjective measures, car parameters (e.g., position, speed, etc.) and videos around the car have been gathered. Thanks to this multimodal approach, the present study aimed at:

In conclusion, the present work will explore the potential of integrating these new methodologies, i.e., neurophysiological measures, with traditional approaches in order to enhance and extent research on drivers' behaviors and road safety.

## MATERIALS AND METHODS

## The Experimental Protocol

Twenty male students (24.9 ± 1.8 years old, licensed from 5.9 ± 1 years, with a mean annual mileage of 10350 km/year) from the University of Bologna (Italy) have been recruited and involved on a voluntary basis in this study. They were selected in order to have a homogeneous experimental group in terms of age, sex, and driving expertise. The experiment was conducted following the principles outlined in the Declaration of Helsinki of 1975, as revised in 2000. Informed consent and authorization to use the video graphical material were obtained from each subject on paper, after the explanation of the study.

Two equal cars have been used for the experiments, i.e., Fiat 500L 1.3 Mjt, with diesel engine and manual transmission. The subjects had to drive the car along a route going through urban roads at the periphery of Bologna (Italy). In particular, the route consisted in three laps of a "circuit" about 2500 m long to be covered with the daylight (**Figure 1**).

The circuit was designed with the aim to include two segments of interest, both about 1000 m long but different in term of road complexity and so supposed different also in terms of cognitive demand, thus named hereafter "Easy" and "Hard": (i) Easy was a secondary road, mainly straight, with an intersection halfway with the right-of-way, one lane and low traffic capacity, serving a residential area; (ii) Hard was a main road, mainly straight, with two roundabouts halfway, three lanes and high traffic capacity, serving a commercial area. This factor will be hereafter named "ROAD." This assumption has been made on the basis of several evidences coming from scientific literature about road safety and behavior (Harms, 1991; Verwey, 2000; Paxion et al., 2014).

Furthermore, each subject had to repeat the task two times within the same day, one time during rush and one during normal hour: this factor will be hereafter named "HOUR," while the

TABLE 1 | Data extracted from the General Plan of Urban Traffic of Bologna (Italy) referred to the traffic flow intensities in the experimental area during the day.

These data have been used to design two experimental conditions different in terms of traffic: the RUSH hours are characterized by traffic higher than during NORMAL hours.

two conditions "Rush" and "Normal." The rush hours of that specific area have been determined according to the General Plan of Urban Traffic of Bologna (PGTU, please see **Table 1**): the two "Rush hour" time-windows were from 12:30 to 13:30 (lunchtime) and from 16:30 to 17:30 (work closing time), with the experiments performed from 9.30 to 17.30, in order to ensure a homogeneous daylight condition.

Finally, during the last lap (i.e., the 3rd one) of each task repetition (i.e., Rush and Normal hour) two different events have been simulated, by involving actors, twice (i.e., along the Hard and the Easy circuit segment) along the route: a pedestrian crossing the road, and a car entering the traffic flow just ahead of the experimental subject, hereafter labeled respectively "Pedestrian" and "Car." The event types have been selected as the most probable events coherently with the urban context, as well as the safest to act, i.e., without introducing any risk for the actors, for the experimental subjects and for the traffic in general.

The **Figure 1** shows the experimental circuit along Bologna roads, highlighting the "ROAD complexity" distribution as well as the occurred events.

To summarize, each subject, after a proper experimental briefing, performed a driving task of three laps along a circuit through urban roads two times, during Rush and Normal hours. The order of Rush and Normal conditions has been randomized among the subjects, in order to avoid any order effect (Kirk, 2015). Each lap consisted in a Hard and an Easy segment, where hard and easy are referred to the road complexity and thus task difficulty. Also, despite the initial briefing, the first lap of both the tasks has been considered an "adaptation lap," while the data recorded during the second and third laps have been taken into account for the analysis. Finally, during the third lap two equal events have been simulated both along the Easy and the Hard segment (i.e., four events in total for each subject for each task, Rush and Normal).

The **Figure 2** shows a graphical representation of the experimental protocol.

During the whole protocol physiological data, in terms of brain activity through Electroencephalographic (EEG) technique and eye gazes through ET devices, and data about driving

behavior, through a professional device mounted on the car (i.e., a VBOX Pro), have been recorded. In addition, subjective measures of perceived Mental Workload have been collected from the subjects after both the tasks through the NASA Task Load Index (NASA-TLX) questionnaire (Hart and Staveland, 1988). It was possible to use Eye Tracker just with half of the subjects' sample (i.e., 10 subjects) because of device availability, so eye tracker– related data have been analyzed for 10 subjects. The following paragraphs will describe in detail the collection and processing of the aforementioned data, while the **Figure 3** shows the subject preparation and the recording setup within the car.

## The Data Collection

#### Electroencephalographic Signal Recording and Processing

The EEG signals have been recorded using the digital monitoring BEmicro system (EBNeuro, Italy). Twelve EEG channels (FPz, AF3, AF4, F3, Fz, F4, P3, P7, Pz, P4, P8, and POz), placed according to the 10–20 International System, were collected with a sampling frequency of 256 Hz, all referenced to both the earlobes, grounded to the Cz site, and with the impedances kept below 20 k. During the experiments the EEG data have been recorded without any signal conditioning, the whole processing chain has been applied offline. In particular, EEG signal has been firstly band-pass filtered with a fourth-order Butterworth filter (high-pass filter cut-off frequency: 1 Hz, low-pass filter cut-off frequency: 30 Hz). The Fpz channel has been used to remove eyes-blink contributions from each channel of the EEG signal by using the REBLINCA algorithm (Di Flumeri et al., 2016). This step is necessary because the eyes-blink contribution could affect the frequency bands correlated to the mental workload, in particular the theta EEG band. This method allows to correct EEG signal without losing data.

For other sources of artifacts (i.e., environmental noise, drivers' movements, etc.), specific procedures of the EEGLAB toolbox (Delorme and Makeig, 2004) have been employed. Firstly, the EEG signal is segmented into epochs of 2 s (Epoch length), through moving windows shifted of 0.125 s (Shift), thus with an overlap of 0.875 s between two contiguous epochs. This windowing has been chosen with the compromise to have both a high number of observations, in comparison with the number

FIGURE 3 | On the left (A), the participant preparation phase. In particular, the EEG signal has been acquired through the EEG amplifier in holter modality: the EEG

of variables, and to respect the condition of stationarity of the EEG signal (Elul, 1969). In fact, this is a necessary assumption in order to proceed with the spectral analysis of the signal. The EEG epochs with the signal amplitude exceeding ±100 µV (Threshold criterion) are marked as "artifact." Then, each EEG epoch has been interpolated in order to check the slope of the trend within the considered epoch (Trend estimation). If such a slope is higher than 10 µV/s, the considered epoch is marked as "artifact." Finally, the signal sample-to-sample difference (Sample-to-sample criterion) has been analyzed: if such a difference, in terms of absolute amplitude, is higher than 25 µV, i.e., an abrupt variation (no-physiological) happened, the EEG epoch is marked as "artifact." At the end, the EEG epochs marked as "artifact" have been removed from the EEG dataset with the aim to have a clean EEG signal to perform the analyses.

From the clean EEG dataset, the Power Spectral Density (PSD) has been calculated for each EEG channel for each epoch using a Hanning window of the same length of the considered epoch (2 s length, that means 0.5 Hz of frequency resolution). Then, the EEG frequency bands of interest has been defined for each subject by the estimation of the Individual Alpha Frequency (IAF) value (Klimesch, 1999). In order to have a precise estimation of the alpha peak and, hence of the IAF, the subjects were been asked to keep the eyes closed for a minute before starting the experimental tasks. Finally, a spectral features matrix (EEG channels × Frequency bins) has been obtained in the frequency bands directly correlated to the mental workload. In particular, only the theta band [IAF – 6 ÷ IAF – 2], over the EEG frontal channels, and the alpha band [IAF – 2 ÷ IAF + 2], over the EEG parietal channels, were considered as variables for the mental workload evaluation (Gevins and Smith, 2003; Aricò et al., 2016b; Borghini et al., 2017a).

At this point the automatic-stop-StepWise Linear Discriminant Analysis (asSWLDA), a specific Machine-Learning algorithm (basically an upgrade version of the well-known StepWise Linear Discriminant Analysis) previously developed (Aricò et al., 2016b), patented (Aricò et al., 2017a) and applied in different applications (Aricò et al., 2016a; Borghini et al., 2017b,c) by the authors has been employed. On the basis of the calibration dataset, the asSWLDA is able to find the most relevant spectral features to discriminate the Mental Workload of the subjects during the different experimental conditions (i.e., EASY = 0 and HARD = 1). Once identified such spectral features, the asSWLDA assigns to each feature specific weights (wi train), plus a bias (btrain), such that an eventual discriminant function computed on the training dataset [ytrain(t)] would take the value 1 in the hardest condition and 0 in the easiest one. This step represents the calibration, or "Training phase" of the classifier. Later on, the weights and the bias determined during the training phase are used to calculate the Linear Discriminant function [ytest(t)] over the testing dataset (Testing phase), that should be comprised between 0 (if the condition is Easy) and 1 (if the condition is Hard). Finally, a moving average of 8 s (8MA) is applied to the ytest(t) function in order to smooth it out by reducing the variance of the measure: its output is defined as the EEG-based Workload index (WLSCORE). For the present work, the training data consisted in the Easy segment of the 2nd lap during the Normal condition and the Hard segment of the 2nd lap during the Rush condition (they have been hypothesized the two conditions characterized by respectively the lowest and highest mental workload demand), while the testing data consisted of the data of the 3rd lap of both the conditions.

Here below the training asSWLDA discriminant function (Equation 1, where fi train(t) represents the PSD matrix of the training dataset for the data window of the time sample t, and of the i th feature), the testing one (Equation 2, where fi test(t) is as fi train(t) but related to the testing dataset) and the equation of the EEG-based workload index computed with a time-resolution of 8 s (WLSCORE, Equation 3), are reported.

$$y\_{train}(t) = \sum\_{i} w\_{i\,train} \cdot f\_{i\,train}(t) + b\_{train} \tag{1}$$

$$y\_{test}(t) = \sum\_{i} w\_{i\,train} \cdot f\_{i\,test}(t) + b\_{train} \tag{2}$$

$$WL\_{SCORE} = 8MA(\mathcal{y}\_{test}(t))\tag{3}$$

#### Eye-Tracking Data and Its Processing

Eye movements of the participants have been recorded through an ASL Mobile Eye-XG device (EST GmbH, Germany), a system based on lightweight eyeglasses equipped with two digital highresolution cameras. One camera recorded the scene image and the other the participant's eye, that is monitored through infrared rays. The data were recorded with a sampling rate of 30 Hz (i.e., 33 ms time resolution), and a spatial resolution of 0.5 ÷ 1 ◦ . ASL software was used to analyze the data, obtaining information about the drivers' fixation points frame by frame (33 ms). A preliminary calibration procedure was carried out for each subject inside the car before starting driving, asking them to fix their gaze on thirty fixed visual points spread across the whole scene, in order to get a good accuracy of the eyemovement recorder. The gazes recorded during the driving task were manually analyzed, in order to group them into three different categories: road infrastructure, traffic vehicles, and external environment. For each subject, each lap (second and third), and each condition (Easy and Hard ROAD, Rush and Normal HOUR) the distribution of eye fixations between the three categories was calculated in terms of percentage of the total.

#### Additional Measures

Each car has been equipped with a Video VBOX Pro (Racelogic Ltd., United Kingdom), a system able to continuously monitor the cinematic parameters of the car, integrated with GPS data and videos coming from up to four high-resolution cameras. The system has been fixed within the car, at the center of the floor of the back seats, in order to put it as close as possible to the car barycenter, while two cameras have been fixed over the top of the car. The system recorded car parameters (e.g., speed, acceleration, position, etc.) with a sampling rate of 10 Hz. For the purpose of the present study, the average speed for each task has been computed. Also, the cameras' videos have been used to count the number of vehicles encountered by the driver during each task.

Also, at the end of each task (thus only the HOUR condition, i.e., Rush vs. Normal, can be compared) the subjects had to evaluate the experienced workload by filling the NASA-TLX questionnaire (Hart and Staveland, 1988). In particular, the subject had (i) to assess, on a scale from 0 to 100, the impact of six different factors (i.e., Mental demand, Physical demand, Temporal demand, Performance, Effort, Frustration), and (ii) to assess the more impacting factor through 15 comparisons between couple of the previously evaluated factors. The result of this questionnaire is a score from 0 to 100 corresponding to the driver's mental workload perception.

