ORIGINAL RESEARCH
published: 23 November 2018
doi: 10.3389/fpsyg.2018.02289
Frontiers in Psychology | www.frontiersin.org 1November 2018 | Volume 9 | Article 2289
Edited by:
Claudia Gianelli,
Universität Potsdam, Germany
Reviewed by:
Edmund Wascher,
Leibniz-Institut für Arbeitsforschung an
der TU Dortmund (IfADo), Germany
Sven Hoffmann,
German Sport University Cologne,
Germany
*Correspondence:
Janna Protzak
Specialty section:
This article was submitted to
Cognition,
a section of the journal
Frontiers in Psychology
Received: 07 July 2018
Accepted: 02 November 2018
Published: 23 November 2018
Citation:
Protzak J and Gramann K (2018)
Investigating Established EEG
Parameter During Real-World Driving.
Front. Psychol. 9:2289.
doi: 10.3389/fpsyg.2018.02289
Investigating Established EEG
Parameter During Real-World Driving
Janna Protzak1
*and Klaus Gramann2,3,4
1Junior Research Group FANS (Pedestrian Assistance System for Older Road User), Institute of Psychology and
Ergonomics, Technische Universität Berlin, Berlin, Germany, 2Biological Psychology and Neuroergonomics, Technische
Universität Berlin, Berlin, Germany, 3Center for Advanced Neurological Engineering, University of California, San Diego,
San Diego, CA, United States, 4School of Software, University of Technology, Sydney, NSW, Australia
In real life, behavior is influenced by dynamically changing contextual factors and is
rarely limited to simple tasks and binary choices. For a meaningful interpretation of brain
dynamics underlying more natural cognitive processing in active humans, ecologically
valid test scenarios are essential. To understand whether brain dynamics in restricted
artificial lab settings reflect the neural activity in complex natural environments, we
systematically tested the auditory event-related P300 in both settings. We developed
an integrative approach comprising an initial P300-study in a highly controlled laboratory
set-up and a subsequent validation within a realistic driving scenario. Using a simulated
dialog with a speech-based input system, increased P300 amplitudes reflected
processing of infrequent and incorrect auditory feedback events in both the laboratory
setting and the real world setup. Environmental noise and movement-related activity in
the car driving scenario led to higher data rejection rates but revealed comparable theta
and alpha frequency band pattern. Our results demonstrate the possibility to investigate
cognitive functions like context updating in highly artifact prone driving scenarios and
encourage the consideration of more realistic task settings in prospective brain imaging
approaches.
Keywords: MoBI, real driving, P300, electroencephalography (EEG), auditory feedback
1. INTRODUCTION
To improve our understanding of human cognition and the underlying brain dynamic processes
in real life situations, ecological task settings are needed that allow complex and realistic behaviors
(Engel et al., 2013). On-road driving scenarios are such an example of an ecological task setting in
which, in contrast to simulated driving, incorrect behavior can have drastic consequences. While
laboratory studies allow controlled investigations of specific cognitive and behavioral processes, it
is not clear whether these phenomena can be observed in real life conditions. This is especially the
case for behaviors that involve active movements of participants which provide sensory feedback
that itself influences brain dynamics and cognition (e.g., Gramann, 2013). However, the application
of established brain imaging methods like electroencephalography (EEG) in more natural task
settings are hindered by artifacts induced by active behavior. Non-brain activity like muscle
and eye movements, or electric and mechanical artifacts can severely impact the signal quality
on the sensor level. However, advances in mobile amplifier systems and developments in data
analyses approaches can overcome these problems. The recently developed Mobile Brain-Body
Imaging (MoBI) approach (e.g., Makeig et al., 2009; Gramann et al., 2011, 2014) overcomes the
restrictions of traditional imaging modalities by using ambulatory EEG or NIRS devices combined
Protzak and Gramann EEG Parameter During Real-World Driving
with motion capture and other data streams that allow active
behavior (e.g., Gwin et al., 2010; Jungnickel and Gramann,
2016; Banaei et al., 2017). MoBI studies demonstrate that brain
activity can be distinguished from environmental and behavioral
artifacts, opening up new possibilities for more realistic test
and acquisition scenarios outside restricted laboratory set-ups.
Driving a car is one such realistic scenario that is highly
relevant for a large part of the population but represents a
hostile recording environment for EEG recordings. Driving takes
place in non-shielded environments with electronic equipment
surrounding the driver and the task requires complex behaviors,
including movement of the eyes, the head, as well as the arms
and shoulders, that are typically restricted in standard laboratory
settings to avoid movement-related artifacts from distorting
the signal of interest. Analyzing human brain dynamics in a
real driving scenario can thus be considered a stress test for
comparison of EEG parameters, e.g., event-related potentials
(ERP), obtained during real-world driving with parameters
established in traditional laboratory settings including car
simulators. If established parameters like the event-related P300
component can be replicated in real driving scenarios, EEG-
data can be used to improve our understanding of how drivers
process information while controlling a vehicle in a realistic
environment. Providing direct access to the driver’s neuronal
responses during different driving process phases, EEG might
serve the development and evaluation of user centered designs
for technical assistance systems in the safety-critical driving
environment (e.g., Brouwer et al., 2017).
