ORIGINAL RESEARCH
published: 28 February 2017
doi: 10.3389/fnhum.2017.00078
Frontiers in Human Neur oscience | www .fr ontiersin.org 1 February 2017 | V olume 11 | Article 78
Edited by:
Mikhail Lebedev ,
Duke University , USA
Reviewed by:
Nima Bigdely-Shamlo,
Qusp, USA
Tjeerd W . Boonstra,
University of New South Wales,
Australia
T omas Emmanuel Ward,
Maynooth University , Ireland
*Correspondence:
Thorsten O. Zander
[email protected]
Received: 08 January 2016
Accepted: 08 February 2017
Published: 28 February 2017
Citation:
Zander TO, Andreessen LM, Berg A,
Bleuel M, Pawlitzki J, Zawallich L,
Krol LR and Gramann K (2017)
Evaluation of a Dry EEG System for
Application of Passive
Brain-Computer Interfaces in
Autonomous Driving.
Front. Hum. Neurosci. 11:78.
doi: 10.3389/fnhum.2017.00078
Evaluation of a Dry EEG System for
Application of Passive
Brain-Computer Interfaces in
Autonomous Driving
Thorsten O. Zander 1, 2 * , Lena M. Andreessen 1, 2 , Angela Berg 1 , Maurice Bleuel 1 ,
Juliane Pawlitzki 1 , Lars Zawallich 1 , Laurens R. Krol 1, 2 and Klaus Gramann 1, 3
1 Biological Psychology and Neuroergonomics, T echnical University of Berlin, Berlin, Germany , 2 T eam PhyP A, Biological
Psychology and Neuroergonomics, T echnical University Berlin, Berlin, Germany , 3 Center for Advanced Neurological
Engineering, University of California San Diego, San Diego, CA, USA
W e tested the applicability and signal quality of a 16 channel dry electroencephalography
(EEG) system in a laboratory enviro nment and in a car under controlled, r ealistic
conditions. The aim of our investigation was an estimation how well a pa ssive
Brain-Computer Interface (pBCI) can work in an autonomous driving scenario. The
evaluation considered speed and accuracy of self-applicability by an untrained person,
quality of recor ded EEG data, shifts of electr ode positions on the head after
driving-r elated movements, usability , and complexity of the system as such and wearing
comfort over time. An experiment was conducted inside and outside of a stationary
vehicle with running engine, air -conditioning, and muted r adio. Signal quality was
suf ficient for standard EEG analysis in the time and fr equency domain as well as for
the use in pBCIs. While t he influence of vehicle-induced interferences to data quality
was insignificant, driving-related movements led to stron g shifts in electrode positions. In
general, the EEG system used allowed for a fast self-applicability of cap and electrodes.
The assessed usability of the system was still acceptable while the wearing comfort
decr eased strongly over time due to friction and pr essur e to the head. Fr om these r esults
we conclude that the evaluated system should pr ovide the essential requ irements for
an application in an autonomous driving context. Neve rtheless, further refinement is
suggested to r educe shifts of the system due to body movements and increase the
headset’ s usability and wearing comfort.
Keywords: autonomous driving, passive BCI, EEG, usability, ERP
INTRODUCTION
Driving has become a part of e veryday life, which makes the drive to work or for recre ational
activities a simple routine task. However , the effects of the ment al workload and effort required
by driving often go unnoticed. A study by Borghini et al. (2014) found that mental workload,
fatigue, and drowsiness are significantly increased while driving. Especially long periods of constant
driving, as often performed by professional truck drivers, result in an accumulation of these effects
over time, decreasing the driver’ s cognitive capabilities and driving performance, thus increasing
the chances of traffic accidents.

Zander et al. Passive BCI for Autonomous Driving
The field of automotive human factors and ergonomics is
concerned with minimizing safety risks depending on human
performance in driving tasks. Today, many automations and
small devices have found their way into cars in order to help
reduce the mental workload required to operate t he vehicle
( Young and Stanton, 1997; T adaka and Shimoyama, 2004; Ma
and Ka ber, 2005 ). A different approach aims to fully or at least
partly automate the task of driving, so the human driver can be
eliminated as a risk factor in most instances. The scientific field
working toward this goal is called Autonomous Dr iv ing ( Franke
et al., 1998 ) and has grown more important over th e past years.
One particular problem with autonomous driving is t he
question of responsibility: Who is accountable in case of an
accident? Most countries still define the human driver of a car
as the entity responsible for anything that happens while driving
( Beiker, 2012 ). Therefore, experts believe it would be best to only
automate some of the tasks t hat arise while driving, but to leave
the most complex tasks to a human driver for t he time being.
A ccording to Sukthankar et al. (1997) , the task of driving consists
of different levels, which are the strategic le vel (route planning),
the tactical le vel (maneuver selection), and t he operational level
(maneuver operation). Automation of the lowest, operational
level is thus le gally the least complex, and also technically possible
( Dickmanns and Z app, 1987; Pomerleau, 1992 ). Driving along
a highway could relatively easily be automated, but once the
traffic situation changes, the human may be required to take
over control. This approach thus requires an important exchange
of information between the human driver and the automated
system: The human must be timely and appropriately informed
of the pending takeover. As stated by Llaneras et al. (2013) , people
tend to focus their attention on secondary tasks once the primary
objective of driving has been taken over by automation. As a
consequence, in a situation where the car drives autonomously,
a signal given by the system to indicate t he necessity for takeover
might be missed, or might catch the human by surprise. This may
result in loss of control over the vehicle.
As a solution to the above problem, the car could monitor
the driver’ s ment al state, and adapt the notifica tion process
to the current context. A completely attentive driver mig ht
quickly perceive and under stand e ven simple signals, whereas
for example a sleeping driver may need to be woken carefully
by the car in advance of leaving the highway. P assive br ain-
computer inter faces (passive BCIs, Z ander and Kot he, 2011 )
are promising approaches for such monitoring and automated
adaptation ( Z ander et al., 2011 ). This te chnology enables real-
time detection of mental conditions lik e fatigue, workload, and
degree of relaxation ( Blankertz et al., 2010; Gerjets et al., 2014 ),
which offer a good estimate of whether or not the driver is
ready to take over control of t he car. But the passive B CI
approach during autonomous driving is not limited to this. More
general information—like mood or situational awareness—and
also very specific information about the subjective interpretation
of the current context—that might be reflected in the driver’ s
brain as error responses—could be assessed by the passive
B CI ( Z ander and J atzev, 2012 ). This information could then
be integrated in the autonomous deci sions of the car. The
car learns how the driver interprets t he context and gains a
degree of context-awareness by utilizing the driver ’ s brain as a
sensor.
P assive B CIs are commonly based on elec troencephalography
(EEG). Traditional EEG systems are relatively cumbersome to
apply and use, requiring preparation of the skin, application of
conductive gel, and cleaning of the c ap afterwards. To make
EEG applicable for non-scientific uses, e.g., to be used by drivers,
its application and handling needs to be as easy as possible.