### Performed Analyses

#### Validation of Experimental Design Assumptions

The first analysis aimed to validate the assumptions in terms of experimental design, that is:

In order to validate the first assumption, the number of vehicles encountered by the experimental subjects and the average driving speed during the two conditions have been computed and statistically compared. It is expected that the number of vehicles is significantly higher and the average speed significantly lower during rush hours (Bucchi et al., 2012).

The second assumption has been validated by investigating the percentage of fixations over the external environment, since such indicator has been proven to be inversely correlated with mental workload: the more the experienced workload is, the less the number of fixations over the external environment is, since the driver gaze will mostly focus on infrastructure and vehicles (Costa et al., 2014; de Winter et al., 2014; Lantieri et al., 2015). Also, we verified the difference in terms of mental workload from a neurophysiological point of view: we computed the ratio between Theta rhythms over frontal sites ("ThetaF") and Alpha rhythms over parietal sites ("AlphaP"), since it is considered a well-established metric of mental workload (Borghini et al., 2014). In particular, The ThetaF/AlphaP has been proven to increase if the mental workload experienced by the user is increasing as well (Gevins and Smith, 2003; Holm et al., 2009; Borghini et al., 2015). The metric has been computed as the ratio between the averaged PSD values in theta band over the frontal electrodes (AF3, AF4, F3, Fz, F4) and the averaged PSD values in alpha band over the parietal electrodes (P3, P7, Pz, P4, P8, POz). Both the analysis have been performed comparing the two conditions employed to train the classifier (please see Electroencephalographic Signal Recording and Processing), i.e., the Easy segment of the 2nd lap during the Normal condition and the Hard segment of the 2nd lap during the Rush condition, assumed as the two conditions characterized by respectively the lowest and highest mental workload demand.

All the statistical comparisons have been performed through two-sided Wilcoxon signed rank tests. In fact, data come from multiple observations on the same subjects, but it is not possible to assume or robustly assess (the number of observations is always equal or less than 16) that the observations distribution is Gaussian, therefore paired non-parametric tests have been used (Siegel, 1956).

#### Classification Performance

Firstly, a synthetic analysis of the brain features selected by the algorithm has been performed in order to evaluate any eventual recurrence of a specific feature. The initial features domain for each subject consisted in a matrix of 187 features (11 EEG channels <sup>∗</sup> 17 bins of frequency – from IAF-6 Hz to IAF+2 Hz with a resolution of 0.5 Hz –). Actually, only 99 of these features can be selected by the algorithm because of the Regions of Interest defined a priori: 45 features related to frontal Theta and 54 related to parietal Alpha.

Then, in order to investigate the algorithm (i.e., the asSWLDA) classification accuracy, the analysis of the Area Under Curve (AUC) of the Receiver Operator Characteristic (ROC) curve of the classifier has been performed (Bamber, 1975). In particular, AUC represents a widely used methodology to test the performance of a binary classifier: the classification performance can be considered good with an AUC higher than at least 0.7 (Fawcett, 2006). In this case there are actually two classes in terms of mental workload, i.e., Easy and Hard, related to the two different difficulty levels characterizing the circuit. As previously described, for each subject the training dataset consisted in the Easy segment of the 2nd lap during the Normal condition and the Hard segment of the 2nd lap during the Rush condition (they have been hypothesized the two conditions characterized by respectively the lowest and highest cognitive demand), while the testing dataset consisted of the data of the 3rd lap of both the conditions (Real data). Therefore, the classifier has been tested shuffling the testing dataset related labels (Random), in order to verify that classifier performance on measured data (Real data) was significantly higher than that one obtained on random data (Random), independently from the traffic intensity (i.e., both in Rush and Normal hour conditions). In both the cases (Real and Random), the time resolution of WLscores is equal to 8 s, obtained as the best compromise between a high time resolution and good classification performance. Three two-sided Wilcoxon signed rank tests have been performed between Real and Random data, one for each HOUR condition (i.e., comparison Real vs. Random in Normal and Rush hour) and one comparing the Normal and Rush conditions only in terms of real data. The results of these multiple comparisons have been validated by applying the False Discovery Rate (FDR) correction (Benjamini and Hochberg, 1995).

#### Workload Assessment

Once demonstrated the reliability of the classification algorithm to obtain the EEG-based index of mental workload in the specific driving scenarios, the workload scores (WL score) have been used to evaluate the impact of different factors, that is the road

complexity and the traffic as well as specific events along the driving experience. Depending on the analysis, the EEG-based WL scores have been analyzed in relation to ET and subjective data.

#### **Evaluation of traffic and road complexity impact**

The WL indexes obtained with a time resolution of 8 s from the testing dataset (i.e., the third lap) were averaged for each subject and for each condition (i.e., HOUR and ROAD). A Friedman test, the non-parametric version of the repeated measures ANOVA (Analysis of Variance), has been performed in order to investigate any possible effect due to traffic and road complexity on the workload perceived by the subject. Furthermore, since post hoc tests specifically designed for Friedman test do not exist but both the factors have been measured on the same subjects, two Wilcoxon signed rank tests have been performed in order to investigate potential within effects among the two factors, i.e., HOUR and ROAD.

Also, the results in terms of workload indexes have been compared with those obtained from ET in order to evaluate the different sensitivity to the phenomenon (i.e., mental workload variations) of the two technologies. In terms of ET measures, it has been investigated the percentage of fixations on the road infrastructure and vehicles, since such indicator has been proven to be directly correlated with mental workload while driving: the more the experienced workload is, the more the number of fixations over the road will be, since the driver gaze will mostly focus on infrastructure and vehicles (Costa et al., 2014; de Winter et al., 2014). Multiple two-sided Wilcoxon signed rank tests have been performed in order to reveal any difference with respect to the two investigated factors.

Furthermore, a two-sided Wilcoxon signed rank test has been performed on the NASA-TLX measures. Please note that for the continuity of the experiment the questionnaires were filled by the subjects only after the tasks end, therefore only the comparison between Normal and Rush hour has been possible (please refer to Section "Additional Measures").

#### **Evaluation of single events impact**

On the basis of the average duration of the events among the subjects during the driving experience, and to homogenize the measures with respect of this parameter (i.e., event duration), a fixed window of 20 s for the car event (from the first fixation of the car to its overtaking) and of 10 s for the pedestrian event (from the first fixation of the pedestrian to the acceleration after its road crossing) has been defined, independently from the traffic and the road complexity. Remembering that the events were acted only during the third lap of each task repetition, similar windows corresponding to the same circuit position were defined during the second lap in order to compare the event's happening vs. no-happening. The WL indexes were averaged for each subject, for each condition (i.e., HOUR and ROAD) and for each event. Multiple two-sided Wilcoxon signed rank tests have been performed in order to reveal any difference (i) with respect to the events' happening, and (ii) among the events types.

#### RESULTS

The following results are referred to a sample of 16 subjects (8 with Eye Tracking), since one subject has been discarded because of technical issues on the EEG data, while three subjects have been discarded because of no objective difference in terms of encountered vehicles (measured through the VBOX cameras) between the two tasks, i.e., during Rush and Normal hours.

#### Experimental Design Validation

**Figure 4** shows the results of the comparisons between (a) the number of vehicles encountered by the experimental subjects and (b) the average driving speed during the two different traffic conditions, i.e., during Normal and Rush hours. The performed statistical analysis revealed a significant increasing (p = 0.001) of vehicles encountered by the experimental subjects and a significant decreasing (p = 0.039) of driving average speed from

FIGURE 4 | On the left (A), a bar graph representing the mean and the standard deviation of vehicles encountered by the participants during the experiments. The Wilcoxon test showed a significantly higher (p = 0.001) number of vehicles during rush hour. On the right (B), a bar graph representing the mean and the standard deviation of participants driving speed during the experiments. The Wilcoxon test showed a significantly lower (p = 0.039) speed during rush hour. The statistical tests showing a significant effect.

Normal to Rush hours, validating the experimental hypothesis about the two different conditions of traffic made a priori on the basis of the General Plan of Urban Traffic of Bologna (see The Experimental Protocol).

**Figure 5** shows the results in terms of percentage of fixations over the external environment between the Easy and Hard segments of the circuit, since such indicator has been proven to be inversely correlated with mental workload. The performed statistical analysis revealed a significant decreasing (p = 0.046) of driver gazes over the external environment, validating the experimental hypothesis about the two different conditions of difficulty made a priori on the basis of scientific literature (see The Experimental Protocol).

**Figure 6** shows the results in terms of ThetaF/AlphaP value between the Easy and Hard segments of the circuit, since

such ratio has been proven to be a physiological indicator directly correlated to mental workload. The performed statistical analysis revealed a significant increasing (p = 0.009) of the proposed index, validating the assumption about the different cognitive demand related to the two conditions, made a priori on the basis of scientific literature (see The experimental Protocol).

#### Classification Performance

**Figure 7** shows the distribution of the features, and the relative frequency of selection, chosen by the asSWLDA during the training phase. The analysis of features selected by the algorithm revealed that the asSWLDA selected on average 4 features per subject, coming from 3 of the 11 channels available. The frequency bins, actually equal to 17 because included between IAF-6 Hz and IAF+2 Hz with a resolution of 0.5 Hz, have been grouped into four areas of interest: Lower Theta [IAF – 6 ÷ IAF – 4], Upper Theta [IAF – 4 ÷ IAF – 2], Lower Alpha [IAF – 2 ÷ IAF] and Upper Alpha [IAF ÷ IAF + 2]. The results show that Lower Theta over F4 and Upper Alpha over POz have been used for more than the 50% of subjects.

The AUC analysis (**Figure 8**) revealed that, by using such approach, it has been possible to achieve mean AUC values of 0.744 ± 0.13 for the Normal hour and of 0.727 ± 0.06 for the Rush hour. In particular, the two Wilcoxon tests demonstrated that the classifier performance on the Real data was significantly higher than on Random data in both the conditions (respectively p = 0.01 and p = 0.0005). Also, there were no significant differences (p = 0.64) in terms of AUC values on Real data between Normal and Rush hours, in other words the classification performance was not dependent on the traffic condition. Because of the three repeated tests, the False Discovery Rate correction has been performed: with respect to the p-values obtained and ordered (0.0005, 0.01, and 0.64), the three corrected q-values are respectively 0.0015, 0.015, and 0.64, thus the first two results are still significant.

#### Workload Assessment

#### Evaluation of Traffic and Road Complexity Impact

**Figure 9** shows the results of the non-parametrical statistical analysis in terms of effects of the two investigated factors, i.e., the traffic (HOUR) and the road complexity (ROAD), on the mental workload experienced by the drivers. In particular, the Friedman test at the top of **Figure 9A** highlights a significant main effect (p = 0.00001) among the different factors: the mental workload significantly increased because of the higher road complexity (i.e., from Easy to Hard), and even more because of the higher traffic intensity (i.e., from Normal to Rush hours). The Wilcoxon tests performed in order to investigate any within effect showed two significant main effects in term of workload increasing if both complexity [bottom left (**Figure 9B**), ROAD, p = 0.0038] and traffic [bottom right (**Figure 9C**), HOUR, p = 0.0032] increase.

**Figures 10**, **11** show the results of the Wilcoxon tests comparing the sensitivity of ET measures with respect to EEGbased ones. For these analyses the EEG-based WL scores of only the subjects wearing also the Eye Tracker (eight of sixteen) have been considered, in order to make the results comparable (i.e.,

both the measures have been collected during same experience). In particular:

Finally, **Figure 12** shows the results in terms of NASA-TLX scores, revealing that there is not any significant difference in terms of workload subjectively assessed between the Normal and Rush hour conditions.

#### Evaluation of Single Events Impact

**Figure 13** shows the results in terms of EEG-based WL scores about how the presence of a specific event impacts the mental workload of the driver, with respect to the different experimental conditions. In terms of external events (the condition EVENT is referred to the event actually happened during the 3rd lap, the condition NO EVENT is referred to the same circuit portion during the 2nd lap when no events were acted), the pedestrian crossing the road induced a significantly higher workload only during the Normal hour along the Hard circuit segment (Wilcoxon test's p = 0.037), while the car induced a significantly higher workload along both the Easy and Hard circuit segments but only during Normal hour (respectively Wilcoxon test's p = 0.007 and p = 0.008).

Considering only the condition "EVENT," despite a decreasing trend from Easy to Hard segments, no significant differences (p > 0.05) have been found for each event during the same traffic

condition (HOUR). However, if considering the same difficulty level (ROAD), all the events induced a significant workload increasing during Rush hours, except the pedestrian along the Easy segment (Pedestrian Hard: p = 0.009; Car Easy: p = 0.023; Car Hard: p = 0.002).