So far, only a few studies have recorded and analyzed brain
activity in real-life driving tasks and the majority of these
studies focus on workload measures (Kohlmorgen et al., 2007)
or vigilance (e.g., Kecklund and Akerstedt, 1993; Papadelis et al.,
2007; Schmidt et al., 2009; Simon et al., 2011; Sonnleitner
et al., 2014). Haufe et al. (2014) present results from a driving
study for an automated braking assistance system using EEG
and EMG data demonstrating the potential use of event-related
potentials (ERP) to enhance automated driving technology.
Because the focus of the study by Haufe and colleagues was
on the replication of classification results from an earlier
driving simulator study (Haufe et al., 2011), no quantitative
analyses of ERP components were provided. Zhang et al. (2015)
executed a combined simulator and real car study to develop a
brain-computer interface (BCI) for detecting error-related EEG-
activity. Despite a clear focus on classification accuracies and a
small sample size for the real car experiment, the ERP results
revealed comparable patterns for both acquisition scenarios, even
though these were not specifically addressed in the discussion.
Krol et al. (2017) investigated a BCI approach during interaction
with an automated cruise control system in a real driving
scenario. The authors demonstrate high classification accuracies
for unexpected events during cruise control. However, as the
focus was on classification and not replication of specific EEG
features, no general conclusion can be drawn from this study
about the replicability of established EEG parameters.
As no previous study has provided a detailed analysis of
event related potentials during real life driving, it is still an
open question whether systematic ERP-analysis is possible with
data recorded in real driving scenarios and whether the results
can be compared with those from traditional laboratory EEG
recordings. We addressed this question by comparing the event-
related P300 recorded during a dual-task driving scenario and
within a highly controlled single task laboratory setup. The
auditory secondary task consisted of an interaction of the
participant with a speech input device, resembling a common
on-road secondary task. ERPs with onset of incorrect feedback
from the speech input device were analyzed with a focus on
the event-related P300 component, a positive deflection in the
ERP that represents a well-established parameter for analyzing
cognitive functions like attention and memory, substantiated by
results from extensive laboratory assessments with numerous and
heterogeneous groups of persons (Sutton et al., 1965, for reviews
see Fabiani et al., 1987; Picton, 1992). Increased P300 amplitudes
can be observed for infrequent targets in a stream of frequent
stimuli (for a review see Polich, 2007), for random and time
varying targets in single-stimulus paradigms (Polich et al., 1994),
for unexpected feedback events (Horst et al., 1980) or when
erroneous actions are observed (de Bruijn et al., 2007). It has
been argued that the reversed relationship of stimulus probability
and P300 amplitudes indexes the amount of working memory
updating after deviant events that is necessary for the processing
of the preceding stimulus (Donchin et al., 1978; Donchin and
Coles, 1988) and that the P300 mediates between stimulus and
response processes (Verleger et al., 2005). Furthermore, dual-
task studies have shown that demanding primary tasks can result
in reduced P300 amplitudes evoked by secondary task stimuli.
This amplitude reduction was interpreted as reflecting resource
reallocation processes between parallel executed tasks (Isreal
et al., 1980a,b; Sirevaag et al., 1989).
The P300 was expected to reflect processing of infrequent
erroneous auditory feedback events in both recording
environments with adequate data preprocessing in the real
driving setup. Specifically, higher P300 amplitudes were expected
for rare incorrect feedback events compared to correct feedback
trials. Furthermore, modulations in P300 amplitudes in the
driving task might index the amount of processing resources
that are needed to perform the driving task. In addition, the
baseline EEG power spectra from both recordings were analyzed
to examine possible tonic differences and to distinguish them
from phasic event-related effects.
2. STUDY 1: LABORATORY SETUP
2.1. Method
2.1.1. Participants
Eighteen participants volunteered for the first study. Three
data sets had to be discarded due to extensive artifacts in the
EEG data. The analyzed sample included 15 healthy adults (10
female, 20–35 years of age, mean 28 years). All volunteers were
right handed as assessed by a German adaptation of Edinburgh
handedness inventory (Oldfield, 1971) and none reported a
history of neurological problems. The study was carried out in
accordance with recommendations of the German Psychological
Society and all participants gave written informed consent in
accordance with the Declaration of Helsinki. At the time of the
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Protzak and Gramann EEG Parameter During Real-World Driving
data recordings, ethics approvals were not required by guidelines
of the Technische Univeristät Berlin.