This is why alternative electrode systems (e.g., des cribed in
Z ander et al., 2011; Liao et al., 2012 ) are an important focus
of autonomous driving related B CI research. Primarily, the use
of gel is eliminated, and the caps containing the e lectrodes are
made for quick application, resulting in less preparation time
and, ideally, more comfortable for the wearer. Recent laboratory
studies provided evidence of good signal quality, comparable to
that of standard gel-based ele ctrodes. It is still unclear howe ver
that the signal quality can be maint ained in real-world contexts.
This study focused on evaluating the use and applica tion
of a dry electrode EEG system in the context of a running
vehicle. It was assessed how easy it is for untrained person to
apply the system on their own he ad, how well the electrodes
can be positioned and remain in place, and whether the signal
quality is sufficient for B CI usage when the system is self-applied.
Two common features in the EEG, an N200-P300 ERP and t he
parietal alpha rhythm, were analyzed as examples of signals that
potentially can be used in a passive B CI applic ation. Furthermore,
interference in the EEG signal resulting from usage inside a
running car —a noisy environment—was investigated. Finally,
wearing comfort over a prolonged period of time as well as
general user acceptance were evaluated.
MA TERIALS AND METHODS
Participants
Ten participants, five male, participated in the experiment. The
mean age was 28 years ( SD = 3.4). Two participants reported to
ha ve sensitive skin. All participants gave their written informed
consent to participate in the study and were paid 20 euros as
expense allowance. The overall duration of the experiment was
on a verage 165 min ( SD = 39 min.), including breaks.
Apparatus
V ehicle
The vehicle we used to evaluate the influence of vehicle-
induced noise on the recorded EEG was a Volkswagen Touran
(year of manufacture 2003). The car was st ationary during the
experiments, but had the engine running, the radio switched on
(though muted), and the air conditioning enabled. A 7.6 ′′ TFT -
display was mounted to the right of the steering wheel ne ar the
center console.
Experimental Room
The experimental room used for baseline recordings was a non-
frequented room at the TU Berlin with constant lig ht, right next
to the parked car. Diversions and disturbances were kept to a
minimum.
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Zander et al. Passive BCI for Autonomous Driving
FIGURE 1 | Overview of the used EEG system, the Brain Products
actiCAP Xpress. Image courtesy of Brain Pr oducts GmbH.
Computer System
The EEG system was connected to a laptop (Sony V aio Z, 2012)
and EEG data was recorded using t he Bra inV ision Recorder ,
Br ainV ision RDA (Brain Products GmbH, Munich, Germany),
and La bRecorder (as part of the B CIL AB framework, Delorme
et al., 2010 ). The experimental paradigms were run using SNAP 1
( Iversen and M akeig, 2013 ). To analyze the dat a, we used the
EEGLAB toolbox ( Delorme and Makeig, 2004 ), an open source
toolbox embedded in MA TLAB. For clas sification we used the
open source toolbox BCIL AB ( Kothe and Makeig, 2013 ), also
embedded in MA TLAB.
EEG System
The system examined in this study was the Bra in Products
actiCAP Xpress dry-electrode EEG system (see Figure 1 )
provided by Brain Products GmbH for the duration of t he
experiment. The system included 16 active data ele ctrodes plus
one reference and one ground electrode. Electrodes were applied
to one of two differently-sized flexible caps, depending on the
head cir cumference of the participant (52–58, or 58–64 cm). To
ensure fixation on the participant’ s head, a chin belt was att ached
to the cap. E ach cap provided 78 possible electrode positions
most of the extended international 10% system, with additional
options to set up regions of interest. We used electrode positions
Fp1, Fp2, Fz, FC5, FC6, C3, C4, Cz, CPz, Pz, CP5, CP6, PO3, PO4,
POz, and Oz.
To adjust the system to an individual participant, the
electrodes can be extended to different shapes and sizes by
attaching so-called QuickBits (see F igure 2 ). The kit used in
the study came with six T -shaped flat tips (with a diameter of
7 mm) to be attached to the forehead and earlobes, as well as
32 mushroom-head tips for application on the s calp. These latter
come in different lengths of 8, 10, 12, and 14 mm , which can be
attached to the electrodes according to head shape and required
pressure. This enabled a per sonalization of the system: Optimal
1 Simulation and Neuroscience Application Platform (SNAP). A vailable:
https://github.com/sccn/SN AP.
FIGURE 2 | The differ ent QuickBit types provided with the actiCAP
Xpress. Image courtesy of Brain Pr oducts GmbH.
sensor lengths for electrode positions can be noted, stored and
re-applied in follow-up experiments.
Prior to applying the actiCAP Xpress , the electrodes were
cleaned using a disinfect ant spray. This was done even in case
the electrodes and sensors had not been used before to remove
dust and particles to improve connectivity.
The electrode array was connected to a V -Amp EEG signal
amplifier (Brain Products GmbH, Munich, Germany), which in
turn was connected to a laptop computer throug h a universal
serial bus (USB) 2.0.
Experimental Pr ocedur e
Experimental Rationale
This study was designed to assess different requirements to an
EEG system for application in real-world driving s cenarios. We
defined the following requirements: (1) self-applicability of the
system, (2) impact of interfering noise signals inside a running
vehicle on EEG signal quality, (3) stability of cap and electrode
positions after context-related movements, and (4) usability and
wearing comfort of the system.
The experiment was divided into four blocks covering these
four issues, answering the following questions.
1. How easy and accurate is self-application of the system in
comparison to application by another person? (Block I)
2. How strong is the effect of interfering signals in a running car
on EEG recording? (Block II)
3. How do electrode positions change during typical body
movements inside a car? (Block III)
4. How do participants rate the system’ s usability? (Block IV)
Figure 3 summarizes the experiment al session. After arrival of
the participant, the experiment was explained and a demographic
sur vey was conducted. While t he cap was personalized by the
investigator by exc hanging electrode tips where necessary, t he
participant was asked to read the instruction manual of the
system, in preparation for Block I.
Block I: Self-application
Self-application of the cap, as opposed to having the cap fitted to
you by a trained operator , may take a different amount of time
and may affect the positioning of the electrodes and the si gnal
quality. To estimate these effects, we compared cap a pplication
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Zander et al. Passive BCI for Autonomous Driving
FIGURE 3 | Experiment timeline.
in two conditions: Application by the experimenter , and self-
application by the participant. Customization of t he cap was not
included here, as it is assumed to be a one-time effort.
P articipants were seated in t he experimental room, in front of
a laptop. A stopwatch was used to first measure the time required
by the experimenter to apply the EEG cap to the participant’ s
head.
Once the cap and ground/reference electrodes were in place,
electrode positions were measured using the Polar is V icra system
(Northern Digital Inc., Waterloo, ON, C anada), allowing for
measuring 3-dimensional electrode locations. We chose to record
the 16 electrode positions, as well as t he inion, the nasion and
the left and right pre auricular points. The latter three were
used as coordinate references to allow the transformation of
coordinates taken from different measuring sessions into one
coordinate system to allow comparison (described below in the
section “Analysis Procedures ”). To achie ve comparable, stable
positions for the reference points in each measurement during
the experiment, we marked them by a small dot on the respective
positions on the participant’ s skin using a removable eudermic
marker.
Following this, signal quality was optimized by relatively
fine-grained adjustments to the electrodes. As the system did
not provide an objective measure of signal quality or electrode
contact (e.g., impedance), signal quality was assessed visually.