## DISCUSSION

Since the impact of drivers' errors in terms of human lives and costs is very high and the next future previsions are even worse (World Health Organization, 2015), the relationship between human errors and driving performance impairment due to a high mental workload has been deeply investigated in the automotive domain. Recent technological advancements as well as the growth of disciplines such as Neuroscience and Neuroergonomics now allow to record human neurophysiological signals, such as in this study brain activity through Electroencephalographic technique, in a robust way also outside the laboratory, and to obtain from them objective neurometrics of human mental states (i.e., workload) (Aricò et al., 2017c, 2018). The present work aimed to validate a machine-learning approach, i.e., the asSWLDA (Aricò et al., 2016a), for the objective assessment of human mental workload while driving in real settings, as well as its integration with traditional tools (e.g., questionnaires, car parameters, eye tracking) in order to evaluate the impact of different factors (road complexity, traffic intensity, external events), thus suggesting new innovative tools for enhancing research in road safety. In order to achieve these objectives, 20 young subjects have been involved in a real driving task along urban roads, performed under different traffic conditions (rush hour and not), driving through different road types (main and secondary streets) and facing to external events.

Firstly, the experiments have been designed making two a priori assumptions:

The statistical analysis performed on the average speed of the experimental subjects and the number of vehicles encountered during the experiments (**Figure 4**) validated the Assumption 1: in fact, the subjects encountered a significantly higher (p = 0.001) number of vehicles and they drove at a significantly lower (p = 0.039) speed during the rush hours, as expected from scientific literature (Bucchi et al., 2012). Statistical analysis of driver's eye fixations over the external environment (**Figure 5**) and physiological brain patterns (**Figure 6**) validated the Assumption 2: in fact the drivers' gazes over the external environment (such index inversely correlates with mental workload; de Winter et al., 2014; Lantieri et al., 2015) have been significantly lower (p = 0.046) along the circuit segment that was hypothesized as Hard, while the ratio between frontal theta and parietal alpha rhythms significantly increased (p = 0.009). These results confirmed the properness of the experimental design. Nevertheless, the analysis of encountered vehicles, determined

FIGURE 9 | At the top (A), the Friedman test highlighting a significant main effect (p = 0.00001), in terms of mental workload increasing among the different factors. At the bottom, on the left (B) the Wilcoxon test on the factor ROAD and on the right (C) the same test on the factor HOUR, showing how both the factors produced a significant mental workload increasing (respectively p = 0.004 and p = 0.003). The statistical tests showing a significant effect.

FIGURE 10 | The Wilcoxon tests performed to investigate eventual sensitivity differences between Eye-Tracking [left (A)] and EEG [right (B)] measures, considered on the same subjects, in relation to ROAD complexity showed that EEG-based measures have been able to significantly discriminate (p = 0.008) the two conditions at least during Normal hour, while the ET-based ones have not been able to show any significant difference both during Normal and Rush hours. The statistical tests showing a significant effect.

FIGURE 11 | The Wilcoxon tests performed to investigate eventual sensitivity differences between Eye-Tracking [left (A)] and EEG [right, (B)] measures, considered on the same subjects, in relation to traffic intensity (i.e., HOUR) showed that, while the EEG-based measures have been able to significantly discriminate the two conditions both along Easy (p = 0.019) and Hard (p = 0.04) segments, the ET-based ones have been able to significantly discriminate Normal and Rush hours only along the Hard segment (p = 0.02). The statistical tests showing a significant effect.

through videos from the VBOX videos, led to discard three subjects because of no differences between rush and normal hours (see Results). Therefore, this validation approach should be taken into account for future works in real driving conditions, where external conditions and events are less controllable, even unpredictable, if compared with laboratory experiments.

Once validated the experiment in terms of differences between the road and traffic conditions, the EEG-based Workload measures have been validated. In particular, the analysis of AUC related to the asSWLDA-based classifier demonstrated that the adopted approach achieves considerable performance, i.e., AUCs > 0.7 (Fawcett, 2006). More in detail, the AUC analysis (**Figure 8**) revealed that it has been possible to achieve mean AUC values of 0.74 for the Normal hour and of 0.73 for the Rush hour, significantly higher than a random classification in both the conditions (respectively p = 0.01 and p = 0.0005). Also, there were no significant differences (p = 0.64) in terms of AUC values on Real data between Normal and Rush hours. All the previous results have been also confirmed by the correction for multiple comparisons, in this case the False Discovery Rate. It is also true that, within the machine-learning theory, AUCs greater than 0.7 are considered remarkable if compared with a random distribution that is assumed to produce AUCs equal to 0.5. In the present study, the performance of the classifier on randomized data achieved AUCs values of about 0.6. A possible explanation could be that the random value would be closer and closer to 0.5 only if the number of repetitions tends to infinite, however, this result undoubtedly encourages research about improving the proposed method. Of course, classification performance of about 0.75 are anyway remarkable, in particular because of the novelty of such application (the EEG-based Workload index is provided with a time resolution equal to 8 s) and the real settings, where mental states assessment is more prone to misclassification: in fact, it is plausible to assume that outside the high controlled laboratory settings, the user experiences more complex mental states that consist of multiple different components having the potential to influence neurophysiological signals used to infer a specific state.

The analysis of the patterns of features selected by the algorithm during its training phase (**Figure 7**) provided interesting insights about its usability: in fact, the asSWLDA selected on average 4 discriminant features for each subject, and even more interesting, by involving 3 of the 11 available channels. It means that, once calibrated the system on a specific user, it would be able to work online during the driving experience involving only three EEG channels, in other words reducing significantly its invasiveness and increasing wearability, two critical aspects for applications outside the laboratory.

At this point, the asSWLDA output, in terms of EEG-based Workload index, has been used to evaluate the effects of road complexity, traffic intensity and external events on drivers' workload (**Figure 9**).

The Friedman ANOVA test (please see Figure) shows the results in terms of effects of the two investigated factors, i.e., the traffic (HOUR) and the road complexity (ROAD), on the mental workload experienced by the drivers: both the

traffic and road complexity contributed to significantly increase (main effect: p = 0.00001; Wilcoxon tests respectively: HOUR, p = 0.0032; ROAD: p = 0.0038) the mental workload. In other words, the drivers' workload increased if traffic increased as well independently from the road complexity. At the same time, the drivers' workload increased while driving along more complex roads independently from the traffic intensity. These results have to be considered with respect to the experimental task: actually, the Hard segment was a three-lanes main street, that with respect to a one-lane main street (Easy segment) implies several additional decisions and actions, such as eventual car overtaking as well as looking at rear-view mirrors because of possible cars coming on lateral lanes. Of course, these actions increase with traffic increasing, because of the higher number of vehicles along the circuit (as demonstrated by Video analysis, please see **Figure 4**). Apparently, the Easy segment should not suffer traffic increasing, since being a one-lane segment the overtaking are very limited and drivers have not to frequently check rear-view mirrors since they cannot change lane. Nevertheless, because of the higher number of vehicles along the circuit during rush hours, the drivers had to continuously monitor eventual preceding cars, adapting safety distance and speed (in fact average speed during rush hour has been lower and drivers' gazes on infrastructure and vehicles higher also along Easy segment). These actions also induced a no-negligible workload increasing, giving a possible justification of the high accident rate along rural roads (Shankar et al., 1995), that are generally considered "Easy to drive" if compared with urban main roads (Harms, 1991; Paxion et al., 2014), thus mismatching the driver's expectations.

Very interestingly but not surprisingly, the neurophysiological measures showed a significantly higher sensitivity with respect to the ET ones (**Figures 10**, **11**) in discriminating the different impact of road complexity and traffic intensity on mental workload. It is important to consider that ET measures were available only for a reduced group of the experimental sample (8 of 16 subjects), therefore it could have affected the performance of such measures in discriminating the mental workload related to different factors. However, the paired statistical analysis highlighted that on the same subjects, EEG-based measures were more sensitive to workload fluctuations. Their high sensitivity has been pointed out also with respect to subjective measures (i.e., NASA-TLX questionnaires, **Figure 12**), that on the contrary were not able to discriminate (p = 0.23) normal from rush hours.

Finally, EEG-based workload measures revealed a significant workload increasing (p < 0.05) related to both the investigated events, that is the car and the pedestrian crossing the road, especially in normal hours independently from the road complexity. Instead, no significant workload increasing were associated to the event during rush hours, despite a significantly higher workload in comparison with the same events during normal hours (**Figure 13**). Although for this analysis neurophysiological measures are not integrated with additional ones (it was impossible to collect subjective data related to specific events, while from the ET point of view it was possible to assess only if the event was been perceived or not), it is possible to deduce that external events could lead to eventually risky situations especially with low traffic (normal hours). In fact, although a lower absolute workload if compared with high traffic

FIGURE 13 | The bar graphs show the mean values and the standard deviation of the EEG-based WL scores related to the different events along the various experimental conditions. In particular, the results are divided per events category, i.e., Pedestrian on the left (A) and Car on the right (B). In both the cases, the condition EVENT (solid color) is referred to the event actually happened during the 3rd lap, the condition NO EVENT (lines pattern) is referred to the same circuit portion during the 2nd lap when no events were acted. The Wilcoxon tests revealed a significant workload increasing (one red asterisk stands for p < 0.05; two red asterisks stand for p < 0.01) related to both the investigated events, that is the car and the pedestrian crossing the road, especially in normal hours independently from the road complexity. Instead, no significant workload increasing were associated to the event during rush hours, despite a significantly higher workload in comparison with the same events during normal hours.

condition, they are characterized by an immediate cognitive demand increase, that could become dangerous if not expected by the driver.

Nevertheless, the main limit that affects the present study is the algorithm calibration with data coming from the task itself and recorded in very similar conditions. From one side, it could be argued that in everyday life context such a calibration would be unfeasible; from the other side it could be argued that the proposed algorithm is not classifying the targeted mental state, i.e., mental workload, but only two conditions that are very similar. Regarding the calibration, actually it is one of the main still open issues in transferring machine learning approaches from research to applied field: several solutions have been explored, such as cross-task calibration or employment of unsupervised algorithms, but the problem is still open and needs further investigation (Aricò et al., 2018). However, the present work did not aim at addressing such issue, but at investigating the possibility of applying a machine-learning algorithm for the mental workload evaluation, already validated in other domains, also in automotive applications. The highly challenging conditions of a "real driving experiment" with twenty subjects, jointly with the employment of high-quality instrumentation, already make the present work very innovative and of interest. Secondly, it is true that the algorithm has been calibrated on two conditions and employed in classifying two similar conditions, but it is also important to consider that calibration data for each subject came from two different repetitions (please refer to Section "Electroencephalographic Signal Recording and Processing" for more information): in fact data recorded during the Easy segment of Normal hour (2nd lap) have been used as EASY CLASS, while data recorded during the Hard segment of Rush hour (2nd lap) have been used as HARD CLASS. Even if assuming that Easy segment of Normal hour and Hard segment of Rush hour of 2nd and 3rd lap were intrinsically similar, no data from Hard segment of Normal hour and Easy segment of Rush hour have been used to train the classifier, therefore their coherent classification (e.g., Hard segment of Normal hour is not easier than the Easy segment during the same hour) is a mere and appreciable result of the proposed algorithm. Undoubtedly, mental workload is a Human Factor concept hard to define and even worse to measure (Moray, 2013), and confounds arising from different mental states are probably present, however, the results of the present study are already remarkable, especially if considering previous results obtained by the employment of the same algorithm in different applications (Aricò et al., 2016a,b, Borghini et al., 2017b,c).

It is important to remark how it is possible to achieve this kind of results only thanks to the proposed methodology: in fact, subjective measures cannot be gathered with high time resolution and without interfering with the main task, briefing and debriefing sessions can be performed only before and after the experience, while eye-tracker as well as other neurophysiological metrics (for example the ThetaF/AlphaP showed in **Figure 6**) are able to provide only an overall evaluation about a "long" condition. On the contrary, the proposed methodology is able to overcome these limitations, providing workload assessment with high time resolution (i.e., in this case 8 s) and thus allowing to evaluate also specific events.

In conclusion, the obtained results appear very interesting in terms of understanding driver's behaviors and its relationship with road environment, highlighting the added value of neurophysiological measures in providing insights about human mind that are not obtainable, or at least difficult to obtain, with traditional approaches. Certainly, further analyses are necessary in order to validate this multimodal approach with a larger sample of subjects, exploring the impact of other factors, such as different events, road signage and so on, and involving additional tools typical of road safety research, as well as exploring the possibility of calibrating the proposed algorithm without any task-related data.

