Psychophysiological Measurements in Real Working Environments
Wireless EEG Study of the Operators’ Vigilance
Pavle Mijović, Ivan Gligorijević, Evanthia Giagloglou and Ivan Mačužićand Branislav Jeremić
Faculty of Engineering Sciences, University of Kragujevac, Sestre Janjić 6, Kragujevac, Serbia
1 STAGE OF THE RESEARCH
The vigilance decrement and inability of the industry
worker to sustain attention during a task can lead to
errors in operating, which could further lead to
dangerous situations including catastrophic events
with fatalities. Therefore, measuring operators’
vigilance level, while performing everyday
monotonous repetitive task, is of crucial interest.
The current project is related to innovation
through human factors in risk analysis and
management at the work place (InnHF project,
http://www.innhf.eu). The term innovation is
justified by hypothesis that it is possible to obtain
higher degree of operators’ safety and workers well-
being by on-line measurement of operators’
vigilance state, using lightweight and wireless sensor
systems. Extensive literature review in the fields of
neuroergonomics, human factors and ergonomics
(HFE), psychophysiology and biomedical signal
processing preceded the current research in order to
approach this problem in the most appropriate way.
At this point, we believe that it is possible to obtain
reliable measure of operators’ vigilance state, while
performing repetitive task, by continuous on-line
recording of the brain signals using compact and
lightweight wireless EEG system.
The initial experiments have been conducted,
using novel and state-of-the-art lightweight wireless
EEG system (SMARTING, made by mBrainTrain
LLC, Serbia), which confirmed the ability of the
device to obtain reliable, artefact-free recordings.
The signal strength, judged by visual inspection of
known eye-blink signatures and clearly visible alpha
activity, proved strong enough for the proposed
psychophysiological measurement of operators’
vigilance state. These "entry" experiments were
carried out using the open-source open ViBE
software package (http://openvibe.inria.fr).
Further, we have created the experimental set-up
of the improvised "lean" workplace, where the real-
life example from one of our industry partners is
authentically replicated. This serves to examine the
psychophysiological correlates of the operators’
vigilance state, while carrying out everyday
repetitive jobs.
We have also identified research paradigms and
the next step is to perform high-density EEG
measurements in these realistic work conditions,
identifying the physiological signatures of vigilance
state of the operator and offer tools for their real-
time detection.
2 OUTLINE OF OBJECTIVES
Neuroergonomics is the study of brain and behavior
at work (Parasuraman, 2003). It is a novel and
interdisciplinary area or research that merges the
disciplines of neuroscience and human factors and
ergonomics in order to maximize the benefits of
each (Parasuraman and Rizzo, 2007).
With the recent technological advancements it
became possible to move the EEG measurement
from the strictly controlled laboratory conditions
(movement-constrained behavior), to the real-life
environments where subjects are allowed to
naturally walk outdoor wearing wireless EEG device
(Debener et al., 2012). Therefore, with these new
wireless EEG systems it becomes possible to merge
EEG measurements with the guiding principle of
neuroergonomics, and examine how the brain carries
out the complex everyday work tasks, and not just
simple and artificial laboratory task (Parasuraman
and Rizzo, 2007).
The first objective of our work is to examine the
psychophysiological correlates of vigilance
decrement in the improvised, but highly realistic
workplace by using the wireless EEG system in or
der to avoid any constrains to physical actions of the
operators.
After the initial identification of these correlates,
the next step is to develop and implement real-time
vigilance estimation mechanisms.
60
Mijovi
´
c P., Gligorijevi
´
c I., Giagloglou E., Ma
ˇ
cuži
´
cand I. and Jeremi
´
c B..
Psychophysiological Measurements in Real Working Environments - Wireless EEG Study of the Operators’ Vigilance .