The signal was monitored using the Bra inV ision Recorder
software, with all 16 channels displayed at once, set to a resolution
of 50 µ V. A dis play filter was enabled, bandpass-filtering the
visible signal from 0.1 to 40 Hz, not affecting the recording. The
duration of this optimization was again timed using a stopwatch.
The resulting signal quality was also recorded, as rated by the
experimenter. The indication for signal quality was the visual
form of the signal on the display, artifacts had to be recognized
visually. The rating followed predefined guidelines and was done
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Zander et al. Passive BCI for Autonomous Driving
on a 5-point scale with 5 me aning “perfect signal” and 1 me aning
“no signal at all” (see Figure 4 ). This rating was done twice: Once
for the signals with the display filter switched on, and once based
on the unfiltered raw signal.
Following this, the cap was removed and participants, who
read the instructions manual, were asked to put on t he cap by
themselves, after all of their questions about the procedure had
been answered by the experimenter. Application time was again
measured, as were the electrode positions and t he resulting signal
quality.
Block II: EEG Recor ding
For investigating signal quality in standard EEG analyses we
chose the well-known N200 and P300 components of the visual
event-related potential and the pariet al alpha rhythm. Both
time- and frequency domain parameters are well-examined
phenomena in EEG research. Hence , clear expect ations about
morphology, topography and signal strength can be drawn, that
build the baseline of comparison for our results.
In order to assess the EEG signal and the possible influence
on it of the electromagnetically noisy environment t hat is the
car , participants performed in two established experimental
paradigms of B CI research ( Zander et al., 2011 ), once in t he
experimental room, and once inside the c ar. The order of these
two conditions was randomized between participants.
The first paradigm focused on the elicitation of visual e vent-
related potentials (ERP s) using a standard oddball approach: An
infrequent deviant stimulus sometimes appe ared instead of the
frequent st andard stimuli ( Duncan-Johnson and Donchin, 1977 ;
see Figure 5 ). This is a common approach when resear ching
ERP s referred to as the N200-P300 complex ( Polich and Kok,
1995; Linden, 2005 ). ERP detection during autonomous driving
can be useful, as they may allow a car to detect how drivers react
cognitively to perceived stimuli/information.
On the screen, participants sa w a circle divided by lines into
30 ◦ angles. First, a bar appeared, like a clock ’ s arm pointing 12
o’ clock. This bar t hen rotated clockwise in discrete steps, once
every second. A standard stimulus had it rot ate by 90 ◦ ; a deviant
consisted of an initial 60 ◦ rotation, followed by a 100 ms pause
and a 15 ◦ counterclockwise rotation. After each deviant, the bar
disappeared and reappe ared at the 12 o’ clock position.
10% of all stimuli were deviants. In total 400 trials were
displayed (360 standard, 40 deviant).
The second paradigm focused on features in t he spectral
domain, specifically the pariet al alpha rhythm. This feature is
of special interest to autonomous driving, as pariet al alpha can
be used as an indicator of whether t he participant is currently
in a relaxed state or performing some ment ally demanding task
( Berka et al., 2007 ). It also is a standard example for fe atures in
the spectral domain.
The paradigm (see Figure 6 ) presented to the participant
was designed to induce changes in parietal alpha activity by
alternating between two states of mind: Engaged and relaxed .
To engage the participant, a six-letter word was presented letter
FIGURE 4 | Examples for signal quality ratings on a scale from one to five. Gr een colored parts indicate adequate signal quality , yellow parts moderate signal
quality , and red parts unacceptable signal quality .
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Zander et al. Passive BCI for Autonomous Driving
FIGURE 5 | Oddball Paradigm.
FIGURE 6 | Induced Alpha Paradigm.
by letter , with letters appearing on random locations on the
screen amidst visual noise. E ach letter was only visible for
1 s. Participants were instructed to read the word. After e ach
engagement trial, the participant was instructed simply to relax
for 6 s with their eyes open. This relaxation phase was introduced
using an auditory signal and ended by a similar one with lower
pitch.
There were 32 trials of each condition. The order of words in
the engaged condition was randomized across participants.
These two paradigms were presented in fixed order to the
participants in the two conditions (room vs. car).
Block III: Driving-Related Movements
The third block investigated the influence of movements on the
position of the electrodes.
Electrode positions were recorded, using again the Polaris
system mentioned earlier , at the st art of this block. Participants
then performed a series of three different types of driving-
related movements inside the car , and the ele ctrode positions
were measured again after each group of movements. Because
measurements were not done inside the car but in a ne arby
room, some walking was required. Electrode c ables were
bundled together and fixed to t he participant ’ s clothing in
a relaxed way to minimize their strain on the c ap while
walking.
To make movements comparable between participants, we
placed markers (sticky notes) at certain places in the car : One
on the left rear window, one above the driver’ s se at, one in
the legroom of the front passenger seat and one in the center
of the rear bench seat. Before se ating the participant in the
driver’ s seat, t he markers were shown to them. The EEG system
was not connected to the amplifier during t he movements.
All instructions for different movements were given through
pre-recorded audio files played back using a laptop and spe akers
inside the car.
Block IV : Usability
To assess the usability of the system, the participants were
asked to fill out a questionnaire right after Block I. This
questionnaire was the System U sab ility Scale (S US; Brooke, 1986 )
was employed, also used in other B CI related studies prior to
this one ( Pasqualotto et al., 2011; Duvina ge et al., 2012 ). SUS is a
standardized questionnaire consisting of ten questions based on
Likert scales with five options ranging from “strongly disagree ”
to “strongly agree.” In total, S US contains five positively and
five negatively formulated questions about the system being
assessed , for example “I think t hat I would like to use this system
frequently” or “I found the system unnecessarily complex.” From
the answers given, a SUS score is calculated, ranging between
0 (worst possible system) and 100 (best pos sible system). This
score has to be interpreted taking t he individual context of system
usage into account. In contrast to qualitative assessments, the
S US does not yield any insight into which usability problems
exactly are present within the system. It provides however a quick
and reliable way to determine whether or not major c hanges are
necessary in order to make the system safe and comfortable to
use.
Additionally, the participants were asked to rate the level
of comfort wearing the system after each of the previously
described experimental blocks (I–III) on a sc ale from 1 to 10,
one meaning “extremely bad ” and ten “very comfortable.” We
acquired these three subjective impressions to gather insight into
how the system’ s perceived comfort changed over the cour se of
the experiment.
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Zander et al. Passive BCI for Autonomous Driving
To get an even deeper insig ht into the comfort of wearing t he
system, participants were asked to fill out another questionnaire
after the third experimental block, after roughly 140 min
of wearing the system almost const antly. We adapted a
questionnaire for the evaluation of t he wearing comfort for
firemen helmets ( F abrizio and Cimolino, 2014 ), by only keeping
questions deemed fitting to our context. All questions were rated
on a five point Likert scale. In addition to t hese questions, we
asked two yes-no questions: Whether or not the participant
believed the c ap had moved, and whether or not it induced the
feeling of dents on their head. Finally , we asked the participants
to mention any discomfort associated with we aring the system,
like the feeling of pressure on the head, head aches, or nausea.