## CONCLUSION

The present study, through a real driving experiment, aimed to validate a methodology able to infer driver's mental workload on the basis of his/her brain activity through Electroencephalographic technique. Once validated, such methodology has been successfully employed to evaluate the impact of different factors, specifically the road complexity, the traffic intensity (depending on the hour of the day), and two specific events (a pedestrian crossing the road and a car entering in the traffic flow), on the drivers' experienced mental workload. The analyses have been supported by information coming from subjective measures, drivers' eye movements tracking and car parameters. The results demonstrated (i) the reliability and effectiveness of the proposed methodology based on human EEG signals to objectively measure driver's mental workload with respect to different road factors, and (ii) the added value of neurophysiological measures in providing insights about human mind while dealing with tasks that are difficult or even impossible to obtain by using traditional approaches. In conclusion, other than the specific obtained results, the present work breaks new ground for the integration of these new methodologies, i.e., neurophysiological measures, with traditional approaches in order to enhance and extend research on drivers' behaviors and road safety.

### ETHICS STATEMENT

This study was carried out in accordance with the recommendations of the Good Clinical Practice (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the 'University of Bologna.'

## AUTHOR CONTRIBUTIONS

GDF is the main author of the paper. Also, he was actively involved within the experiments as well as in the EEG data

analysis and interpretation. PA, GB, and NS supported the experimental design, the data recording, the EEG data analysis, and the manuscript writing. PL and SP supported the experimental design and the results interpretation, providing their contribute in particular about the Human Factor concepts. VV, CL, AB, and AS were in charge of experiments planning, they contributed actively to the experiments execution, they analyzed Eye Tracking and subjective measures analysis, and supported the results interpretation. FB coordinated the research group, from the experimental design to the manuscript editing.

#### REFERENCES

## ACKNOWLEDGMENTS

The authors are grateful to the Unipol Group Spa and, in particular, ALFAEVOLUTION TECHNOLOGY, for the considerable help given in the research study. This work has been also co-financed by the European Commission through the Horizon2020 project H2020-MG-2016 Simulator of behavioral aspects for safer transport, "SimuSafe," (GA n. 723386), and the project "BrainSafeDrive: A Technology to Detect Mental States During Drive for Improving the Safety of the Road" (Italy-Sweden collaboration) with a grant of Ministero dell'Istruzione dell'Università e della Ricerca della Repubblica Italiana.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Di Flumeri, Borghini, Aricò, Sciaraffa, Lanzi, Pozzi, Vignali, Lantieri, Bichicchi, Simone and Babiloni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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

Greg Rupp<sup>1</sup> \*, Chris Berka<sup>1</sup> , Amir H. Meghdadi <sup>1</sup> , Marija Stevanovic Kari ´ c´ 1 , Marc Casillas <sup>1</sup> , Stephanie Smith<sup>1</sup> , Theodore Rosenthal <sup>2</sup> , Kevin McShea<sup>3</sup> , Emily Sones <sup>3</sup> and Thomas D. Marcotte<sup>3</sup>

*<sup>1</sup> Advanced Brain Monitoring Inc., Carlsbad, CA, United States, <sup>2</sup> Systems Technology, Inc., Hawthorne, CA, United States, <sup>3</sup> Department of Psychiatry, University of California, San Diego, San Diego, CA, United States*

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, event related potentials, sustained attention, driving, HIV, neurodegeneration, driving impairment test, on-road evaluation

## INTRODUCTION

Driving is an essential aspect of maintaining health, independence and quality of life as individuals age (Ball et al., 1998). Those who voluntarily avoid driving due to perceived age-related sensory or cognitive deficits often suffer substantial consequences such as decreased mobility, increased dependency, social isolation, depression, and higher incidence of nursing home placement

#### Edited by:

*Karel Brookhuis, University of Groningen, Netherlands*

#### Reviewed by:

*Jodi M. Gilman, Massachusetts General Hospital, Harvard Medical School, United States Berry Wijers, University of Groningen, Netherlands*

> \*Correspondence: *Greg Rupp<EMAIL_ADDRESS>
Received: *15 June 2018* Accepted: *17 December 2018* Published: *15 January 2019*

#### Citation:

*Rupp G, Berka C, Meghdadi AH, Karic MS, Casillas M, Smith S, ´ Rosenthal T, McShea K, Sones E and Marcotte TD (2019) EEG-Based Neurocognitive Metrics May Predict Simulated and On-Road Driving Performance in Older Drivers. Front. Hum.  Driving requires a myriad of cognitive functions including attention, visuospatial processing, psychomotor integration, adequate processing speed, and executive function (Kellison, 2009). Normal aging, in the absence of any neurological or psychiatric disease, can lead to declines in these cognitive abilities increasing the risk for an automobile collision (Brayne et al., 2000; Ball, 2009). However, the aging process and its effects on driving performance vary significantly between individuals (Ball, 2009). It has been suggested that specific age-related functional impairments, and not age itself, put one at risk for impaired driving (Ross et al., 2009). Overall, older drivers as a group incur the highest number of fatalities per mile driven compared to other age groups (although the physical frailty of older individuals contributes significantly to this mortality rate) (Tefft, 2017). In addition to normal aging, functional deficits associated with neurodegenerative diseases (NDDs) such as Alzheimer's (AD), Mild Cognitive Impairment (MCI), or HIV-associated neurocognitive disorders (HAND) may affect driving performance. While NDD patients are more likely to be at-risk drivers, research suggests that memory deficits alone may not necessarily lead to unsafe driving (Carr et al., 1998; Marcotte et al., 1999, 2004; Silverstein et al., 2002; Charlton et al., 2003; Duchek et al., 2003; Uc et al., 2004, 2005; Man-Son-Hing et al., 2007; Frittelli et al., 2009; Wadley et al., 2009; Kawano et al., 2012). Cognitive impairments that affect driving, such as visuospatial processing deficits often found in patients with MCI or HAND, may be subclinical and unobserved by the patient themselves or their friends and family (Cysique et al., 2009; Chiao et al., 2013). Therefore, driving impairment cannot be established using only age and/or a NDD diagnosis.

In the United States, legal requirements for elderly drivers vary greatly from state to state. Some states have no safetyrelated policies for older drivers, whereas other states may have limited requirements for elderly individuals. For example, license renewal in California for those over the age of 70 may require a vision and/or written test, and in rare cases an on-road evaluation is administered (Department of Motor Vehicles, 2018a). Other states like Connecticut and Delaware have no age-related safety policies in place. Driver's licenses in these states need to be renewed every 6–8 years for all drivers regardless of age, often with no functional assessment required (Department of Motor Vehicles, 2018b,c).

Physicians have a responsibility to identify patients of all ages that might be considered at-risk drivers. However, they are often reluctant to take action due to privacy concerns and/or the severe impact their intervention could have on the patient's quality of life that results from the loss of a driver's license. Currently, there is no definitive diagnostic test for physicians to administer that identifies at-risk drivers, but individuals deemed potentially high risk may be referred for neuropsychological testing. The relationships between on-road driving performance and standard neuropsychological tests are modest, particularly in patients with mild to moderate cognitive decline or those recovering from trauma, surgery or treatments such as chemotherapy (Withaar et al., 2000; Reger et al., 2004; Leproust et al., 2008; Classen et al., 2009). The most reliable method of evaluating driving impairment is an on-road test with a DMV-certified driving examiner, but annual on-road driving evaluations for all seniors, or even just those with clinically diagnosed cognitive impairments, are neither practical nor economical (Schanke and Sundet, 2000; Kay et al., 2008; Versijpt et al., 2017). Therefore, there is a need for inexpensive and sensitive tests to predict on-road driving impairment.

This study investigated the use of simultaneous electroencephalogram (EEG) and electrocardiogram (ECG) measurement in a population of healthy participants and HIV+ patients (>55 years old) during a test of sustained attention and processing speed. The combination of EEG, ECG, and behavioral performance metrics derived from the 3CVT were previously proven highly sensitive and specific in quantifying daytime drowsiness associated either with sleep deprivation in healthy participants or in sleep disordered patients, predicting susceptibility to sleep deprivation, and assessing neurocognitive deficits in patients with Parkinson's disease (PD), AD, MCI, and sleep disorders (Westbrook et al., 2002; Berka et al., 2006, 2007, 2009; Pojman et al., 2009a,b; Johnson et al., 2010, 2011; Waninger et al., 2018). The 3CVT evaluates sustained attention, visuospatial processing speed, and decision-making. These cognitive abilities are relevant to driving performance and prior work suggests that EEG metrics obtained during 3CVT were sensitive to improvements in cognition as a result of successful interventions for both sleep deprivation and sleep disorders (Westbrook et al., 2002; Berka et al., 2006, 2007, 2009; Pojman et al., 2009a,b; Johnson et al., 2010, 2011; Stoiljkovic et al., 2018). In addition to 3CVT, EEG and ECG were also acquired during both a simulated driving scenario and an on-road driving evaluation to conduct an exploratory analysis to assess any potential real-time neurophysiological changes associated with driving performance. Specifically, differences in the early (P200) and late (LPP) components evoked by the 3CVT have been associated with differences in cognitive abilities such as selective attention, memory, and decision-making. Since the 3CVT EEG metrics were previously shown to be associated with neurocognitive deficits in cognitively impaired populations, the investigators hypothesized that these metrics could be useful in distinguishing Safe and Unsafe drivers.

Physiological (heart rate, heart rate variability, skin conductance, and respiration) and neurophysiological (EEG) measures have long been used to unobtrusively assess the psychophysiological correlates of driving performance during simulated and on-road driving. Characteristic changes in EEG Power Spectral Densities (PSDs) have been associated with real-time changes in driving performance, phasic task demands, multiple domains of workload, and drowsiness (Zwinkels et al., 1990; de Waard and Brookhuis, 1991; Brookhuis and de Waard, 1993; Rookhuis et al., 1993; Mitler et al., 1997; Lei and Roetting, 2011; Dijksterhuis et al., 2013). Similarly, heart rate and heart rate variability have proven useful in measuring dynamic changes in cognitive demand during driving (Brookhuis et al., 1991; Mulder, 2004; Mehler et al., 2009, 2012). Several recent reports suggest the potential utility of real-time EEG-based algorithms to detect driver drowsiness and inattention. Continuous monitoring of EEG and heart rate data during driving provides excellent temporal resolution and offers the potential for identifying driver fatigue early enough to intervene and prevent sleep onset. Several recent reports suggest the potential utility of real-time EEG-based algorithms to detect driver drowsiness and inattention (Ajinoroozi et al., 2016; Perrier et al., 2016; Hajinoroozi et al., 2017). Several challenges remain for the implementation of integrated driver monitoring systems including: obtaining high quality EEG and ECG with unobtrusive sensor systems, validating and implementing the real-time algorithms to achieve accurate identification of fatigue or inattention, and determining the optimal approach to interventions during driving (Dong et al., 2010). Another important consideration is the generalizability of the algorithms across all age groups, as the majority of published results use algorithms that have been designed and tested on college age research participants. These EEG-based algorithms are used to monitor real-time changes during driving. To date, EEG metrics have not been used to predict driving performance in elderly individuals with or without cognitive impairment.

As normal aging is associated with changes in cognitive abilities related to driving, normal aging also affects EEG signals. Older individuals show a decrease in power in the alpha band (8–13 Hz) and decreased amplitude of ERP components, particularly the P300 and Late Positive Potential (LPP) (De Gennaro et al., 2005; Polich and Corey-Bloom, 2005; Olichney et al., 2008; Vecchio et al., 2013; López et al., 2014; Ishii et al., 2017). Older drivers are also more likely to exhibit EEG based signs of fatigue and distraction that increase risks of driving errors (Johansson, 1997). In patients diagnosed with Alzheimer's disease, the most commonly reported findings for resting-state EEG are: a shift of the power spectrum to slower frequencies (i.e., increased delta and theta specifically over the temporal-parietal regions; decreased alpha, beta, and gamma) (Bonanni et al., 2008; Jelic and Kowalski, 2009; Dauwels et al., 2010a,b; Tsolaki et al., 2014). Patients with AD also display prolonged latencies and diminished ERP amplitudes and these cognitive-evoked measures do tend to correlate better with severity of cognitive impairments (Polich and Corey-Bloom, 2005; Garn et al., 2014). The EEG power shifts and ERP differences in AD are primarily associated with memory related functions. Additionally, patients with HIV (with a subset of those potentially having HAND) exhibited decreased amplitude and increased latency of the P300 and the Late Positive Potential (LPP) components compared to healthy controls (Polich et al., 2000; Polich and Basho, 2002; Chao et al., 2004; Bauer, 2011; Olichney et al., 2011; Papaliagkas et al., 2011). To date, these studies have not directly examined the relationship between EEG metrics associated with aging or cognitive impairment and driving competencies.