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
Final stage assumes applying knowledge from
the improvised workplace repeating the experiments
in the real working environment featuring "lean"
production procedures. This is planned in
collaboration with the industrial partners from the
InnHF project (FIAT Automobiles Serbia and
Tetrapak Serbia). This brings twofold benefit: on the
side of research, it will be possible to obtain useful
information on the cognitive state of the operator
without disrupting the everyday work routine; on the
industry side, new guidelines and constrains for
increasing the workers vigilance levels are expected
to improve the ergonomic of the workplace and
consequently, the operators’ and equipment safety,
bringing reduction in costs and maximizing
production efficiency.
Another side objective is to upgrade the
vigilance monitoring system to create a brain
computer interface (BCI) that will allow the
operators’ safety action in cases that are critical from
the safety point of view. Also, this system is to
serve for control of robotic actions for the places
hardly reachable by the hand of an operator. It is
well known that in the industries with high
production output it is not always possible to design
the production line that is completely user-friendly,
due to robustness of the machinery. Therefore, the
BCI application for the safety actions would be a
highly desirable solution: the operator would use
comfortable, completely wireless EEG system that
would not require any physical action.
Finally, we believe that we will be able to
identify the optimal sensor positions using high-
density recordings brought in by our current wireless
EEG system. This is expected to enable reduction of
the number of necessarily used electrodes, which
could in turn offer guidelines for future industrial
vigilance and BCI sensor system that is lightweight,
comfortable, completely mobile and ready to be
used by workers. Therefore, we aim to contribute to
creation of the state-of-the-art on-line monitoring
and interaction system for the operators’ in the
industry. This way, the operators’ potential errors
due to decrement of alertness level could be
prevented, leading to decrease of the industrial
accidents and incidents that are in the most cases
caused by the operators’ lapses in sustained
attention.
The outline of the objectives of this work is
graphically represented in Figure 1.
Figure 1: Outline of the research objectives.
3 RESEARCH PROBLEM
3.1 Vigilance Monitoring
Regarding safety assessment, HFE is concerned with
the elimination, reduction, or mitigation of human
error. Human error is typically categorized as slips
(errors in actions), lapses (errors in memory), or
mistakes (errors in applying knowledge; Reason,
1990) and is often cited as a factor in up to 80% of
accidents (Wiegmann and Shappell, 2001).
The critical role that is assigned to HFE in design
and safety assessment depends on the widespread
use of automation and its impact on human errors
(Cacciabue, 2004). The automation is often
proposed as a method for removing or reducing
human errors in a system and it shifted the roles of
operators from active controllers to that of system
supervisors who serve in fail-safe capacity only
when problem arise (Sheridan, 1980). Even though
automation can improve performance of routine
operations by reducing the workload and reducing
human error at behavioural level, it also introduces a
variety of safety critical issues due to lapses in
attention and errors of cognition. Therefore,
vigilance became crucial component of human
performance in many working environments where
automated systems are ubiquitous (Warm et al.,
2008), because lapses in attention and errors
committed by the operator in industry can have
catastrophic consequences.
Vigilance is a term that has been used in
different ways by different scientist and therefore, it
has various definitions (Oken et al., 2006). However,
in cognitive neuroscience and psychology, the term
PsychophysiologicalMeasurementsinRealWorkingEnvironments-WirelessEEGStudyoftheOperators'Vigilance
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of vigilance is used to refer ability of organisms to
maintain their focus of attention and to remain alert
over prolonged period of time (Warm et al., 2008).
When studying vigilance, the neuroscientist often
specifically refers to a vigilance decrement and
lapses in the sustained attention over extended
period of time, especially when repetitious,
monotonous and continuous tasks are carried out
(Oken et al., 2006; Warm et al., 2008). However,
many studies of vigilance have shown that for most
of operators engaged in attention and monotonous
tasks, it is not possible to retain a constant level of
alertness (Yu et al., 2007).
Various authors studied the physiological
correlates of vigilance decrement. Hung et al. (2001)
quantified alertness level by correct rates on auditory
and visual vigilance task in an EEG study.