Analysis Pr ocedur es
Block I: Self-application
Comparison of time needed by the experimenter and the
participant to apply the system and to adjust the electrodes was
done by two-sample t -tests.
The signal quality ratings were subjected to a three-way mixed
measures ANOV A with the two within-subject factors visual
filters (no filters vs. 0.1–40 Hz bandpass) and electrode (Fp1 vs.
Fp2 vs. vs. Fz vs. FC5 vs. FC6 vs. C3 vs. C4 vs. Cz vs. CPz vs.
Pz vs. CP5 vs. CP6 vs. PO3 vs. PO4 vs. POz vs. Oz) and the
between-subject factor applicant (investigator vs. participant).
Because a total of six different measurements of ele ctrode
positions were taken during the course of this experiment, t hese
measurements were first transformed into one coordinate system
to allow a unified comparison. To this end, all measurements
were re-referenced to a mean head middle and radius, wit hin
participants, as follows.
1. All coordinates of re cording j , j = 1, ..., 6 were referenced to
the head midpoint hm j , which is calculated with t he recorded
reference points (nasion n j and left and right preauricular
points, lp j and r p j ) by
a. Drawing a li ne through both pre auricular points lp j and
r p j :
Calculate the slope by computing new coordinates
( u j ) i : = ( lp j ) i − ( r p j ) i , for i = 1, 2, 3 denoting the
scalars of the three-dimensional ve ctor u j .
Define the line by
g j : = lp j + r j u j with r j to be determined.
b. Construction of a plane H j through n j , which is
perpendicular to the line g j :
Find the variables x , y , z to determine the plane equation
for H j
H j : ( u j ) 1 x + ( u j ) 2 y + ( u j ) 3 z : = e .
To find e , insert the coordinates of the nasion reference
point n j into the equation
H j ( n j ) : ( u j ) 1 ( n j ) 1 + ( u j ) 2 ( n j ) 2 + ( u j ) 3 ( n j ) 3 = e .
c. For t he purpose of finding the intersection of the line g j
with the plane H j , insert the coordinates of g j into the plane
equation above and solve for r j :
H j  g j  : r j = e − ( u j ) 1 ( lp j ) 1 − ( u j ) 2 ( lp j ) 2 − ( u j ) 3 ( l p j ) 3
( u j ) 2
1 + ( u j ) 2
2 + ( u j ) 2
3
.
Inserting r j into the plane equation yields the head
midpoint:
hm j = lp j + r j u j .
2. After c alculating the head midpoints hm 1 to hm 6 , we compute
the arithmetic average hm over all recordings as the final
reference point in order to minimize the error of measurement
in the system.
3. The de viation of the re corded head midpoint hm j to hm is
calculated for each recording:
d j : = hm j − hm , j = 1, ..., 6.
4. Then, all re corded electrode positions ( ep k ) j , k = 1, ..., 16
are re-referenced to hm by addition with d j and the euclidean
distance ed j 1 j 2 between different recordings j 1 , j 2 is calculated:
( d j 1 j 2 ) i : = (( ep k ) j 1 + d j 1 ) i − (( ep k ) j 2 + d j 2 ) i ,
ed j 1 j 2 : = q ( d j 1 j 2 ) 2
1 + ( d j 1 j 2 ) 2
2 + ( d j 1 j 2 ) 2
3
The value used for comparison of different recordings j 1 , j 2 was
this euclidean distance e d j 1 , j 2 .
For Block I, recorded positions from the investigator-applied
cap were compared to the positions from the self-applied c ap.
Mean differences of electrode positions were then compared to
the expected value of no difference in positions using a one-
sample t -test against zero.
Block II: EEG Recor dings
Oddball par adigm: ERP analys is
EEG data was first preprocessed by applying a bandpass-filter
from 1 to 30 Hz, retaining all frequencies relevant for later
analyses. Then, epochs of 1100 ms were extracted, starting 100 ms
before stimulus onset of the standard a nd deviant e vents. Baseline
correction was performed with a 100 ms pre-stimulus inter val.
To compare event-related activity between car and i ndoor
recordings, amplitudes and latencies of the N200’ s and P300’ s
were extracted.
First, the indoor condition was used as a baseline as it
conforms to laboratory conditions. Inspection of the grand
a verage revealed a global negative minimum at 300 ms over
the centro-parietal lead (Pz) and a global positive maximum
at 400 ms over the centro-central lead (Cz). Based on t hese
peaks, a search window was defined around 300 ± 70 and
400 ± 70 ms to search for maxima in the individual a verages.
Once for each individual the global pe aks were identified, the
peaks on individual channels were identified using a ± 25 ms
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Zander et al. Passive BCI for Autonomous Driving
window around the individual global pea k. Mean amplitudes and
latencies were extracted for all channels. This procedure resulted
in a 4 x 16 vector for e ach participant, consisting of the mean
amplitudes and the latencies of the two components at each
channel.
For comparison of mean peak amplitudes two repeated
measures ANOV A s were performed. Me an amplitudes from
electrode Pz were used for the negativity and from Cz for the
positivity. Each 2x2 ANOV A had the two within-participant
factors recording condition (indoor vs. car) and stimulus
(standard vs. deviant).
In order to examine disparities of mean pe ak latencies between
conditions (indoor vs. car), mean difference pe ak latencies were
calculated by subtracting the negative from the positive pe ak
latency. The mean difference was taken per participant for the
two conditions and subjected to a paired sample t -test.
To test for equivalence of EEG measures between recording
conditions the two one-sided tests (TOST , S chuirmann, 1981,
1987; Westlake, 1981 ) procedure was applied to mean peak
amplitudes and mean difference peak latencies with an epsilon
of the standard de viation of the indoor condition, which was
regarded as the control group (R -package “equivalence ” May 14,
2016; V0.7.2). A p -value of 0.05 was taken as the significant
threshold for all TOST.
Induced alpha parad igm: frequency analysis
To compare oscillatory features between car and indoor
recordings, three different measures were t aken: The
power spectral density function covering 0.1–40 Hz, single
measurements of the band power in the alpha b and, and the
time course of the alpha band power during the 6-s trials of the
paradigm (engaged vs. relaxed).
Fluctuations in alpha power occur with a broader distribution
over posterior areas of the s calp ( Sauseng et al., 2005 ). Since we
were interested in parietal alpha as potential indicator of mental
load, analyses were restricted to five posterior electrodes, namely
Pz, PO3, PO4, POz, and Oz. The data was bandpass filtered from
0.1 to 40 Hz and time epochs of 6 s were sele cted, covering each
full trial.
Power spectral densities (PSD) were calculated for e ach entire
epoch and a veraged per participant, resulting in 2 x 2 x 5 PSD
distributions for each participant (2 experimental conditions x 2
mental states x 5 channels). We used th ese participant-individual
PSDs as well as the a veraged PSDs over all participants (grand
a verage), resulting in a total of 11 (2 x 5 + 1) PSD-distributions
for each experimental condition.
Individual and grand avera ge Pearson Correlation of the PSD
in the frequency band of 0.1 Hz to 40 Hz were calculated for
each electrode between indoor and car conditions and tested for
significance using one sample t -tests against zero.