2.1.2. Experimental Design and Procedure
Participants were seated in front of a 19 ′′ screen for visual
stimulus presentation with their index fingers positioned on the
marked ctrl-buttons on a standard keyboard on a table in front of
them. Auditory feedback was presented through speakers placed
at either side of the screen. A pool of common German first
names with at least two syllables served as the stimulus material.
All names were digitized as auditory feedback cues with Natural
Reading Software (Natural Reading Software, Vancouver, BC
Canada) and used for a simulated dialog between the driver and
a technical speech based input system.
Each trial started with a black and gray flashing display for
800 ms, followed by a grayscreen for 200 ms (Figure 1). Three
randomly chosen names from a pool of 145 forenames were
presented consecutively in black letters on a gray background for
2,000 ms each. In parallel, the same names were read aloud in
their digitized version by a synthesized female voice. Participants
were asked to remember all three names and then speak out loud
the name of the sequence position that was randomly displayed
at the end of the trial (e.g., “two back” indicating to repeat the
second name). A subsequent response interval lasted for 5,000 ms
followed by an auditory repetition of the participant’s response.
In 80 % of all cases the auditory feedback matched the stated
name (eg. “Ella”), while in 20 % of all cases, only the last syllable
(eg. “la”) was replayed. Correct and incorrect feedback trials
were randomly presented in each trial sequence. Participants
were required to wait for a tone after another 1,000 ms to
categorize the feedback. Correct repetitions had to be confirmed
by a button press with the right index finger on the right ctrl-key
and incorrect repetitions had to be indicated by pressing the left
crtl-key using the left index finger.
The task protocol followed a Wizard of Oz procedure where
people believed to interact with a technical system even though
operations were at least partially controlled by a human operator
(cf. Dahlbäck et al., 1993). In the present case, the participants’
spoken responses were not analyzed by an automated speech
recognition system but by the experimenter. The manually
registered response was transferred to the experimental program
to implement a fixed and random error rate in the auditory
feedback. Subsequent interviews revealed that none of the
participants recognized the manipulation. The study consisted of
six blocks of 50 trials each. The entire procedure took 2.5 h on
average.
2.1.3. EEG-Recording and Pre-processing
EEG-data were recorded continuously from 64 active electrodes
(Brainproducts GmbH, Gilching, Germany), mounted in an
elastic cap according to the extended international 10–20 system
(Chatrian et al., 1985), with the exception of positions PO7
and PO8, which were placed below the left and right eye,
respectively, to measure electroocular activity. The data were
digitized with a sampling rate of 1,000 Hz. Prior to data
recordings, impedances were brought below 5 k. Off-line
preprocessing and data analysis were performed in Matlab 2015
(MATLAB, The MathWorks Inc., Natick, MA, USA), using
Eeglab-based routines (Delorme and Makeig, 2004). The data
were filtered with a 0.1 to 100 Hz band pass filter and the
sampling rate was subsequently reduced to 500 Hz. Artifact
contaminated channels (M=11, SD =3.5) were removed
using automatic rejection (5 standard deviations of the mean
kurtosis value or 3 standard deviations from mean probability
distribution of each single channel) and subsequent manual
visual inspection. Afterwards, all channels were re-referenced
to an average reference calculated by the remaining channels.
At this point, two copies were made of each data set. The first
set was filtered with a 1 Hz high pass filter and only used for
independent component analysis (ICA). The second set was
filtered with a 40 Hz low pass filter and used for any further
analysis. Spatially static and maximally temporally independent
components (ICs) were calculated for each participant on the
first set using adaptive mixture independent component analysis
algorithm (AMICA, Palmer et al., 2008) which allows flexible
source modeling for each component by using Generalized
Gaussian density models. The applied AMICA settings included
that one model was trained, three base components densities
were assumed for the mixture models and the number of
rejections of unlikely data samples was set to three. The resulting
ICs weighs were mapped on the 40 Hz low pass filtered sets for the
ERP analysis. ICs representing eye movements were categorized
for each participant (M=3, SD =0.6) by means of scalp maps
and activation time courses. Eye movement activity was removed
from the recordings by removing ocular ICs and subsequent
back-projection to the sensor level.
All resulting data sets were segmented to 1,800 ms epochs,
starting 300 ms before the onset of the auditory feedback.
For each participant, epochs were automatically discarded if
amplitudes exceeded +/−80 µV or if the measured probability
of a trial exceeded a criterion of 6 standard deviations of the
mean calculated probability distribution on a single channel level
or 3 standard deviations for all channels. In total, 2,604 correct
feedback trials (M=174, SD =29.9) and 656 incorrect
feedback trials (M=44, SD =6.1) were considered for the
analysis.