According to them, in the auditory vigilance task,
the relative spectral amplitudes in the alpha and
theta bands as well as the mean frequency spectrum
was found to be the best combination for predicting
the alertness level. In visual vigilance task, the beta
frequency band was the only feature for predicting
alertness level (Huang et al., 2001). This was
supported by the work of the Dockree et al. (2007).
They confirmed that the subjects with higher tonic
frequencies in the alpha range show a larger-
amplitude late positive event-related potential (ERP)
component that has previously been found to predict
a good sustained attention performance. Bonnefond
et al. (2010) showed in an ERP study that vigilance
decline that is reflected by significant changes in
performance and spectral power, is also
accompanied by specific effect of time on the P2 and
Late Positive LP1 component.
Jung et al. (1997) proposed a method for
estimating operators’ level of attention in near real
time, by merging power spectrum estimation,
principal component analysis and artificial neural
networks. They confirmed that there is a close
relation between changes in performance and in
EEG power spectrum.
Pattyn et al. (2008) studied the mechanisms of
sustained attention by targeting two modes of
attention control, endogenous and exogenous
attention. In their study, they did not use the EEG
measurements, instead the reaction times (RT), error
rates (ERs) and heart rate variability (HRV) were
measured and it was found that performance
decrement in the visual vigilance task appears after
approximately 20-30 minutes.
Above mentioned studies almost exclusively
agree on obtaining the information about vigilance
from EEG signals. However, the optimal set of
features for detecting it, as well as their applicability
to on-line monitoring is still out of reach. Identifying
measures that are intuitive, reliable and applicable
for working environment would bring a huge
benefit. These would then serve to tune the future
industrial algorithms.
3.2 Problems regarding Real-life
Vigilance Monitoring
As already stated before, continuous monitoring of
the operators’ mental state, in operational
environment, could decrease potential for serious
errors and provide valuable information concerning
the ergonomics of the tasks being performed (Gevins
et al., 1995). However, in order to achieve this kind
of monitoring, specific requirements need to be
fulfilled including the reliable measurements using
inexpensive and highly mobile equipment. These are
the necessary prerequisites for conducting brain
activity monitoring outside the laboratory settings
(Gevins et al., 1995). Therefore, the non-invasive,
small in size and weight, and relatively inexpensive
wireless EEG device that produces reliable results,
seems like a promising solution for the above-
mentioned problems. Even though mobile EEG
represents a promising tool for the experiments to be
carried outside the laboratory settings, it is far from
being the only requirement for achievement of
reliable psychophysiological results. In fact, it is
necessary to carefully design and set up the
experiment itself due to limitations of use of
presentation software and other conditions
contributing to realistic character of the improvised
workplace.
Previously mentioned studies were conducted in
the strictly controlled laboratory conditions using
presentation software and obtaining the time-locked
events in order to study, mostly, the ERPs and time
locked power spectral densities. Further, apart from
measuring the electrocortical brain activity, in most
of these studies, the reaction time (to a stimuli) was
one of the main parameters for measuring the
vigilance level. At this point, it is important to
outline the difficulty of obtaining the time-locked
events in dynamic real working environment. We
believe that if there is a requirement for operator to
put additional effort for the button press, or any
other action in order to obtain the time-locked events
while performing actual task, the credibility of
results will decrease as in that case the working
environment is changing as the action that operator
is carrying out is altered.
Therefore, one of the biggest challenges
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regarding EEG recording outside the laboratory
settings, assuming the movement related artefacts
already being suppressed, is to find a way to obtain
the time locking events, without distracting the
operator from performing his task in order to asses
the information regarding the ERP components and
time-locked spectral densities.
4 STATE OF THE ART
Recent studies indicated that it is possible to
measure the human state of vigilance using EEG
signals (Oken et al., 2006; Yu et al., 2007; Dockree
et al., 2007; Huang et al., 2007; Bonnefond et al.,
2010). It has been further reported that it is possible
to estimate the vigilance level in near real-time
(Jung et al., 1997). However, all of the mentioned
studies have one common limitation, that is, all of
them have been performed in laboratory conditions,
with constricted movements and featuring bulky
equipment.