This paper contributes to the field by: (1) establishing the link between neurophysiological measures obtained during computerized neurocognitive assessments and on-road driving performance, (2) evaluating older adults (>55 years old) and individuals with a condition that can lead to cognitive impairment (HIV+). As such, this research offers the potential to provide a standardized methodology for predicting driving impairment due to disease related causes or natural aging.

## MATERIALS AND METHODS

## Participants

Sixty-three healthy participants (HP) (age 55–87 years, mean = 65 ± 8.2 years, 49.2% male) and 39 HIV+ patients (age 55–74 years, mean = 61 ± 4.7 years, 87.1% male) were enrolled in the study. The groups did not differ in years of education (HIV+: 9–20 years of education, mean = 15.5 ± 2.9; HP: 10–21 years of education, mean = 15.6 ± 2.7). HIV+ patients were primarily recruited from the University of California, San Diego HIV Neurobehavioral Research Program (UCSD HNRP) and healthy participants from the surrounding San Diego community using flyers and handouts.

Participants were selected after an initial telephone screening to determine their eligibility including the capability to provide informed consent to cognitive testing, simulator testing, and an on-road driving evaluation. Participants were included only if they possessed a current driver's license which was confirmed by the California Department of Motor Vehicles (CA DMV) on the day of their visit.

Additional exclusion criteria were: a history of loss of consciousness >30 min, current substance dependence, psychosis, diagnosis of a cardiovascular, sleep, or pulmonary disorder, and central nervous system opportunistic infections or neurologic disease other than HIV infection, reported diagnoses of Attention Deficit Hyperactivity Disorder (ADHD) or anxiety related disorders. All HIV+ individuals were on anti-retroviral therapy to control viral load, and healthy participants were excluded for all medication except for over the counter drugs and drugs for hypertension, diabetes, arthritis (non-opioid pain medication), and mild to moderate depression. The HIV+ populations used for this study were taking the following medications: 15 on antidepressants, eight on benzodiazepines, two on antipsychotics, three on anxiolytics, three on narcotics, and one on an anticoagulant. Urine toxicology (7-panel) and breathalyzer evaluations were also collected from all participants prior to starting the study visit. If either test was positive or the participant acted in a manner suggesting intoxication, he/she was rescheduled, or withdrawn from the study.

Three participants who signed informed consent forms and began the study protocol were excluded from all analyses due to a positive urine test for methamphetamine, and one additional participant was excluded due to being severely cognitively impaired despite a negative HIV status. Protocols were approved by both the UCSD IRB and Sharp IRB (IRBANA).

## Procedures

All participants completed neuropsychological (NP) testing and Advanced Brain Monitoring's (ABM) 3-Choice Vigilance Task (3CVT) as well as driving simulations (a screening drive, and subsequent challenge drive). A subset of the participants from the HIV+ (N=20) and HP (N=30) groups also completed an on-road driving evaluation (see below). EEG was collected concurrently using ABM's STATTM X10 EEG sensor headset during all three tasks: 3CVT, simulated driving, and the on-road driving evaluation. The X10 is a battery-powered, lightweight, easy-to-apply wireless EEG system that acquires 9 channels of EEG (Fz, F3, F4, Cz, C3, C4, P3, P4, POz, referenced to linked mastoids), and ECG. It uses passive, Ag/AgCl electrodes printed on PET strip flex circuit cables. A piece of singleuse foam filled with conductive cream (Synapse by Kustomer Kinetics) was attached to the strip over each electrode site in order to make contact with the scalp. Impedances were measured and all channels were considered acceptable at or below 40 kOhms. Amplification and the A/D conversion was done adjacent to the electrode sites, allowing for high-quality data to be collected with higher than traditional impedance cut-offs. Data were sampled at 256 Hz with a high band pass at 0.1 Hz and a low band pass, fifth order filter, at 100 Hz obtained digitally with sigma-delta 16-bit A/D converters. Data were transmitted wirelessly via Bluetooth to a host computer, where acquisition software then stored the psychophysiological data.

#### Cognitive and Medical Assessment

Cognitive status was successfully obtained through NP testing for 85 of the 102 participants (29 HIV+ and 56 HP), determined using either the HNRP NP assessment battery (56% of cohort) or the NIH Toolbox Cognition module (44% of cohort) (Berka and Marcotte, 2017). For this subset of participants, 34% of the HIV+ group was classified as impaired and 27% of the healthy participants were classified as impaired based on the NP testing, meaning there were no group differences in cognitive status due to HIV status. Impairment was defined for the toolbox as a T score of <40 on two of the tests, and for the NP assessment as a global deficit score of <0.5. For all participants, HIV status was confirmed through a finger stick blood test.

#### 3CVT and EEG Measures

All participants were administered 3CVT, with concurrent EEG recording to assess neurocognitive functions. The 3CVT incorporates features of the most common measures of sustained attention, such as the Continuous Performance Test, Wilkinson Reaction Time, and the PVT-192 (Riccio et al., 2001; Sateia, 2003). The 3CVT requires subjects to discriminate one primary Target (triangle shape N, 70% of trials) from Non-Target (triangle shape upside down H, 15% of trials). The remaining 15% of the trials were used as Distracters (presenting a diamond shape: ) to increase the task complexity but are not included in the final Event Related Potential analysis. The test is 20-min long, during which 376 images are presented for a duration of 0.2 s each. A training period is provided prior to the start to minimize practice effects (Levendowski et al., 2000, 2001). The 3CVT challenges the participant's ability to sustain attention by increasing the interstimulus interval (ISI) across four, 5-min quartiles. During the first quartile, the ISI ranges between 1.5 and 3 s, increasing up to 6 s during the second quartile, and up to 10 s during the third and fourth quartiles.

#### **ERP Measures**

For the 3CVT task, raw EEG signals were filtered between 0.1 and 50 Hz using a Hamming windowed Sinc FIR filter (0.1 Hz transition band). For each event type, EEG data were epoched from 1 s before and 2 s after the stimulus onset. The baseline was adjusted using data from 100 ms before the stimulus onset. Artifacted epochs were detected and excluded using automated algorithms (EEGLAB software) (Delorme and Makeig, 2004). Outliers were detected based on kurtosis of signal distribution (kurtosis >5 standard deviation), joint probability of values in an epoch given the whole data set (thresholded at 5 standard deviation), and unusual spectral patterns of epochs (with power spectrum 35 dB higher or lower than the baseline in the frequency range of 20–30 Hz). To exclude trials contaminated by ocular artifacts, trials were rejected if the absolute value of the EEG amplitude in any channel exceeded 100 microvolts during a window of 50 ms pre-stimulus onset to 750 ms post-stimulus onset. A minimum of 15 clean trials for each of the stimulus subtypes in 3CVT (Target and Non-Target) were required to be included in the analysis of that subtype. Grand average ERPs in each condition and trial type were calculated using a weighted average with the number of ERPs in each condition as the weights. For each participant, ERPs were measured using the average of the signal during a window of 180–220 ms poststimulus onset for the P200 component, and the late positive potential (LPP) was measured using the average of the signal during a window 300–700 ms post-stimulus onset.

#### Simulated Driving

Participants completed two simulated driving scenarios: an initial screening and a challenge. Seventy-eight percent of eligible participants were able to complete both scenarios. The remaining 22% were unable to complete both scenarios, primarily due to mild to severe motion sickness. To mitigate motion sickness, the driving scenario was split into three sessions with breaks in between. A STISIM M300WS Console driving simulator (System Technology Inc., Hawthorne, CA, USA) was used for both sets of driving simulations (**Figure 1**). The screening drive is a practice session of approximately 15 min given in order to familiarize participants with the driving simulator. Following the Screening Drive, participants began the Challenge Drive, which is a longer (3, 10-min segments, 30 min total), more complex drive assessing a range of abilities. Participants were instructed to complete the Challenge Drive while following traffic laws. The Challenge Drive was designed to be a surrogate for measuring on-road driving performance.

The Challenge Drive consisted of monotonous, uneventful, and low-load driving scenarios as well as highly demanding events such as busy intersections, crash avoidance, and unprotected turns. Busy sections were interspersed throughout the simulation run and lasted for 4–5 min. For example, one complex segment required the driver to avoid and pass slow moving cars while driving through dense fog. Once the fog lifted, the driver entered a city scene where a van was parked in the left lane and two pedestrians suddenly stepped into the road from in between two parked cars. Other highly engaging events included passing through a narrow construction zone with many barriers, avoiding cars suddenly entering the roadway, making left turns in front of oncoming traffic, passing slow moving trucks on a twolane highway with oncoming traffic, and avoiding pedestrians stepping into the roadway without warning. The non-challenging consent obtained).

sections consisted of stretches of highway where no other cars were present and no challenging events were triggered.

The Challenge Drive also contained a divided attention task called the Surrogate Reference Task (SuRT), aimed at examining distracted driving. The SuRT was initiated by an auditory cue (phone ringing) and required the participant to look down and to their right, forcing them to take their eyes entirely off the roadway to perform this secondary task, much like using a GPS or infotainment system. Participants were required to identify a circle that was different in size from other circles on the screen of a tablet (**Figure 2**). The easy, medium, and hard trials of this task were differentiated by the difference in size between the target and distractor circles. The target circle radii remained 20.7 mm for all three trials, while the distractor circle radii increased from 10.4 to 13.8 to 17.4 mm. Throughout this task, the simulation consisted of a two lane freeway without turns, a speed limit of 65 MPH, and no cars in either direction. Outcomes of interest included swerving [standard deviation of lateral position (SDLP)], speed maintenance (including variability) as well as accuracy and reaction time on the secondary task.

#### On-Road Driving Evaluation

A subset of 50 participants (age 55–79 years, mean = 62 ± 6.6, 66% male, 40% HIV+) who completed the neurocognitive testbed and the driving simulator were selected to complete the on-road drive. Only 50 were selected due to time and budget restraints; selected participants must have completed the 3CVT and driving simulator scenarios. The on-road driving route was approximately 12 miles and required, on average, 45 min to complete (**Supplementary Figure 1**). It was conducted by the Sharp Rehabilitation Services Driving Program using a standardized approach with excellent inter-rater reliability (Cohen's K = 0.86) and established sensitivity to HIVrelated driving changes. A DMV-certified driving examiner was positioned in the front passenger seat of a dual-brake automobile; an occupational therapist (OT) and ABM technician (taking detailed notes about the driving safety and performance as well as monitoring the EEG signals) observed the drive in the rear seats. Participants were instructed to drive through residential and commercial areas, across controlled and uncontrolled intersections, and on freeways (including multiple merges). The participants followed single and multi-step directions (e.g., "Make the next available right turn. . . In three traffic lights, make a left turn") throughout the duration of the drive.

## Evaluating Driving Performance

In order to evaluate driving performance participants were divided into groups of "Good" or "Poor" drivers based on performance in the simulator and "Safe" or "Unsafe" drivers based on on-road performance. The following sections describe this group assignment process.

#### On-Road Performance

Both the driving examiner and OT evaluated the drive in two ways. First, 186 scoring criteria for correctly performing traffic checks, maintaining lane position and speed, yielding when appropriate, etc. were assigned either a zero for pass, or a one for fail. Second, participants were given an overall score of 1 (excellent) through 5 (recommends they should not be driving) (**Supplementary Table 1**).

Each evaluator independently completed the pass/fail scores during the drive, and assigned an overall score after the conclusion of the drive. The driving instructor and OT would then arrive at a consensus evaluation for the overall score as well as a consensus regarding individual pass/fails. In addition, the OT documented critical errors in the form of physical or verbal interventions. Physical interventions included using the passenger-side brake and grabbing the wheel, while verbal interventions included any additional instructions or warnings that were not part of the scripted directions. Each driver was designated Safe or Unsafe based on the consolidated raters scores, comments, critiques, observations, and critical errors. Thirty-five of the 50 drivers were designated Safe (70%) and 15 drivers were designated Unsafe (30%).