2.1.4. Data Analysis
Averaged correct and incorrect feedback amplitudes were
analyzed relative to a 300 ms pre-stimulus baseline (300 −
0 ms before feedback onset). The P300 time windows and
electrode sites (Fz, Cz, Pz) for analysis were selected based on
the literature (e.g., Johnson, 1993) and visual inspection of the
grand averages. A 100 ms -time window around the most positive
peak at parietal electrode site Pz (738 −838 ms after stimulus
onset) was chosen for P300-analysis. Mean P300 amplitudes were
assessed by 2 ×3 repeated measures of variance (ANOVA) with
the factors feedback type (correct vs. incorrect) and electrode
site (Fz, Cz, Pz). Degrees of freedom were adjusted by means
of the Greenhouse-Geisser method in case of deviations from
sphericicity. Post-hoc t-tests were calculated for each condition
at each electrode to evaluate differences in the topographical
distribution of the measured activations and tested against
correspondent Bonferroni-corrected alpha levels.
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Protzak and Gramann EEG Parameter During Real-World Driving
FIGURE 1 | The stimulus sequence of a trial. The time interval considered for ERP-analysis is framed in red.
2.2. Results
Stimulus-locked ERP-waveforms for incorrect and correct
feedback are shown in Figure 2. ANOVA results for the main
P300 peak time window revealed significant main effects for
feedback type, F(1,14) =59.93, p<0.001, η2
p=0.81 and
electrode site, F(1.24,17.29) =22.11, p<0.001, η2
p=0.61. Mean
P300 amplitudes were significantly higher for incorrect (M=
2.21µV, SD =1.05µV) as compared to correct feedback (M=
0.07µV, SD =0.96µV). Activity for both feedback conditions
increased from frontal electrode site Fz (M= −0.99µV, SD =
2.23µV) toward more posterior sites Cz (M=1.45µV, SD =
1.23µV), t(14) = −5.88, p<0.001, and Pz (M=2.97µV, SD =
0.91µV), t(14) = −5.23, p<0.001. A further increase was
measured from electrode site Cz to Pz, t(14) = −3.02, p=0.009.
A significant electrode ×feedback type interaction, F(2, 28) =
14.65, p<0.001, η2
p=0.51, reflected amplitude differences
between correct feedback trials with lower values at Fz (M=
−0.96µV, SD =2.06µV) compared to Cz (M=0.53µV, SD =
1.44µV), t(14) = −4.41, p=0.001, and compared to Pz
(M=1.29µV, SD =0.62µV), t(14) = −3.63, p<0.003.
Incorrect feedback trials resulted in reduced activity at Fz (M=
−0.71µV, SD =3.01µV) compared to Cz (M=3.17µV, SD =
1.70µV), t(14) = −6.08, p<0.001, and Pz (M=4.85µV, SD =
1.59µV), t(14) = −5.08, p<0.001. Furthermore, incorrect
feedback elicited larger amplitudes than correct feedback at Cz,
t(14) = −6.49, p<0.001, and Pz, t(14) = −9.81, p<0.001, but
not a frontal site Fz, t(14) = −0.44, p=0.665.
2.3. Discussion Study 1
For the laboratory study we used a well-controlled experimental
setup to establish a baseline for the experimental manipulation
in the subsequent driving task. The analysis focused on
the sensitivity of the P300 as an index for the processing
of improbable and erroneous events. As expected, the task
manipulation elicited differences in event-related brain activity
with increased P300 amplitudes for the infrequent incorrect
feedback trials. The analysis revealed a posterior distribution
with most pronounced differences between correct and incorrect
feedback trials over parietal sites. This activation was absent in
trials containing correct feedback information. Similarly, studies
have shown the P300 amplitude to be sensitive to the subjective
probability of an event (Horst et al., 1980) and to errors in picture
sequences (de Bruijn et al., 2007). In our case, the P300 appears
to reflect enhanced processing costs for the categorization of
the less frequent and unintended erroneous feedback events.
This is in line with interpretations of the functionality of the
P300 that claim that the P300 reflects the context updating
within the evaluation process of new events (Donchin and
Coles, 1988). In the present tasks, participants expected to hear
a repeat of their own speech input. Consequently, the large
P300 for incorrect fragmented feedback most likely displayed
the memory update after the mismatch between the anticipated
and received feedback. Moreover, as the less often incorrect
feedback required a different manual button press, deviating
response requirements might also be depicted by these changes
(Verleger et al., 2005). The results from Study 1, confirmed our
approach for investigating P300 activity for rare and deviant
auditory feedback. Consequently, the procedure was applied in
the following in-car recordings.
3. STUDY 2: DRIVING SETUP
Study 2 was conducted in a real driving setting to test whether
human brain dynamics reflective of deviance detection can
be recorded while participants actively drive a car. The same
task as in the laboratory recordings was used to allow a
direct comparison. Data processing procedures were guided by
laboratory study routines reported in section 2.1.