The alpha b and (7–13 Hz) being of prime interest here, we
also calculated a single bandpower value in this frequency range
for each participant, electrode, and trial. We used epoch s of 4 s
length, starting 2 s after stimulus onset. Logarithmic variances
of each trial per electrode of each participant were calculated
and normalized with the maximum value of each electrode.
These measures were then averaged over all trials, resulting in
a normalized mean alpha band power for e ach participant under
each experimental condition on the five investigated electrodes.
Effects between recording conditions, stimuli and ele ctrodes were
investigated in a 2 x 2 x 5 ANOV A with the three within-
participant factors recording condition (indoor vs. car), stimulus
(standard vs. devia nt) and electrode (Pz vs. PO3 vs. PO4 vs.
POz vs. Oz). The factor electrode is a repeated me asure here as
EEG measures at one electrode depend on values measured by
other electrodes. Again, the TOST procedure with an epsilon of
the standard de viation of the indoor condition was applied to
normalized mean alpha band power values to test for equivalence
between recording conditions.
As a third measure, the time course of the band power in
the alpha band was used. It was calculated by shifting a 500 ms
window over each single trial and calculating the ba nd power
for each window position. To avoid leakage effects, the window
was multiplied with a Gaussian bell cur ve of t he same size.
Afterwards the single-trial measurements were normalized with
the mean of all band powers. The normalized measurements were
a veraged, resulting in 2 x 5 time courses for each participant (2
experimental conditions x 5 channels). As a bove, we also took the
grand avera ge into account, resulting in 11 time courses in total
per experimental condition.
To examine the difference in the time course of the
band power in the alpha range between conditions, Pe arson
Correlations were calculated for each participant, channel and
condition.
BCI Analysis of both par adigms
B CILAB ’ s built-in classification approaches were used to evaluate
the offline single-trial accuracies as an estimate of potential online
performance.
For the oddball paradigm, data was b andpass filtered from 0.1
to 15 Hz and downsampled to 100 Hz. Epochs of 800 ms were
extracted starting at each stimulus marker. A windowed-means
approach ( Blankertz et al., 2011 ) was used to extract features,
using 8 consecutive windows of 50 ms starting at 300 ms post-
stimulus. As a classifier we used linear d iscriminant analysis , LD A
( Webb, 2002 ). Mean ERP classification error rates of all eight
participants were subjected to a paired samples t -test.
Logarithmic band power was used for fe ature extraction ( Solis-
Escalante et al., 2010; Z ander et al., 2011 ) of the data of the
second paradigm. This was applied t o epoch s of 6 s, as a bove. We
performed a (10 x 10)-fold cross-validation, and classified using
LD A. Me an classification error rates were subjected to a paired
samples t -test.
Classification error rate results from both paradigms were
subjected to a TOST procedure with an epsilon of the standard
deviation of the indoor condition t o test for equivalence between
recording conditions.
Block III: Driving-Related Movements
Each of the three movement groups had one ele ctrode position
measurement before, and one after it. Mean differences of
electrode positions prior to and after each movement group were
compared to the expected value of no difference in positions
using a one-sample t -test against zero.
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Block IV : Usability
The System U sab ility Scale was interpreted following the
guidelines set by Brooke (1986) . To determine the resulting SUS
score of the system, all given a nswers were weighted accordingly
and added up. This resulted in a total s core per participant, which
then was multiplied by the factor 2.5.
After experimental blocks I to III, participants were asked to
give a subjective estimate of how comfortable the system felt. The
median of the comfort ratings of all participants was used as the
overall comfort rating here. To test for differences between the
three time points, a W ilcoxon Signed-rank test was applied. The
wearing comfort questionnaire was evaluated descriptively.
RESUL TS
Block I: Self-application
Application Time
A two-samples t -test indicated that the me an time needed
for application of the cap did not differ signific antly between
experimenter ( M = 123.2 s, SD = 43.8) and participants ( M =
104.9 s, SD = 49.0), t (9) = 0.880, p = 0.391, though showing a
tendency that participants perform faster. Mean times needed for
adjustment of electrodes also did not differ significantly between
investigator ( M = 256.3 s, SD = 221.3) and participants ( M =
310.2 s, SD = 285.1), t (9) = 0.472, p = 0.642, showing a tendency
that experimenters are faster.
Electr ode Signal
The three-way mixed measures ANOV A on signal quality ratings
revealed no signific ant main effect of applicant, F (1, 18) = 0.341,
p = 0.341, η 2 = 0.019. The main effect of filter was significant,
F (1, 18) = 66.861, p = 0. 000, η 2 = 0.788. Since the main effect
of electrode violated the assumption of sphericity Greenhouse-
Geisser corrected values were used. The main effect electrode was
significant, F (5.167, 93.012) = 2.876, p = 0.017 η 2 = 0.138. None of
the interaction effects were significant, all ps > 0.281.
Electr ode Positions
The t -test against zero performed on mean differences of
electrode positions ( M = 13.76 mm, SD = 5.12 mm) between
investigator - and self-applied cap yielded significance, t (9) =
8.498, p = 0.00001. The electrode positions varied most on
the midline of the head, with 15.5 mm variation (averaged over
all 10 partici pants) at Oz to 16.1 mm a veraged variation at Fz.
This could be due to the structure of the cap: It has two holes
for the ears, so electrodes in thi s area are fixated more clearly
than electrodes elsewhere. Electrodes on t he forehead can be
positioned up to 1 cm higher or lower without any ob vious effects
on the cap like inconvenience or ill-fittingness, so it was hard for
both participants and investigators to position the cap correctly
around the midline of the head (see Figure 7 ).
For Block I, recorded positions from t he investigator -applied
cap were compared to the positions from the self-applied c ap.
Mean differences of electrode positions were then compared to
the expected value of no difference in positions using a one-
sample t -test against zero.
FIGURE 7 | Shifts in electrode position s after self application in mm
compared to application by investigator .
Block II: EEG Recor dings
Due to software problems on a laptop EEG data of two
participants had to be excluded. Analyses of the EEG data were
based on the remaining eight pa rticipants.
Oddball Paradigm: ERP Results
Grand avera ge ERP s from the oddball paradigm are depicted in
Figure 8 . The repeated measures ANOV A performed on mean
amplitudes of the negativity measure yielded significance for t he
main factor stimulus, F (1, 7) = 21.745, p = 0.002, η 2 = 0.756.
Amplitudes of the deviant stimuli ( M = − 5.44 µ V , SD = 6.21
µ V) were more negative than in stand ard stimuli ( M = − 0.01
µ V , SD = 2.66 µ V). The main factor environment was not
significant, F (1, 7) = 0.101, p = 0.760, η 2 = 0.014. There was also
no significant interaction, F (1, 7) = 0.261, p = 0.625, η 2 = 0.036.
Results of a TOST procedure with an epsilon of the standard
deviation of the indoor condition were not signific ant ( mean
difference = 0.145; eps ilon = 3.95; confidence-inter val : − 6.79 to
7.08; df = 7; p = 0.166).