#### Driving Simulator Performance

Individual mistakes over the course of the challenge drive were counted and given weights to generate a weighted score as follows:

Using these weights, a total weighted score was computed for each participant who completed the simulated drive. This weighted score was used to divide drivers into either Good or Poor groups. Drivers with a weighted score of 35 or more designated as Poor. This threshold of 35 was chosen to result in 70/30% Good/Poor ratio to match the Safe/Unsafe ratio observed during the on-road drive (see On-Road Performance). **Figure 3** shows the distribution of weighted scores for all participants who completed the simulated driving scenario.

### Predicting Driving Performance

A linear discriminant function (LDF) was designed to classify Safe vs. Unsafe drivers using EEG ERP measures (P200 and LPP for both Target and Non-Target trials across all channels) obtained during the 3CVT test. The variables used for the LDF were selected through a step-wise algorithm in a logistic regression analysis. The classifier was evaluated using a leave-one-out cross validation method.

## RESULTS

EEG and behavioral measures were computed for all three tasks (3CVT, simulated driving, and on-road evaluation). Performance in the driving simulator was used to group subjects into either

Good or Poor (section Driving Simulator Performance), and onroad driving performance was used to designate subjects as either Safe or Unsafe (section On-Road Performance). To investigate the relationship between each behavioral/EEG measure and driving performance, these measures were averaged across the Safe (or Good) groups and were compared to the average of the Unsafe (or Poor) groups.

To investigate the relationships between HIV seropositivity and driving performance, chi-square tests of independence were performed for simulated and on-road driving groups. The proportion of Good vs. Poor (60.6 vs. 39.4%) drivers in the HIV+ group was not significantly different than that of the HP group (69.2 vs. 30.8%) [χ**2** (1, n = 85) = 0.34, p = 0.56]. Similarly, the proportion of Safe vs. Unsafe (60.0 vs. 40.0%) drivers in the HIV+ group was not significantly different than that of the HP group (76.6 vs. 23.4%) [χ**2** (1, n = 50) = 0.89, p = 0.34]. Therefore, driving performance both in the simulator and onroad was determined to be independent of HIV status in this population.

### Behavioral Measures

Behavioral measures included simulated driving performance, on-road driving performance, and Reaction Time (RT)/Accuracy for the 3CVT, as described in sections Driving Simulator Performance, On-Road Performance, and 3CVT and EEG Measures, respectively.

#### 3CVT Behavioral Measures as Predictors of Driving Performance

Behavioral measures during the 3CVT attention task were computed for each participant including RT, Accuracy (percent correct), and a combined measure of performance (F-measure, i.e., a harmonic mean of normalized accuracy and reaction time) (Stikic et al., 2011). A student's t-test was used to determine whether group averages of 3CVT behavioral measures were different for Safe/Unsafe (on-road drive) and Good/Poor (driving simulator) drivers. F-measure showed no significant difference in performance between Safe and Unsafe drivers (p = 0.81, df = 47) (**Figure 4A**). However, Good drivers in the simulator had significantly higher performance compared to Poor drivers (p < 0.01, df = 77) (**Figure 4B**).

#### Driving Simulator

Throughout the driving simulation, there was high variability between subjects in speed, speed deviation, SDLP, and time to collision as individuals navigated the various complex segments with varying approaches. For example, **Supplementary Table 2** shows the high variance of speed between subjects for each block. Although participants were instructed to follow the rules of the road, the completion time for each segment of the driving scenario varied widely between participants. Because of the high between- and within- subject variability of these metrics, driving performance in the simulator was quantitatively computed using the variables described in section Driving Simulator Performance. To assess the relationship between onroad driving performance and simulator performance, a chisquared test of independence was performed. 72.7% of Safe drivers were Good in the simulator and 71.4% of Unsafe drivers were Poor in the simulator [χ**2** (1, n = 47) = 6.23, p = 0.01].

#### **SuRT Performance in Driving Simulator to Predict Simulator/On-Road Driving Performance**

The mean Number Correct and mean Reaction Time for each of the three difficulty levels of the secondary task are illustrated in **Figure 5**. Students' t-tests revealed that no significant difference in Number Correct from easy to medium was present, but Number Correct did differ significantly between medium and hard (t-test, df = 141, p < 0.01), and easy to hard (t-test, df = 141, p < 0.01). Mean Reaction Time significantly increased from easy to medium (t-test, df = 143, p < 0.05) and medium to hard (t-test, df = 141, p < 0.01).

Ideal driving behavior during the SuRT would be characterized by a low rate of swerving (low SDLP), an average speed close to the speed limit (65 MPH), and a low rate of speed deviation. SDLP significantly increased from easy to hard (t-test, df = 141, p < 0.01) and from medium to hard (t-test, df = 141, p < 0.01). Speed deviation significantly increased from easy to hard (t-test, df = 141, p < 0.05) and medium to hard (t-test, df = 141, p < 0.01). Average Speed decreased from medium to hard (df = 141, p < 0.05).

While the SuRT task proved to be useful in measuring the effect of multitasking on driving behavior, neither SuRT driving performance nor secondary task performance were significantly different for Good vs. Poor (simulator) or for Safe vs. Unsafe (on-road) drivers.

#### On-Road Drive

The overall Safe and Unsafe driver's scores were computed as described in section On-Road Performance and were used for group comparisons.

## Association Between 3CVT EEG ERP Measures and Driving Performance

EEG measures obtained during 3CVT were compared for each group in order to discover any potential associations between 3CVT EEG measures and driving performance measures. **Figure 6** shows the grand average ERPs for 3CVT Non-Target trials (left) and Target trials (right) plotted to compare the Safe and Unsafe drivers. On average, Unsafe drivers exhibit higher amplitudes at 200 ms post-stimulus onset and lower amplitude from 300 to 700 ms post-stimulus onset.

For each participant, ERPs were measured using the average of the signal during a window of 180–220 ms post-stimulus onset for the P200 component, and the late positive potential (LPP) was measured using the average of the signal during a window 300– 700 ms post-stimulus onset. Safe drivers exhibited a significantly smaller P200 over the left central region for Non-Target trials compared to Unsafe drivers (**Figure 7A**). HP Safe drivers

exhibited a significantly larger LPP over the left frontal region compared to HP Unsafe drivers for Target trials (**Figure 7B**). There was no significant difference between HIV Safe and HIV Unsafe in terms of LPP amplitude (**Figure 7B**). Additionally, there was no significant difference in LPP amplitude when comparing Safe and Unsafe drivers from both groups. **Table 1** summarizes the significant findings. The difference in the P200 and LPP components between Safe and Unsafe drivers are listed for both trial types (Target and Non-Target) and for all channels in **Supplementary Table 3**.

**Figure 8** shows the grand average ERPs for 3CVT Non-Target trials (left) and Target trials (right) plotted to compare the Good and Poor drivers in the simulator. On average, Poor drivers exhibit higher amplitudes at 200 ms post-stimulus onset, and lower LPP amplitude from 300 to 700 ms post-stimulus onset.

Overall, Poor drivers had a significantly higher P200 over left frontal-central channels (**Figure 9A**) and a significantly lower LPP amplitude over left frontal channels (**Figure 9B**) compared to Good drivers. **Table 2** summarizes the P200 findings and **Table 3** summarizes the LPP findings for all significant channels. The difference in the P200 and LPP components between Good and Poor drivers are listed for both trial types (Target and Non-Target) and for all channels in **Supplementary Table 4**.

## EEG Measures During the Simulator and On-Road Drive

EEG was acquired during the simulated driving scenario as well as the on-road drive in order to identify any possible real-time neurophysiological differences associated with driving performance. However, there were no significant findings.

## EEG and Behavioral Measures in Relation to Cognitive Status

Cognitive status (impaired vs. unimpaired, see section Cognitive and Medical Assessment) was not correlated with any of the behavioral, EEG, and driving performance measures included in this study.

## Classifier for Predicting On-Road Driving Performance

At the operating point the true positive rate and false positive rate of the classifier were 0.85 and 0.23, respectively. The area under ROC curve was also used as an overall measure of classification performance. The results (AUC = 0.88) were compared with another LDF using only performance measures obtained from the driving simulator as the predictors (see Driving Simulator

TABLE 1 | Average P200 components for all groups and subgroups based on on-road driving performance.

*Significant differences (t-test, p* < *0.05) are marked with asterisk.*

Performance) resulting in AUC = 0.73. The true positive and false positive rate at the operating point of this second classifier was 0.64 and 0.21, respectively. **Figure 10** shows the ROC curve for both classifiers. The higher performance of the EEG-based classifier, as opposed the classifier based on simulator data, demonstrates the power of EEG measures during an attention task in predicting on-road driving performance.

## DISCUSSION

Evidence from the present study revealed an association between on-road driving performance and EEG ERP data obtained during a short neurocognitive test of sustained attention (3CVT). The 3CVT EEG ERP measures were related to driving performance during a driving simulator task as well as an on-road driving evaluation. Unsafe on-road drivers and Poor drivers in the simulator both exhibited significantly larger P200 amplitude over the left frontal-central region compared to Safe (on-road) and Good (simulator) drivers, respectively. While this finding was observed for Target (frequent) and Non-Target (less frequent) trials, it was largest in response to Non-Target trials during the 3CVT. The P200 component is believed to index automatic, stimulus-driven allocation of attention to stimuli and may

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).

#### TABLE 2 | Average P200 amplitude for all groups and subgroups based on simulator driving performance.

*Significant differences (t-test,* \**p* < *0.05,* \*\**p* < *0.01) are marked with an asterisk.*

TABLE 3 | Average LPP amplitude for all groups and subgroups based on simulator driving performance.

*Significant differences (t-test,* \**p* < *0.05,* \*\**p* < *0.01) are marked with an asterisk.*

reflect biases for preferential processing of particular types of stimuli (Eldar et al., 2010; Gole et al., 2012; McIntosh et al., 2015). In this study, the association between P200 amplitude and driving performance may be linked to deficits in selective attention. Bad drivers exhibit impaired ability to maintain focus, improper allocation of attention, and are more easily distracted. In a separate study in which 3CVT EEG ERP biomarkers were evaluated in patients with a neurodegenerative disease affecting memory (amnestic MCI), no P200 differences were observed compared to healthy controls (Waninger et al., 2018). These amnestic MCI patients did not present with noticeable attentional deficits.

Additionally, Unsafe on-road drivers and Poor drivers in the simulator both exhibited a lower LPP amplitude over the frontal region, particularly for Target trials, compared to Safe (on-road) and Good (simulator) drivers, respectively. The late positive potential (LPP) has been shown to reflect feature evaluation, memory matching, and decision making (Withaar et al., 2000; Reger et al., 2004; Meghdadi et al., under review). Multiple reports suggest reduced amplitude of the LPP is associated with

cognitive decline (Schanke and Sundet, 2000; Charlton et al., 2003; Kay et al., 2008; Cysique et al., 2009; Versijpt et al., 2017; Department of Motor Vehicles, 2018a,c; Meghdadi et al., under review) and normal aging (Polich and Corey-Bloom, 2005; Babiloni et al., 2006, 2010; Olichney et al., 2008; López et al., 2014; Ishii et al., 2017). The association of bad driving and reduced amplitude of the LPP reported in the present study is consistent with previous studies that reported a correlation between LPP reduction and severity of cognitive impairment (Polich and Corey-Bloom, 2005; Garn et al., 2014).

The current study included healthy participants (HP) as well as HIV+ participants with well-controlled immune function as a result of antiretroviral therapy. Although current antiretrovirals are increasing the longevity and overall health of HIV+ individuals, HAND is still prevalent and may affect driving performance. The present study included only participants over the age of 55 due to the high likelihood of age-related decline in driving performance. There were no significant differences observed in driving performance between the HIV+ and healthy groups. In fact, the proportion of bad drivers was equivalent for both groups. Bad drivers (Unsafe or Poor) exhibited an increase in P200 amplitude independent of HIV status with highest observed P200 amplitude in HIV+ Unsafe (or Poor) drivers and lowest P200 amplitude in HP Safe (or Good) drivers. Cognitive status as measured by standard neuropsychological testing (see Cognitive and Medical Assessment) did not correlate with P200 amplitude.

Additionally, group differences were observed in the LPP during 3CVT, with the association between bad driving performance and the reduced amplitude of the LPP only significant for the HP group. While bad drivers (Unsafe or Poor) in the HP group show a significant decrease in LPP compared to HP Safe or Good drivers, this reduction was not observed for the HIV+ group. This may be because the LPP has already been significantly reduced as a result of HIV seropositivity (Hillyard et al., 1973; Polich et al., 2000; Olichney et al., 2011; Papaliagkas et al., 2011).