3.1. Method
3.1.1. Participants
Seventeen participants volunteered in the second study. Data
from one participant had to be excluded from analysis due
to technical problems during data recording, and data from a
second participant had to be removed due to insufficient data
quality. The analyzed sample included 15 adults (10 female, 22–
36 years of age, mean 28 years,). All participants held a valid
driver license for at least 2 years. As in Study 1, all volunteers
complied with the requirements and were tested under the same
conditions. None had participated in Study 1.
3.1.2. Experimental Design and Procedure
Participants performed the same audio feedback task with
identical stimulus material and time course as described in Study
1 (see section 2.1.2). Only set-up modifications for the in-car
realization are described here. The driving tests took place on
a part of a restricted runway (length: approx. 1.5 km) of a
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Protzak and Gramann EEG Parameter During Real-World Driving
FIGURE 2 | Topographic plots for four time windows (top) and ERP traces (bottom) for incorrect and correct feedback trials in Study 1. The time windows used for the
upper plots are highlighted in gray in the ERP traces.
former military airfield in Brandenburg, Germany (Figure 3). A
gear shift Volkswagen Touran was provided as test vehicle by
the Department of Human-Machine Systems, TU Berlin. Audio
feedback was transmitted through portable speakers located in
the front interior. Names were presented on a 7.6 ′′ TFT-display,
mounted on the central console. Two buttons were added to the
steering wheel, in a convenient position that allowed for safe
steering and button presses with the left and right thumb.
A blocked 1.2 km section of the runway served as the test track
(Figure 3). All participants had time to familiarize with the car
before maneuvering the car to the starting position at the head of
the test track. Test blocks were defined by driving the test track
twice back and forth (= 4.8 km). Participants were instructed to
accelerate the car to 40 km/hand to shift into the fourth gear at
the beginning of each run. Speed and gear had to be maintained
until the end of the test track was reached (indicated by a pylon).
Behind that point, the car had to be turned around and accelerate
again for the next test run in the reversed direction. Throughout
each block, the experimental task was only started when speed
was within a range of 40 km/h +/−3km/h (monitored via
Control Area Network Data). For economic reasons and to
keep up alertness, task blocks alternated with blocks in which
participants worked on an acceleration and braking task, not
reported here. The total number of completed test blocks differed
individually (range 12–14 blocks and 80–120 trials) dependent
on weather and the participant’s individual condition.
FIGURE 3 | Test track from participants’ perspective (Left), schematic driving
course (Right).
3.1.3. EEG Recording and Preprocessing
The EEG recording setup and preprocessing steps followed the
protocol for the laboratory recordings. Data were recorded with
64 active electrodes digitized with a sampling rate of 500 Hz.
Impedances were kept below 5 k. All data sets were offline
filtered with a high-pass filter of 0.1 Hz and a low-pass filter
of 40 Hz. Again, after automatic and visual inspection artifact
contaminated channels were discarded (M=9, SD =3.1)
and the remaining channels were re-referenced to an average
reference. As in Study 1, two copies were made of each
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Protzak and Gramann EEG Parameter During Real-World Driving
accordingly preprocessed data set. The first set was filtered with
a 1 Hz high pass filter and only used for independent component
analysis. The second data set was filtered with a 40 Hz low pass
filter and used for any further reported analysis. The calculated
IC weigths were map on the 40 Hz low pass filtered sets and ICs
representing eye movements (M=4, SD =1.1) were removed.
The resulting data were back projected to the channel level. Trials
from the epoched data sets were automatically rejected if any
channel contained amplitudes that exceeded +/−80 µV. Slightly
broader probability criterions (6 SD on single channel level and
3SD for all channels) were applied for the automated rejection
based on deviation from the mean probability distribution to
adapt to the generally more fluctuating data quality of the in-car
recordings. In sum, 1,222 correct trials (M=81, SD =20.0)
and 296 incorrect trials (M=20, SD =5.5) were considered for
analysis.
3.1.4. Data Analysis
Activity at midline electrodes Fz, Cz, and Pz were averaged for
correct and incorrect feedback trials and, respectively, calculated
in relation to a 300 ms baseline time window preceding the
auditory feedback onset. For the analysis of amplitude differences
the 100 ms time widow (852 −952 ms) around parietal peak
activity was specified. Mean amplitudes in the P300 time-window
were subjected to a 2 ×3 ANOVA with the factors feedback type
(correct vs. incorrect) and electrode site. Greenhouse-Geisser
corrections were applied and Bonferroni-corrected t-tests were
calculated for post-hoc comparisons of factor levels.
3.2. Results
The analyses of mean amplitude values revealed a main effect
for the factor feedback type, F(1,14) =31.67, p<0.001, η2
p=
0.69, with higher amplitudes for incorrect feedback (M=
2.04µV, SD =1.81µV) compared to correct feedback (M=
−0.75µV, SD =1.08µV). The main effect for electrode site,
F(1.40,19.61) =2.55, p=0.117, η2
p=0.15, and the interaction
effect of feedback type ×electrode site, F(2,28) =0.84, p=
0.444, η2
p=0.06, were not significant (Figure 4).