For the positivity measure there was no signific ant main
effect of stimulus, F (1, 7) = 5.001, p = 0.060, η 2 = 0 .417. The
main effect environment also was not significant, F (1, 7) = 2.767,
p = 0.140, η 2 = 0.283. The interaction between stimulus and
environment was significant, F (1, 7) = 31 .800, p = 0.001, η 2 =
0.820. Amplitudes of the deviant trials were higher indoors ( M =
9.54 µ V , SD = 9.05 µ V) than in the car ( M = 5.18 µ V , SD = 10.57
µ V), while amplitudes in standard trials indoors ( M = 0.02 µ V ,
SD = 1.25 µ V) were only slightly smaller t han in the car ( M =
0.92 µ V , SD = 2.27 µ V). Due to this significant interaction effect
no TOST was performed.
Results from the t-test performed on mean pe ak latency
differences of the indoor ( M = 85 ms, SD = 46.3 ms) and t he
car condition ( M = 101.5 ms, SD = 75.1 ms) were not significant
( p = 0.569). The TOST procedure with an epsilon of the st andard
deviation of the indoor condition showed no significance for
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FIGURE 8 | Grand average ERPs of the indoor condition (top left) and the running car condition (top right) on channel Cz. Deviant (bottom left) and standard
(bottom right) ERPs in comparison between indoor and car condition.
mean peak latency differences ( mean difference = − 16.5; eps ilon
= 46.3; confidence-inter val : − 68.8 to 35.8; df = 7; p = 0.158).
Induced Alpha Paradigm: Fr equency Results
All individual correlation values for power spectral densities
between conditions were higher than 0.79 on all five ele ctrodes,
with a mean correlation value of 0.97 ( SD = 0.046). All t -tests of
these correlations against zero were significant with p s < 0.0001.
For the grand average , correlation values between indoor and car
condition were both higher t han 0.989, with a mean of 0.997 ( SD
= 0.004). T -tests against zero yielded significance ( p s < 0.0001)
for both conditions (engaged/relaxed).
The three-way repeated measures ANOV A with within-
subject factors recording condition ( p = 0.061), stimulus
( p = 0.177), and electrode ( p = 0.24) performed on mean
alpha band powers was not significant on main or interaction
effects, with non-significant interactions (all p s > 0.272). The
TOST procedure with an epsilon of the standard deviation of t he
indoor condition assigned to mean alpha band powers showed
significance on electrodes PO4 ( mean difference = 0.049; eps ilon
= 0.129; confidence-inter val : − 0.031 to 0.128; df = 7; p = 0.049)
and Oz ( mean difference = 0.001; eps ilon = 0.127; confidence-
inter val : − 0.079 to 0.076; df = 7; p = 0.009). The TOST was not
significant for electrodes PO3, POz, and Pz, all ps > 0.340.
Alpha band time course (see Figure 9 ) correlations between
indoor and car condition yielded a mean correlation of r = 0.27
for the relaxed condition (Pz: r = 0.43, PO3: r = 0.26, PO4: r =
0.29, POz: r = 0.30, Oz: r = 0.09). Correlations in this condition
were significant on all five electrodes for five participants ( p s <
0.00001), on four electrodes for one participant ( p s < 0.005), and
for the other three participants on three electrodes ( p s < 0.021).
In the engaged condition the mean correlation of all participants
was r = 0.23 (Pz: r = 0.34, PO3: r = 0.19, PO4: r = 0.18, POz:
r = 0.31, Oz: r = 0.14). Tests yielded significance of correlations
on all five channels for three participants ( p s < 0.043). For three
participants correlation was significant on four channels ( p s <
0.00001) and for two participants on three ele ctrodes ( p s <
0.00001).
BCI Results of Both Paradigms
A paired samples t -test indicated that the error rates for ERP
classification in the indoor condition ( M = 0.126, SD = 0.086)
did not differ significantly from the error rates in the car
condition ( M = 0.145, SD = 0.116), t (7) = − 0.68149, p =
0.518. Furthermore, the TOST procedure with an epsilon of
the standard de viation over participants in the indoor condition
confirmed significant equivalence classification results in the two
recording conditions ( mean difference = 0.018; eps ilon = 0.086;
confidence-inter val : − 0.032 to 0.069; df = 7; p = 0.020).
A paired samples t -test indicated that the error rates of b and
power classification for the indoor condition was lower ( M =
0.283, SD = 0.160), but did not differ significantly from the
error rates in the car condition ( M = 0.351, SD = 0.137), t (7) =
− 1.608, p = 0.152. The TOST procedure with an epsilon of the
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FIGURE 9 | Grand Averages of the alpha band time courses for r elaxed and engaged conditions indoors and in the car . For the red and the gr een curve,
displaying the relaxed conditions, a similar pattern starting 1 s after onset of stimulus presentation is observed. Similarities over time are also apparent for the engaged
conditions, repr esented in the black and blue curve. Clear co-variation of indoor and in car alpha time courses for both r elaxed and engaged conditions is proven by
high correlation between the signals.
FIGURE 10 | Shifts in electrode positions after movements of the head (A) , the arms (B) , and the whole body (C) in mm.
standard de viation over the participants in the indoor condition
confirmed significant equivalence for classification results in t he
two recording conditions ( mean difference = 0.066; eps ilon =
0.162; confidence-inter val : − 0.012 to 0.144; df = 7; p = 0.026).
Block III: Driving-Related Movements
Figure 10 shows the shifts in electrode positions after each of the
three groups of movements.
After head-related movements the difference between
electrode positions ( M = 9.6, SD = 9.1) differed significantly
from zero, t (9) = 3.3237, p = 0.009. The apparent lateralization
of this effect (25.3 mm mean variation at CP5 vs. 19.6 mm at
CP6) may be due to the direction of t he shoulder check.
After performance of arm movements the mean difference
between electrode positions ( M = 7.6, SD = 4.8) differed
significantly from zero, t (9) = 5.0241, p = 0.001. V ariations were
located mainly to the right side of t he head with a maximum of
10.5 mm mean variation at PO4. The cause for this may be the
direction of the rotation and/or handedness of participants.
Mean electrode position differences after whole-body
movements ( M = 8.4, SD = 6.4) differed significantly from zero,
t (9) = 4.1691, p = 0.002. The greatest shift was on the forehead
with 10.1 mm avera ge variation on Fp2 and on the midline of the
head (8.2 and 9.3 mm mean variation at POz and Fz). This could
be caused by the cables, which were tied together , but interfered
with the seatbelt ne vertheless.
Block VI: Usability
The total SUS score of the system added up to 65. Following
the official SUS score interpretation, this is slig htly above the
threshold for an acceptable system.
Due to minor delays during the experiments, the time
points of the additional questionnaires varied slightly for each
participant. On avera ge, questions were answered after 60 (Block
I), 122 (Block II), and 137.5 (Block III) min.
After the first 60 min, the system got a comfort rating of 7.5,
which then decreased significantly over t he next hour resulting
in a rating of 3 after 122 min. In the following quarter of an
hour needed for block III, the comfort rating st ayed stable at 3.