The classifier used both P200 and LPP metrics to predict drivers as either Safe or Unsafe. However, variables selected by the stepwise feature selection and the results from 3CVT ERP data of the present study suggest the P200 is a stable and reliable predictor of driving performance. Preliminary results suggest this P200 effect is consistently observed across other tests of focused and divided attention (Meghdadi et al., under review).

While EEG measures acquired during the 3CVT sustained attention task were highly associated with driving performance, analysis of the EEG measures acquired in the driving simulator and on-road drive did not significantly predict driving performance. The complexity of the driving scenarios and varying driving strategies employed by participants did not allow for precise event locked EEG analyses as was the case for 3CVT. Although participants were instructed to follow the rules of the road, the completion time for each segment of the driving scenario and onroad drive varied widely between participants. The only highly controlled segment of the either task was the SuRT task performed during the simulated driving scenario. SuRT task difficulty was inversely correlated with SuRT driving and secondary task performance. However, neither was correlated with overall simulated or on-road driving performance.

In this study, EEG ERPs observed during attention tasks and their relation to driving performance provide the basis for an inexpensive, fast, and reliable screening exam for elderly drivers using only EEG acquired concurrently during attention tasks. Performance in the driving simulator alone provided only a reasonable prediction of on-road driving performance but was not nearly as accurate as the 3CVT EEG-based classifier.

Driving is an essential aspect of maintaining independence, but driving ability can begin to deteriorate as people age. Through natural aging or disease-related causes, functional impairments can impede elderly drivers from driving safely. ERP measures (P200 and LPP) described in this study are shown to reliably predict driving performance in both healthy and HIV+ individuals across a broad age spectrum (55–87 years old). A diagnosis of a neurodegenerative disease (MCI, PDD, HAND, AD, etc.) alone does not necessarily mean an individual is too impaired to drive safely. In the present study, standard neuropsychological testing was not predictive of driving performance. Currently, there is no sensitive test to determine if an individual is actually impaired except for an on-road drive with a driving examiner. To address this unmet need, a portable EEG system could be used to perform a short and inexpensive neurocognitive test to obtain ERP data for any patient. This ERP data could in turn be fed into a classifier to determine whether or not an individual requires an onroad driving evaluation (classifier responded Unsafe or Safe). While there is a false positive rate of 23%, this approach offers a much better alternative than requiring on-road evaluations for all older or cognitively impaired drivers. Additionally, the model will be improved and refined by increasing the size of the dataset with other populations currently being studied.

Future research is required to fully describe the P200 effect by implementing different types of tasks designed to activate neural circuitry associated with varying aspects of attention and cognition. In the field of driving assessment, further experiments with larger and more diverse populations (including drivers with a variety of neurodegenerative diseases) are needed. A more in-depth analysis of driving performance is also needed to further understand the specific functional deficits associated with increased P200 amplitude.

## DATA AVAILABILITY STATEMENT

A link to download the de-identified data (.edf files) will be made available upon request.

## ETHICS STATEMENT

The protocols in the study were approved by both the University of California, San Diego (UCSD) IRB and Sharp IRB (IRBANA) with written informed consent of all subjects. The authors only received de-identified data. HIPPA guidelines were followed throughout the study to protect patient privacy.

## AUTHOR CONTRIBUTIONS

TM and CB conceived the present project, and CB supervised the project. GR wrote the manuscript with the help of CB, AM, SS, and TM. ES and KM collected the in-lab data, and GR collected the data in the field. AM, MK, MC, and GR analyzed the data. TM, ES, KM, and TR designed and implemented the simulated drive and TM designed the onroad drive. CB, AM, GR, MC, and MK interpreted the results.

## FUNDING

This work was supported by the NIH [Grant Number: 5R42MH097303].

#### ACKNOWLEDGMENTS

The authors would like to thank the following people for their contributions to various aspects of this project: Vedeline

#### REFERENCES

Torreon, Bradly Stone, Rudy Chang, Robin Johnson, Kyla Manawatao, and Josh Miller.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnhum. 2018.00532/full#supplementary-material

M. Grootjen (Westchester, IL: American Academy of Sleep Medicine One Westbrook Corporate Center).

**Conflict of Interest Statement:** GR, CB, AM, MK, MC, SS are paid salaries or consulting fees by Advanced Brain Monitoring, and CB is a shareholder of Advanced Brain Monitoring, Inc.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Rupp, Berka, Meghdadi, Kari´c, Casillas, Smith, Rosenthal, McShea, Sones and Marcotte. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Demonstrating Brain-Level Interactions Between Visuospatial Attentional Demands and Working Memory Load While Driving Using Functional Near-Infrared Spectroscopy

#### Edited by:

Jakob Scheunemann1,2†

\*

Jochem W. Rieger<sup>1</sup>

Bruce Mehler, Massachusetts Institute of Technology, United States

#### Reviewed by:

Noman Naseer, Air University, Pakistan Sean Seaman, Touchstone Evaluations Inc., United States John Lenneman, Toyota Collaborative Safety Research Center, United States Yu-Kai Wang, University of Technology Sydney, Australia

#### \*Correspondence:

Jochem W. Rieger<EMAIL_ADDRESS>†These authors have contributed equally to this work

Received: 19 June 2018 Accepted: 31 December 2018 Published: 23 January 2019

#### Citation:

Scheunemann J, Unni A, Ihme K, Jipp M and Rieger JW (2019) Demonstrating Brain-Level Interactions Between Visuospatial Attentional Demands and Working Memory Load While Driving Using Functional Near-Infrared Spectroscopy. Front. Hum. Neurosci. 12:542. doi: 10.3389/fnhum.2018.00542 <sup>1</sup> Department of Psychology, University of Oldenburg, Oldenburg, Germany, <sup>2</sup> Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, <sup>3</sup> Institute of Transportation Systems, German Aerospace Center (DLR), Braunschweig, Germany

, Klas Ihme<sup>3</sup>

, Meike Jipp<sup>3</sup> and

, Anirudh Unni<sup>1</sup>†

Driving is a complex task concurrently drawing on multiple cognitive resources. Yet, there is a lack of studies investigating interactions at the brain-level among different driving subtasks in dual-tasking. This study investigates how visuospatial attentional demands related to increased driving difficulty interacts with different working memory load (WML) levels at the brain level. Using multichannel whole-head high density functional near-infrared spectroscopy (fNIRS) brain activation measurements, we aimed to predict driving difficulty level, both separate for each WML level and with a combined model. Participants drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. In half of the time, the course led through a construction site with reduced lane width, increasing visuospatial attentional demands. Concurrently, participants performed a modified version of the n-back task with five different WML levels (from 0-back up to 4-back), forcing them to continuously update, memorize, and recall the sequence of the previous 'n' speed signs and adjust their speed accordingly. Using multivariate logistic ridge regression, we were able to correctly predict driving difficulty in 75.0% of the signal samples (1.955 Hz sampling rate) across 15 participants in an out-of-sample cross-validation of classifiers trained on fNIRS data separately for each WML level. There was a significant effect of the WML level on the driving difficulty prediction accuracies [range 62.2–87.1%; χ 2 (4) = 19.9, p < 0.001, Kruskal–Wallis H test] with highest prediction rates at intermediate WML levels. On the contrary, training one classifier on fNIRS data across all WML levels severely degraded prediction performance (mean accuracy of 46.8%). Activation changes in the bilateral dorsal frontal (putative BA46), bilateral inferior parietal (putative BA39), and left superior parietal (putative BA7) areas were most predictive to increased driving difficulty. These discriminative patterns diminished at higher WML levels indicating that visuospatial

attentional demands and WML involve interacting underlying brain processes. The changing pattern of driving difficulty related brain areas across WML levels could indicate potential changes in the multitasking strategy with level of WML demand, in line with the multiple resource theory.

Keywords: driver state assessment, mental workload, driver workload estimation, visual-motor coordination, visual attention, brain-level interactions, dual-task, fNIRS

## INTRODUCTION

Driving is a complex task, composed of multiple subtasks where different cognitive demands are concurrently imposed on the driver. For instance, one needs to be attentive toward unforeseen events, integrate information from within and outside the vehicle, and control the vehicle to keep it on the lane. All those tasks require cognitive resources of limited capacity (Wickens et al., 2008). Some of these tasks could possibly draw from the same shared resources, leading to a potential interaction between different subtasks.

Working memory plays an important role while driving since the driver has to continuously integrate and dynamically update information from internal and external traffic environments (De Waard, 1996; da Silva, 2014). For example, Wood et al. (2016) have associated increased working memory capacity with better ability to control visual attention while being less distracted in different driving tasks. Further, certain driving situations are associated with increased working memory demands, e.g., left turns at intersections (Guerrier et al., 1999) or driving within a dense city environment (Patten et al., 2006) as they require integration of more items into trajectory planning. Yet, working memory is a capacity-limited system (Baddeley, 2003; Cowan, 2010) and working memory overload deteriorates driving performance (Lavie, 2010). For example, it has been shown that increasing working memory load (WML) via a secondary task decreases driving performance on the lane change task (Ross et al., 2018). Interestingly, this effect was larger for people with less working memory capacity.

Besides working memory, driving requires visuospatial attention and visuomotor control (Vingerhoets and Stroobant, 1999; Lust et al., 2011; Benedetto et al., 2013). Visual attention is demanded because the driver needs to simultaneously integrate central and peripheral vision within a rapidly changing moving environment, while monitoring for unexpected critical events (Owsley and McGwin, 2010). Under decreased vision, more resources are allocated to lane keeping (Gao and Zhang, 2016). More specifically, Brooks et al. (2018) could link a decrement in driving performance in a lane-keeping task to increased peristimulus alpha activity, an indication for poor visuospatial attention. Further, when participants drove in a narrow road condition as compared to the ordinary driving task with normal lane widths, fNIRS measured increased activation in the prefrontal areas (Shimizu et al., 2009). This supports other findings showing that driving in narrowed lanes is more demanding (De Waard et al., 1995; Liu et al., 2016a) and associated with performance loss (Rosey and Auberlet, 2012). Thus, narrowed lanes seem to increase visuospatial attention load necessary for controlling the vehicle safely.

In driving, different task demands interact with each other (Borghini et al., 2014; Matthews et al., 2015). On the behavioral level, there are various studies that have investigated the effect of cognitive load on driving performance. For example, a majority of the studies suggest that cognitive load actually improves driving performance indicated by improved lane keeping (He and McCarley, 2011; Cooper et al., 2013; for review see Engström et al., 2017). Yet, for studies in which driving difficulty was increased by exposing the car to crosswinds, an additional cognitive load task led to an improvement in lateral control in one study (He et al., 2014), but a drop in another study (Medeiros-Ward et al., 2014).

The interaction of workload and driving performance on the neural level was studied by Wang et al. (2018). In their driving study, using electroencephalography (EEG), car drifts were induced requiring the participant to make lane-keeping adjustments. Additionally, a mathematical calculation task was presented either right before, right after or simultaneously to the induced car drifts. Theta and alpha oscillations in frontal, parietal and occipital areas in the different dualtask conditions were compared to oscillations in single task conditions. While over-additive activation in the frontal theta oscillations were found for the simultaneous condition, all other location-band combinations revealed either additive or under-additive activation in dual-tasking. Vossen et al. (2016) studied the effect of WML on the temporal neural markers for visuospatial attention. Participants performed worse in a visuospatial attention task, in which participants had to react to specific cued visual stimuli in a traffic scenery, when they had to complete an additional verbal memory rehearsal task simultaneously. A further analysis of evoked response potentials (ERPs) from EEG showed that in the high WML conditions, there was a reduction and delay of neural markers only in the early stages of the visuospatial task associated with the initiation of spatial orienting. On the contrary, later stages of the visuospatial task responsible for retaining attentional focus and target selection revealed no differences in the high WML conditions.

The effect of an additional task on the primary driving task was also studied with functional magnetic resonance imaging (fMRI). In a driving simulator study, Just et al. (2008) found a decrease in parietal activation associated with spatial attention in normal driving when participants performed an additional listening comprehension task. As spatial attention and listening comprehension draw resources mostly from non-overlapping

cortical areas, the authors interpret the "diversion of attention as reflecting capacity limit on the amount of attention or resources that can be distributed across the two tasks" (p. 76). Similarly, in a more recent fMRI-driving simulator study, Choi et al. (2017) found a decrease in activation in the parietal areas and an increase in activation in the inferior frontal gyrus and the superior temporal gyrus associated with an additional listening comprehension task while driving. These results illustrate the complex interaction of how an additional task alters the neural activation associated with the primary driving task.