3.3. Discussion Study 2
In Study 2, we tested whether the results from Study 1 could be
replicated when the identical task had to be accomplished during
real driving. As in the laboratory assessment, incorrect feedback
elicited larger amplitudes in the P300 time window compared
to correct feedback. Although ongoing parallel cognitive and
motor processes are needed to solve the driving task, differences
in neural response patterns for regularities and discrepancies in
auditory feedback could be replicated.
In contrast to Study 1, no significant topographic variations
in P300 amplitudes over midline electrodes were found. This
activity pattern might be explained by the enhanced complexity
of the driving task. Frontal P300 activity as an index of an
orienting response has been reported to be dependent on time
on task and to diminish with habituation (e.g., Courchesne,
1978, for a review see Friedman et al., 2001). However, less
pronounced reductions in frontal activity were found for more
complex tasks (Segalowitz et al., 2001). The broad grand average
waveform activation pattern including frontal activity in the
driving scenario might be due to the fact that the driving task
counteracted habituation effects in the secondary task. As the
driving task required constant attention, fewer resources might
have been available for obtaining automated processes in the
auditory secondary task.
4. COMPARISON OF RECORDING
ENVIRONMENTS
For comparison with the data recorded in Study 1, additional
data analyses were performed to answer two main questions:
(1) Do changes in EEG dynamics depend on the recording
environment (lab vs. car)? (2) Is there a interaction between
recording environment and feedback type (incorrect vs. correct)?
A main effect of feedback type should be observed irrespective
of the recording environment if EEG-recordings in a driving
car reliably measure brain dynamics. A main effect of recording
environment would indicate an impact of the recording
environment on P300 amplitudes, possibly reflecting decreased
data quality due to in-vehicle artifact sources and movement of
participants. Importantly, the absence of an interaction effect
would indicate that the recording, analysis, and interpretation
of EEG data in realistic driving scenarios is feasible for this
particular task.
4.1. Data Analysis
Comprehensive analysis on both data sets recorded within
the two recording environments were calculated. Differences
in data characteristics in terms of trial amount for both
recording environments were addressed. Tonic differences in
power spectrum density (µV2/Hz) at midline electrode sites
(Fz, Cz, Pz) were analyzed for the theta band (4 −7 Hz) and
alpha band (8 −12 Hz). Power spectrum density estimates were
calculated using Welch’s method with windows of 256 points
length, zero padded to 512 points and no overlap. Mean density
values were assessed for both frequency bands by a 2 ×3 ANOVA
with factors recording environment (lab, car) and electrode site
(Fz, Cz, Pz). Event-related amplitude differences were assessed by
a 2x2x3 mixed design ANOVA with the between factor recording
environment (laboratory vs. car) and the within factors feedback
type (correct vs. incorrect) and electrode site.
4.2. Results
4.2.1. Data Characteristics
In total, significantly more trials were recorded in the lab
environment (3,795 trials) than in the driving environment
(2,185 trials), t(21.16) =14.11, p<0.001. Furthermore,
the proportion of trials rejected by automated cleaning was
significantly higher, t(28) = −2.84, p=0.08, for epochs extracted
from the driving study (28.89%) compared to the lab recordings
(14.11%). Therefore, more trials were considered for analysis of
the laboratory data (M=217 trials per person, SD =32.61)
compared to the driving study data (M=102 trials per person,
SD =23.30), t(28) =11.05, p<0.001.
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Protzak and Gramann EEG Parameter During Real-World Driving
FIGURE 4 | Topographic plots for four time windows (Top) and ERP traces (Bottom) for incorrect and correct feedback trials in Study 2. The time windows used for
the upper plots are highlighted in gray in the ERP traces.
4.2.2. Theta and Alpha Band Power
The analysis of the power in the theta frequency band
revealed a significant main effect for the factor electrode site,
F(2,56) =40.76, p<0.001, η2
p=0.59 (Figure 5). Higher
theta frequencies, t(29) =7.33, p<0.001, were measured
at frontal electrode site Fz (M=1.76µV2/Hz, SD =
0.69µV2/Hz) compared to the central electrode site Cz (M=
0.98µV2/Hz, SD =0.44µV2/Hz) and compared to parietal site
Pz (M=1.02µV2/Hz, SD =0.45µV2/Hz), t(29) =6.90, p<
0.001. No difference, t(29) = −0.45, p=0.656, was found for
theta frequencies at electrode site Cz compared to Pz. No main
effect, F(1,28) =0.22, p<0.642, η2
p=0.01, or interaction effect,
F(2,56) =2.35, p<0.105, η2
p=0.08, were found for the factor
recording environment.