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Zander et al. Passive BCI for Autonomous Driving
A W ilcoxon signed-rank test s howed that there was a significant
difference between the first time point of the rating after 60 min
( Mdn = 7.5) and the s econd rating after 122 min ( Mdn = 3),
( W = 0, Z = − 2.69, p = 0.008). No valid W ilcoxon signed-rank
test could be performed to compare the se cond and third ratings,
because the number of effecti ve samples was less than 6 after
subtraction of ratings equaled zero for six participants ( W = 4,
Z = − 0.82, p = 0.625). R ating scores of the first and the th ird
rating again showed significant differences, ( W = 0, Z = − 2.67,
p = 0.008).
The six examined items of wearing comfort of the system are
summarized in Figure 11 . A feeling of pressure on the head was
rated as the most irritating with a me an score of 2.2. The overall
impression of wearing comfort got a mean score of 2.7, and was
therefore also perceived as bad. The overall weight of the system
on the head was on avera ge rated as the most pleasant aspe ct of it
with a score of 4.2.
Furthermore, the wearing comfort questionnaire yielded the
following insights. Seven participants complained about dents
and chafe marks on their heads, four about headaches, and
one each about neck pains, nausea, and dizziness. Moreover ,
one participant had the subjective impression that the system
had moved over the course of the experiments. None of the
participants reported skin irritations due to wearing t he cap.
DISCUSSION
Block I: Self-application
We found that the participants were equally fast as t he
experimenter in applying the cap, and equally capable in
optimizing signal quality. We thus conclude t hat this type of dry
electrode EEG system can indeed be used by individual end-
users. We should note, however , that there was no objective
measure of when the application was finished; it was b ased on
individual judgements of the experimenter.
We did not investigate the personalization of the cap by
adjusting the length of each electrode pin, because t his task needs
to be done only once. Therefore, we did not investigate how easy
it is to personalize the cap while wearing it. Personalization did,
however , t ake up quite some time. We assume that the QuickBit
approach would benefit from improvement: Continuously
adjustable bits would probably simplify perso nalization and
optimize the result.
While it is not surprising that the signal quality was rated
better with active display filters, we had assumed that the signal
quality would be better after adjustments by an expert operator
than compared to that adjusted by the participant. This, however ,
was not the case: Participants reached a similar , sometimes even
better signal quality. We assume the reason for this to be t hat
participants had a better feeling for how hard, and where exactly
the electrodes pressed against their heads, allowing them to
fit them even better to t he scalp t han the experimenter could
without the risk of harming the participant.
For the electrode positions, some variation in the
measurements must be taken into account. The used system
has known variations in measured data points, and for some
electrodes (primarily at the back of the he ad), the measuring
stylus may have moved slightly due to head shifts that were
sometimes necessary for the measurement. This problem was
addressed mathematically, as described above. It was also not
possible to point the stylus exactly at the electrode ’ s point of
contact with the skin, but only at t he electrode ’ s body. It remains
unclear , whether or to what extent the differences in ele ctrode
positions we measured, imply that the points of contact changed
as well.
Block II: EEG Recor dings
For the oddball paradigm ERP analysis reve aled highly similar
morphology of ERP s elicited by deviant stimuli in bot h recording
conditions. We found highly significant effe cts for the negative
FIGURE 11 | Mean score of questions about wearing comfort.
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Zander et al. Passive BCI for Autonomous Driving
peak in the ERP condition. The de viant trials were significantly
different from the standard trials in both the indoor and the
car condition, showing no difference between conditions. This is
not the case for the positivity. The main effe ct is not significant.
It should be mentioned though that we have a clear tendency
into the right dire ction with a p value slightly missing the
threshold criteria of 5%. Peaks of the P300 are reduced in the
car environment as a result of other signals interfering wit h
the recorded signal in the c ar. No significant differences were
found between peak latencies between indoor and car recordings.
We conclude that the main information carried in t he signal
is comparable for indoor and in car recordings, but its signal
strength is attenuated slightly in t he car condition.
For the alpha recordings, we have a slightly more complex
case. We clearly see a correlation between conditions—alpha
values show a similar development over time outside of and in the
car. However , there is no significant difference between relaxed
and engaged trials on a verage over all participants, which was
expected from the experimental design. Wh en we take a closer
look at the individual values (see Figure 12 ), we see that some
participants managed to get relaxed in the corresponding task,
while others did not. This explains why we do not get significant
main effects—several participants were not able to relax in the
appropriate condition. This effect can be seen consistently on
both conditions, inside and outside the c ar. However , we do
perhaps see a tendency on the main effect of condition that, e ven
though it’ s not significant, indicat es a small change in alpha power
between recordings inside and outside of the car.
For all comparisons that showed no significant difference
between conditions an equivalence test was performed. Features
of the ERP were not equivalent between conditions while spectral
features were equivalent on some of the tested elect rodes.
These results show that even though we do not ha ve significant
differences, the recoded dat a cannot be taken equivalent. For
strict neurophysiological measurements it hence might be wort h
a consideration whether the tested headset should be used or not.
For ERP and spectral dat a classifications were not significantly
different, and were furthermore clearly equivalent. We, hence,
assume that the evaluated system measured t he differences
in cognitive states, well, in both conditions. Despite small
morphological and power differences, classification results were
comparable in both domains. Therefore, a BCI can be applied
with equal reliability to data from both conditions.
The results we found on the EEG components examined
here are as expected from the literature and replicate
results from a previous comparison study ( Z ander et al.,
2011 ). Therefore, we conclude that the dry electrode system
investigated here provides comparable data to a conventional
gel-based system when used in an autonomous driving
context.
It still remains unclear whether the results can be fully
transferred to a real-world autonomous driving context where
the car would most likely be moving. A driving car would bring
additional factors like increased vibration from the engine, jerks
due to uneven roads, or inertial effects induced by dire ction
changes. Moreover , the driving task itself could lead to additional
artifacts, such as stress related sweating on the scalp and the
user scratching their own skin. Also head movements against
the headrest might le ad to changes of electrode positions in
a way that was not examined here. Another factor would be
the radio not being muted in a real-world-driving s cenario:
Environmental noises between 70 and 120 decibels ha ve been
found to increase the amplitude of measured P300 events ( N am
et al., 2008 ). Drivers will also be moving e.g. their heads and
hands, which they minimized during data recording. This study
however presents a first step in investigating the applicability
of dry systems in a car environment, re vealing initial insights
in a scenario with controlled artifact activity. These results can
form the basis for future studies in active driving study s cenarios,
where that control is further relaxed.
Block III: Driving-Related Movements
The results showed that the electrodes shifted in position when
executing different driving-related movements.
The most significant shifts occurred during movements
involving the head dire ctly, primarily at the rear left of t he head.
FIGURE 12 | Mean alpha power in relaxed and engaged trials for individual subjects.
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We assume this was due to the shoulder check, which required a
sudden, fast turn of the whole head to the left and back. We can,
however , not be sure as to whether the shoulder check or the look
at the ceiling had more effect on th e electrodes positions since
they were measured together as one movement group. Eit her
way, the resulting differences may well-influence the quality of
the data re corded by the system.
The performed arm movements had less impact on the
electrode positions, though t he shifts were still significant.