In a cognitive approach on dual-tasking, Wickens (2008) defined resources in his multiple resource theory of attention along four dimensions, namely stages of processing, codes of processing, modalities, and visual channels. The model assumes an interference in dual-tasking when tasks compete for the same resources. For each task, a computational model codes the amount of resources needed for each dimension. For any dimension, if all tasks combined require more resources than what is available, the model predicts interference and performance loss (Wickens, 2002). In an earlier study, the model was implemented to predict driving performance along nine different dual-task combinations consisting of different driving conditions (e.g., urban vs. rural routes) and additional different secondary tasks (e.g., visual vs. auditory backward reading of numbers; Horrey and Wickens, 2003). Performance loss in dualtasking was successfully predicted by the model for latency of the secondary task and response times to critical road hazards.

An important aspect of the multiple resource theory is executive control, which describes the allocation of resources between tasks. Especially in situations of high dual-task demands, resources might be drawn away from a less prioritized task toward a task with higher priority. Hence, the amount of resources allocated to a subtask depends on the demands of the other subtask, in particular when the other subtask is prioritized. However, how these interactions happen on the brain level in real world tasks is largely unknown. Therefore, in this study, we aimed to investigate at the brain level, how different task demands in one cognitive domain affect the resource allocation for another cognitive domain, by comparing the specificity of predictive brain activation patterns across various dual-tasking scenarios. Specifically, we sought to explore how the assessment of visuospatial attentional driving demands from functional near-infrared spectroscopy (fNIRS) measurements depends on different WML levels.

Functional near-infrared spectroscopy has recently become popular in driving research as a measure of brain activity because it provides brain activations measures with reasonable anatomical and temporal resolution in relatively unconstrained applied settings (Liu et al., 2016b; Sibi et al., 2016). FNIRS uses near-infrared light to measure local concentration changes of deoxygenated hemoglobin (HbR) and oxygenated hemoglobin (HbO) from cortical brain areas which are seen as correlates of functional brain activity (Villringer et al., 1993; Sassaroli and Fantini, 2004). In comparison to HbO, HbR signals are considered to be less influenced by systemic physiological artifacts like cardiac pulsation, respiration, or Mayer wave fluctuations than HbO (Obrig et al., 2000; Zhang et al., 2005, 2009; Huppert et al., 2009; Suzuki, 2017). Other studies additionally reported that HbR tends to correlate stronger with blood oxygenation level dependent (BOLD) response than HbO (MacIntosh et al., 2003; Huppert et al., 2006; Schroeter et al., 2006; Foy et al., 2016).

In comparison to fMRI, fNIRS has lower spatial (Cui et al., 2011; Mehta and Parasuraman, 2013; Pinti et al., 2018), but better temporal resolution (Huppert et al., 2006). Compared to EEG, fNIRS has lower temporal (Naseer and Hong, 2015), yet better spatial resolution (Scholkmann et al., 2014). Due to its robustness against motion artifacts and external electrical noise, fNIRS is suitable for applied settings (Masataka et al., 2015; Balardin et al., 2017) and has been used in actual driving (Yoshino et al., 2013a,b). FNIRS has shown to be sensitive toward changes in mental workload in the applied fields of simulated flight operation (Ayaz et al., 2012; Durantin et al., 2014), simulated urban rail driving (Li et al., 2018), as well as simulated (Unni et al., 2017; Xu et al., 2017) and actual car driving (Ahn Son et al., 2018). Further, fNIRS could detect elevated visual attention in curve driving, as indicated by increased activity in right premotor cortex, right frontal eye field, and bilateral prefrontal cortex (Oka et al., 2015). Thus, fNIRS is applicable in applied driving settings while providing independent measures of activity in functionally specific brain areas.

In this study [some data has already been published in Unni et al. (2017)], we used fNIRS brain activation measurements obtained during driving to predict two types of cognitive demands: visuospatial attentional demands and working memory demands, both modulated simultaneously. To manipulate visuospatial attentional driving difficulty, participants drove in a 360◦ Virtual-Reality (VR) driving simulator, half of the time through a construction site with a reduced lane width. At the same time, participants had to perform the primary driving task, which was a working memory speed regulation task (Unni et al., 2017) with five different WML levels. Recording almost whole-head fNIRS brain activation measurements, we aimed at predicting driving difficulty (i.e., driving outside and within construction sites with narrower lane widths) as a measure for visuospatial attentional demands. One of our central questions was whether it is possible to predict driving difficulty or whether task interactions between visuospatial attentional demands and WML levels at the brain level render this impossible. More precisely, we calculated decoding models for the prediction of driving difficulty from almost whole-head fNIRS for each WML level separately and a model which combined fNIRS data over all WML levels. A model with good prediction accuracy for driving difficulty can be interpreted such that there exist distinct neural correlates associated with increased driving difficulty. If there was no interaction between WML and visuospatial attention, a decoding model which combined fNIRS data over all WML levels would perform similarly well in predicting driving difficulty as using a decoding model for each WML level separately. However, if there was an interaction between visuospatial attentional demands and working memory demands, activation patterns associated with increased driving difficulty would differ over WML levels leading to better prediction accuracy for the separate models. Hence, the comparison of prediction

accuracies of the different decoding models characterizes the interaction between the visuospatial attention with working memory processing at the brain level. This is relevant for the development of brain-based driver assistive systems as well as for understanding the nature of the multitasking interactions at the brain level.

## MATERIALS AND METHODS

The experiment was implemented in a driving simulator where participants drove on a highway with varying concurrent traffic. Participants performed a driving task in a two factorial within participant design with factors driving difficulty manipulated by visuospatial attentional demands (two levels: non-construction and construction) and WML (five levels: 0–4 back). The driving difficulty was manipulated via changes of lane width and for WML manipulation, participants performed a digit-span n-back speed regulation task. The details of the tasks are provided below.

## Participants

Nineteen volunteers (17 males) aged 19–32 years (Mean ± SD = 25.2 ± 3.7) participated in the experiment. All participants possessed a valid German driving license at the time of the experiment. Participants gave informed consent prior to the experiment and received a financial reimbursement of 10 € per hour. The experiment was conducted according to the guidelines of the German Aerospace Center and was approved by the Ethics Committee of the Carl von Ossietzky University, Oldenburg.

## Experimental Set-Up

The experiment was set up in a VR-lab at the German Aerospace Research Center allowing a 360◦ full view (Fischer et al., 2014). During the experiment, participants were operating a realistic vehicle mock-up equipped with common throttle, brake pedal, steering wheel, and indicators. Participants drove on a simulated, slightly curvy highway (64 km in total; developed on the platform Virtual Test Drive, Vires Simulationstechnologie, Bad Aibling, Germany) with varying concurring traffic. There were 15 vehicles set randomly in an area with a radius of 1000 m around the ego vehicle. Of those vehicles, 60% followed the direction of the ego vehicle; 35% were in the front, 35% in the back, 15% to the left and 15% to the right of the ego vehicle; and 45% were trucks, other 55% were cars.

While driving, fNIRS brain activation measurements were recorded from almost whole-head at a sampling frequency of 1.955 Hertz (Hz) from thirty-two optical emitters and detectors using two NIRScout systems (NIRx Medical Technologies, LLC, United States) in tandem mode. The system uses two wavelengths of 760 and 850 nm to calculate the relative concentration changes of HbO and HbR. We defined 78 fNIRS channels (emitterdetector combinations) in total with an average channel distance of about 3.5 cm. The exact channel locations are provided in Unni et al. (2017). Along with fNIRS data, steering wheel position and driving speed was also recorded at a sampling frequency of 50 Hz.

## Visuospatial Attention Manipulation

We manipulated the visuospatial attention demands for the driving task throughout the highway. For about half of the time, participants were driving within a construction site (labeled as construction). During the other half of the drive, participants were driving on a normal road without the construction site (labeled as non-construction).

The main differences between those two conditions were the number of available lanes and their widths. In the nonconstruction condition, there were three lanes available with a total width of 10.75 m, consisting of two lanes with a width of 3.5 m (left and center lane) and a slightly wider right lane with a width of 3.75 m. Driving in the construction site was more difficult where only two lanes were available. The widths of the lanes were also reduced along the construction sites with the left and right lanes having a width of 2.5 and 3.5 m, respectively, resulting in a total width of 6 m.

Further, the highway resembled the typical design of German highways. In the non-construction site, there were solid markings in white on the left and right of the road with dashed lines between the lanes. As typical for German highways, pylons marked the beginning and end of the construction sites and yellow markings highlighted the new lanes. The positions and design of the speed signs remained the same.

Screenshots from the experimental paradigm for both conditions can be seen in **Figure 1**. In both conditions, participants had to avoid collisions with other vehicles in ongoing traffic and overtake when it was deemed necessary to drive at the correct speed. Speed signs and WML levels varied at the same rate over both levels of driving difficulty.

## Working Memory Load Manipulation

The n-back task is considered to be a benchmark for WML manipulation in neurocognitive psychology (Kirchner, 1958). In a classical n-back task, a series of numbers, letters, or other stimuli are presented. Participants then have to compare the current stimulus with the stimulus n steps back and give a response whenever they are the same. We modified the classical n-back task to be applicable in the driving scenario by using speed signs as stimuli. Participants had to adjust their speed to the speed sign they passed n speed signs before. For a successful performance, it was necessary that participants continuously update, memorize and recall the previous n speed signs. Our experiment consisted of five different workload levels from n = 0 (adjusting the speed to the current speed sign) to n = 4 (adjusting the speed to the 4th previous speed sign). The task is illustrated in **Figure 2**. A detailed explanation of the WML speed regulation task can be found in Unni et al. (2017).

and has to keep the current speed sign in memory (80 km/h). Figure taken from Unni et al., 2017.

Participants had a 6 s window (3 s both before and after passing the sign) to adjust their speed to the target speed. A deviation up to ±5 km/h from the target speed was judged as correct. Whenever the deviation was more than ±5 km/h, a warning message 'Please pay attention to your speed' was displayed on the screen. This was done to motivate the participant to drive at the correct speed. This message appeared on the screen until the participant drove within the correct speed range. For every new n-back task, participants were instructed to stay at the speed of the first sign until they passed 'n' successive speed signs before they could begin with the n-back task. There were nine different speed signs (60–140 km/h in steps of 10 km/h) presented in random order to avoid sequencing effects. At the beginning of a new n-back condition, participants were informed via a message displayed for 5 s on the VR-screen about the next n-back level to be accomplished.

#### Experimental Procedure

The participants started with a 20 min training session where they drove each of the five different n-back levels twice. Then, the main experiment started, which lasted about 60 min with a break in the middle. In total, the participants performed each 3 min long n-back level four times, twice in each of the construction and non-construction conditions. The speed signs were distributed such that the participants passed a new speed sign roughly every 20 s with some temporal jitter. The construction and nonconstruction sites were alternating with every change in n-back level. The order of the n-back levels was pseudorandomized in such a way that the same n-back level was never driven twice in a row and each n-back level was performed twice in the construction and the non-construction conditions respectively. Also, the sequence of n-back levels repeated itself in reversed order after the break to avoid sequencing effects.

### Data Analysis Driving Behavior

To determine the effect of increasing WML levels, we calculated error rates in the speed regulation task. As a measure of performance in the working memory task, we calcultated the

percentage of time segments in which the participant did not reach the target speed (<90% driving within the tolerance interval around the target speed). In line with the analysis for the fNIRS data described below, we have excluded those time segments (∼8% of time segments over all participants) from the other analysis of driving behavior.

In order to check whether driving through the construction site was associated with changes in driving performance, we analyzed the steering reversal rate. Steering reversal rate was defined as the number of times the participant crossed the centered position of the wheel. Steering reversal rate usually increases with increased driving difficulty, as more corrections to the steering wheel position are required (Macdonald and Hoffmann, 1980). As a measure for increased driving difficulty, we calculated the difference in steering reversal rate between driving in the construction and non-construction condition for each n-back level.

Due to a problem in data recording in one participant, driving behavior is presented for only 14 participants.

#### Working Memory Capacity

To ensure that all participants had comparable levels of working memory capacity, they first performed the memory updating task from the working memory capacity test battery by Lewandowsky et al. (2010). In this test, participants had to remember a set of digits which they had to update continuously through a series of simple arithmetic operations (single digit addition and subtraction). For every correct trial, participants received 1 point. The average total score was 38.4 (SD = 10.7) out of a maximum possible score of 60. One participant was excluded from the data analysis, because of a score more than two standard deviations below the mean.

#### FNIRS Data Processing