With respect to the power in the alpha frequency range, a
significant main effect of electrode site was observed, F(2,56) =
13.30, p<0.001, η2
p=0.32. This effect can be explained
by reduced spectral power at central electrode sites (M=
0.64µV2/Hz, SD =0.38µV2/Hz) as compared to frontal
electrode sites (M=1.02µV2/Hz, SD =0.51µV2/Hz),
t(29) =4.41, p<0.001, and parietal electrode sites (M=
0.97µV2/Hz, SD =0.54µV2/Hz), t(29) = −4.12, p<0.001.
No difference, t(29) =0.62, p=0.539, was found for alpha
frequencies at electrode site Cz compared to Pz. Again, no main
effect, F(1,28) =1.90, p<0.179, η2
p=0.06, or interaction effect,
F(2,56) =1.58, p<0.217, η2
p=0.05, were found for the factor
recording environment.
4.2.3. ERPs
The comparison analysis on both data sets revealed significant
main effects for the factor feedback type, F(1,28) =75.60, p<
0.001, η2
p=0.73, and electrode site, F(1.36,38.02) =17.40, p<
0.001, η2
p=0.38. Mean P300 amplitudes elicited by incorrect
feedback (M=2.13µV, SD =1.46µV) were more pronounced
compared to correct feedback (M= −0.34µV, SD =1.09µV).
Activity at Fz (M= −0.59µV, SD =2.16µV) was lower than
activity recorded at Cz (M=1.04µV, SD =1.64µV), t(29) =
−4.76, p<0.001, and Pz (M=2.24µV, SD =1.73µV),
t(29) = −4.53, p<0.001 and lower at Cz compared to Pz,
t(29) = −2.60, p=0.015.
The main effects were qualified by a significant interaction of
the factors feedback type and electrode site, F(2,56) =7.84, p=
0.001, η2
p=0.22, revealing highest P300 amplitudes for correct
feedback at Pz (M=0.62µV, SD =1.60µV) compared to
Fz (M= −1.09µV, SD =2.03µV), t(29) = −3.22, p=
0.003. Incorrect feedback elicited lower P300 amplitudes at Fz
(M= −0.07µV, SD =2.84µV) compared to Cz (M=
2.71µV, SD =2.84µV), t(29) = −5.37, p<0.001, and Pz
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Protzak and Gramann EEG Parameter During Real-World Driving
FIGURE 5 | Mean Power density (y-axis, in µV2/Hz) in theta (4 −7 Hz, left two graphs) and alpha band (8 −12 Hz, right two graphs) at midline electrodes (x-axis, Fz,
Cz, Pz). Scatter points indicate individual mean values for each participant at each electrode.
(M=3.95µV, SD =2.23µV), t(29) = −4.72, p<0.001.
Amplitudes for incorrect feedback were larger than for correct
feedback at all three electrode sites, Fz: t(29) = −2.81, p=0.009,
Cz: t(29) = −5.05, p<0.001, Pz: t(29) = −11.30, p<0.001.
A marginal significant main effect of recording environment,
F(1,28) =3.42, p=0.075, η2
p=0.11, with higher amplitudes in
the laboratory study (M=1.14µV, SD =0.87µV) compared to
the driving study (M=0.65µV, SD =1.14µV) was specified by
an again marginal interaction effect of recording environment x
electrode site, F(2,56) =3.09, p=0.053, η2
p=0.10. A tendency
toward larger amplitudes at electrode site Pz was found for the
laboratory study (M=2.97µV, SD =0.91µV) compared to the
driving study (M=1.50µV, SD =2.05µV), t(19.30) =2.55, p=
0.019. No significant effects were found for the interaction of
feedback type ×recording environment, F(1,28) =1.26, p=
0.271, η2
p=0.04, and feedback type ×electrode site ×recording
environment, F(2,56) =1.94, p=0.153, η2
p=0.07.
4.3. General Discussion
Two studies were conducted to establish an experimental
protocol for systematically comparing the neural responses
elicited by unexpected erroneous events within a realistic
driving setting. In the first study we tested our experimental
manipulation successfully by provoking the well-known P300
deflection for the processing of infrequent but task-relevant
auditory events (Sutton et al., 1965; Katayama and Polich, 1996).
In a second study, the same test was carried out in a real driving
scenario, replicating the P300 response observed in the first
study.
While the results demonstrate that it is feasible to investigate
the neural dynamics underlying incorrect feedback processing
in both scenarios, general differences in data characteristics
had to be addressed for a more specific comparison. Despite
clear visual similarities in mean ERP traces from both
acquisitions (shown in Figure 6), higher variance was observed
in the data recorded in the car. As a real-life driving
scenario is an inherent source of technical artifacts and active
behavior, differences in signal quality are not unexpected.