The third group of movements resulted in the le ast position
changes for all electrodes although the participants had to
move their whole upper body—including the head. The most
pronounced shifts were obser ved at the rig ht frontal area. The
instruction to touch the marker in the legroom of the passenger
seat might offer an explanation for t his, as the head had to be
moved rather far to the rig ht and down. Also in th e area around
the left ear increased shifts in position were obser ved. Most likely,
this was a result of fastening and unfastening the se atbelt which
may ha ve induced some strain in that are a, maybe by pulling on
the cables.
Finally, since the movements were always performed in t he
same order (head, arm, and body), order effects cannot be
excluded.
For future use, the cap could be applied e.g., only after the seat
belt has been fastened, which often requires some effort. Since the
cables may also ha ve caused some of the position shifts, a wireless
system is preferable.
Block IV : Usability
The System U sab ility Scale is a general questionnaire to evaluate
the usability of technical systems, and is not specific ally designed
for B CI systems. As S US provided significant insights in ot her
B CI-related studies, we decided to use it here as well ( Duvinage
et al., 2012; Käthner et al., 2013 ). Some questions however ,
especially about the interaction with the system, did not fit the
current purpose and even confused some of t he participants.
The resulting S US score might t herefore not be entirely accurate,
but, we believe, still provides a good indication about the overall
usability of the system in an autonomous driving context.
The evaluation of the wearing c omfort was better tuned to
the current context and raised no questions from participants.
The results showed that the first hour of using the system did
not bother the participants much, which qualifies it for short-
term usage at least. After the second hour of using the system,
however , t he subjective comfort ratings dropped significantly and
participants began to complain about dents, slight headaches,
neck pain, even nause a and dizziness, which clearly shows that
the EEG system with the current cap design is not suitable for
long-term use. We did not investigate recovery time: How long a
break is needed, before the cap can be comfort ably worn again?
This remains an open question.
The most annoying features of the system, according to the
participants, were its rather tight fi t onto the head resulting in
the feeling of pressure. The overall weight of the system was, in
contrast, rated to be quite pleasant which might be caused by
the flexible, thin material of the cap. Also, participants rated the
adaptability of the cap as quite high. The cap was rated as being
fixated well, thanks to the chin belt and the holes for the ears
providing a lot of stability–only one participant had the feeling
the cap had moved at all.
CONCLUSION
Concluding in brief, the EEG system allowed for technically
sound recordings, even with c ar -induced interferences present.
It thus appears to be suitable for passive B CIs in autonomous
driving scenarios, allowing mental st ates to be detected in re al
time.
In only a few minutes, individuals were able to apply and
adjust a pre-customized cap, with the help of a little mirror , like
the rear view mirror of a car. A system to better support the
evaluation of signal quality would be beneficial, howe ver.
A ccording to the system usability scale, the system is at
the edge of acceptability in terms of usability. This may
suffice for professional drivers, who likely stand to gain the
most from autonomous driving and supportive systems, but
room for improvement remains. In particular the reported
discomfort after longer use is unacceptable. Here, ma jor
improvement is necessary to decre ase pressure on the scalp
so the system is no longer obstructive and uncomfortable,
hindering the users from focusing on themselves and their
tasks.
Seeing now that EEG technology has made clear progress
toward ease of use and mobile scenarios, we can envision th e
application of passive BCI s in the context of autonomous driving.
P assive B CIs can provide essential information about the driver’ s
cognitive or affective state, which can be combined with other
sensor data of the c ar. In that way, the car c an adapt to, and
make decisions informed by, individual aspec ts of the driver. As
passive BCIs do not rely on dire cted or even cons cious actions
of the driver ( Z ander and Kothe, 2011 ), th e car will still drive
autonomously but gains an additional stream of information,
pertaining to the subjective situational interpretation of the
driver.
For example, we can clearly imagine applications improving
safety and comfort. In cases where the driver is required to t ake
over control, the communication of this requirement can be
adapted to the current, actual state of t he driver. Another scenario
would be the detection of whet her or not communicated alarm
signals were perceived and proces sed by the driver. These are
only a few, simple examples of a broad range of applications to
be thought of.
Moreover the investigated system could be used in a broader
field of scenarios and might be of spe cial interest for the field
of Mobile brain/body imaging (MoBI). The field ’ s objective is
to acquire neurophysiological recordings of human cognition
in real world environments where subjects perform real-world
tasks. A portable, wireless, high-quality data recording and f ast to
prepare dry contact system would prove useful for brain activity
recordings on actively behaving participants ( Gramann et al.,
2011, 2014; De Sanctis et al., 2012 ).
The application of passive B CI during autonomous driving
however provides an exemplary use case for technology that
adapts to the (neuronal) state of its operator during automation
Frontiers in Human Neur oscience | www .frontiersin.org 14 February 2017 | V olume 11 | Article 78

Zander et al. Passive BCI for Autonomous Driving
in general. Such Neuroadaptive Technology is a clear additional
step toward closing the cybernetic loop ( Pope et al., 1995 ).
ETHICS ST A TEMENT
The study involved standard EEG procedures covered in an
ethic statement approved by the et hics committee of the
Institute of P sychology and Ergonomics of the Berlin Institute
of Technology. All participants gave written consent to their
participation in the conducted study. They were provided wit h
information on the purpose of the study, given t he opportunity
to ask questions and were informed that their participation was
voluntary and they could end the experiment whene ver they liked
without a need to provide reasons. Participants also ga ve their
consent for data recording , anonymous storage of that data, as
well as its usage for publication.
AUTHOR CONTRIBUTIONS
All authors contributed substentially to the work presented
here. Everybo dy was contributing to the drafting and revising
of the documents and approved the final version. Everybody
agreed to be accountable for the integrity and accuracy of the
work. Specifically: TZ designed and super vised the experimental
procedures, conducted and super vised t he analyzes, interpreted
the results for the context of autonomous driving. LK and
KG were responsible for quality of writing and validation
of results. E verybody below was involved in conducting the
experiments and ensured data quality. LA was responsible for
the statistical analyzes and integrity of t he manuscript. JP and
MB were responsible for the electrode loc alization and the
related mathematical procedures. LZ: Was responsible for th e
programming and EEG and B CI analyzes. AB: Was responsible
for evaluation of the questionnaires.
ACKNOWLEDGMENTS
We thank Brain Products GmbH (Munich, Germany) for
providing us with the tested EEG System which made this
research pos sible. We are also indebted to Prof. Dr.-Ing. Matthias
Roetting and Mario Lasch, Chair for Human-Machinse Systems,
TU Berlin for providing the research car and supporting us in all
technical questions regarding the car.
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Conflict of Interest Statement: The authors declare that the resear ch was
conducted in the absence of any commercial or financia l relationships that could
be construed as a potential conflict of interest.
Copyright © 2017 Zander , Andreessen, Berg, Bleuel, P awlitzki, Zawallich, Krol and
Gramann. Th is is an open-acces s ar ticle distributed under the terms of t he Crea tive
Commons A ttribution L icense (CC BY). The use, distribution or reproduction in
other forums is permitted, provided the orig inal author(s) or licensor are credited
and tha t the original publica tion in th is journal is cited, in accordance with accepted
academic pr actice. No use, distribution or reproduction is permitted wh ich does not
comply with the se terms.
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