This was confirmed by a significantly higher number of trials
subject to automated artifact rejection due to amplitudes
that exceeded a criterion of +80 µV or deviated clearly
from the mean calculated probability distribution. Moreover,
the more complex and time consuming preparation and
acquisitions sessions in the driving setup led to generally
shorter recording times. These two factors accounted for a
significant lower number of trials for the in-car recordings.
Furthermore, the introduction of a perceptual demanding
additional driving task could have influenced P300 amplitudes
(Wickens et al., 1983) and increased the variance in the
driving condition. However, clear P300 deflections for incorrect
feedback events were observed, as before in the laboratory
assessment. To allow a more direct comparison, further analyses
in the time and frequency domain were computed with both
data sets. While a clear P300 component associated with
the processing of infrequent and task-relevant stimuli was
successfully replicated under realistic driving conditions, no
significant main or interaction effect of the factor recording
environment on P300 amplitudes was revealed by the analysis.
Furthermore, the general impact of data quality in the
different recording environments on the P300 deflection was
addressed by analysis in the frequency domain. Tonic power
spectra in both recordings were comparable and again, no
significant effect of the factor recording environment was
found.
Following the argumentation on a reciprocal relationship
between task difficulty of the primary task and P300 amplitudes
in the secondary task (e.g., Sirevaag et al., 1989) we suggest that
the task load introduced by the driving task was not sufficient
to produce a significant effect. Thus, keeping track and speed
on a straight and blocked course appears to demand little
attentional resources. However, marginal significant differences
between overall amplitudes in both recording environments
and a marginal significant interaction effect of recording
environment and electrode site indicate potential primary task
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Protzak and Gramann EEG Parameter During Real-World Driving
FIGURE 6 | ERP-traces (in µV, y-axis and time in ms, x-axis) from the laboratory (left column) and driving (right column) studies at midline electrodes Fz (Top), Cz
(Middle), and Pz (Bottom). Mean amplitude courses for correct feedback are green and for incorrect Feedback they are red. A 95%-confidence interval for each
condition is indicated by the surrounding envelope in the corresponding color.
resource demands. Mean amplitudes tended to be smaller at
parietal electrode sites while driving compared to the laboratory
assessment. This tendency might reflect the reallocation of
resources that were needed to accomplish the driving task. We
expect more complex driving tasks to result in a pronounced
reduction of P300 amplitudes. In addition, future studies might
also consider a within-subject design to control for possible group
differences that could have caused the obtained marginal effects
of the different recording environments on parietal activity.
In sum, our approach showed that P300 amplitudes elicited
by unexpected and erroneous events can be assessed during
the performance of an unhindered driving task. Once more,
the feasibility of EEG measurements beyond more or less
restricted standard laboratory settings with new application-
oriented approaches was demonstrated in this study (e.g., Gwin
et al., 2010; Debener et al., 2012; Jungnickel and Gramann, 2016).
Based on these results, more complex dual-task paradigms with
varied difficulty levels in either the primary driving task or in the
secondary task can be addressed. This will be of importance for
further research in autonomous driving and for the development
of driving assistance by providing insights into the driver’s
processing of incoming information while interacting with the
car and the surrounding environment. Thus, systematic analysis
on variations in different stages of information processing could
be used for more direct driver state assessments and the design of
adaptive assistance.
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Protzak and Gramann EEG Parameter During Real-World Driving
5. CONCLUSION
With two studies we were first able to replicate previous
laboratory- based work on P300 amplitudes and then to
confirm a high level of ecological validity of our results in
a realistic driving task setting. Our findings provide strong
evidence that complex cognitive functions like context and
response updating processes can be examined in a highly artifact
prone driving environment. The processing of infrequent and
incorrect auditory feedback events was reflected by clear P300
deflections with slightly different topographical distributions
in both recordings. Despite differences in data quality and
variance, amplitudes and tonic EEG power spectra from both
studies were comparable and not significantly affected by the
factor recording environment. The possibilities to provide direct
insights into brain dynamics of humans participating in a
real world driving task provides compelling arguments for
further investigation in realistic task settings with more complex
manipulation or on less robust potentials. A gradual transfer
of the extensive knowledge gathered from laboratory ERP
reports into ecological task settings could prospectively result
in complex findings about brain dynamics of actively behaving
humans.
AUTHOR CONTRIBUTIONS
JP carried out the experiment. JP and KG contributed to the
analysis and interpretation of the data. JP and KG wrote the
manuscript.
ACKNOWLEDGMENTS
The present project was embedded in the Research Training
Group prometei - Prospective Design of Human-Technology
Interaction founded by the German Research Foundation (DFG)
and the data recordings were parts of a dissertation project
(Protzak, 2015). A preprint version of this article (Protzak and
Gramann, 2018) was posted on the bioRxiv.org pre-print server.
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