Apart from the advantage that wireless EEG
systems are small enough and allows the on-field
measurement of the operators’ vigilance state, this
setting also allows to the operator to move naturally
in the working environment, without causing huge
instrumentation artefacts. This is supported by the
fact that movement of electrode wires, a major
source of instrumentation artefacts (Usakli, 2010),
and electrode displacement are minimized and
therefore, artefacts that are not physiologically
related are suppressed (Debener et al., 2012).
Recently a few EEG studies were conducted in
various areas, including the study of vigilance.
However, the most of these studies were carried out
in order to assess information regarding the drivers
safety and safety of the pilots in aviation industry
and not for operators that are working in industry
sector. This is not surprising, since the most of the
advances in HFE, including the early beginnings of
this field, are coming from aviation industry (Canas
et al., 2011). On the other hand, a large portion of
the research in the field of mobile EEG is conducted
in the scope of medical studies, example being the
work of Lin et al. (2008) in which non-invasive
neural prostheses was proposed for continuously
monitoring high-temporal resolution brain dynamics
using wireless EEG.
It is important to note, that while carrying out the
above-mentioned studies for driver and/or pilot
vigilance, virtual reality software was used. While it
allowed replicating the real-life situations to a large
extent, it still could not sufficiently mimic the real
environment and all of ifs features.
Further, the majority of the previous studies were
carried out using commercial wireless EEG gadgets
with dry and non-contact electrodes that are
becoming increasingly popular, mostly due to
gaming purposes. The attractive side of these
commercial devices lies in their comfortable and
easy-to-use character, e.g. stemming from the fact
that the traditional, gel electrodes are avoided.
However, on the downside, they currently provide
the limited signal strength and therefore, the results
obtained with this equipment are not reliable.
Debener et al. (2012), on the other hand, showed
that it is possible to modify one of the consumer 14-
channel wirelesses EEG in order to improve the
signal quality, obtaining the state-of-the art device
for further research. In the aforementioned study,
wireless and mobile character of the consumer EEG
device was combined with the research-grade
electrodes allowing for high-quality recordings.
5 METHODOLOGY
5.1 Vigilance Monitoring
Measuring EEG in an unrestricted environment
always gathered close attention of scientific
community. However, high-quality, medical graded
recordings were only recently demonstrated with
fully mobile platforms (Debener et al. 2012).
Company mBrainTrain recently provided a research
tool for neurofeedback testing paradigms. We
adapted their "SMARTING" device for vigilance
research task mainly due to two features:
1) Small, lightweight and mobile character, and
2) Close to medical signal quality
Figure 2 portrays a close-up of "SMARTING"
while being prepared for recording. This solution
uses gel-based electrodes produced by the renown
Easycap company (Germany), offering high-quality
recordings mainly due to low impedance.
This system features 24 EEG channels with 24-
bit resolution. The real-time data transmission is
achieved using the Bluetooth 2.1 EDR that is able to
communicate with a PC or Android based
phones/tablets. In addition, electrode impedance
information is continuously sent, together with the
gyroscope readings.
PsychophysiologicalMeasurementsinRealWorkingEnvironments-WirelessEEGStudyoftheOperators'Vigilance
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Figure 2: Mounting of "SMARTING": recording
electrodes are filled with conductive gel.
Figure 3 shows a preview from our initial quality
tests. A simulated "lean" factory work place was
also created (Figure 4). Although still in the fine-
tuning stage, it can faithfully replicate several
common working places related to the targeted,
repetitive factory tasks.
Figure 3: Signals with a moving subject exhibited almost
no visual movement artefacts.
5.2 Signal Segmentation
For tracking of changes in vigilance, it is necessary
to properly segment the signal according to the
performed action. This is not an easy task taking into
account the often vague beginning and the end of
this process.
We decided to take a two-step approach to this:
1) Provide a sound signal, identical to the sound
that is produced by the machine used by the
operator at the real working environment, to
mark a beginning of the operation (and also
serve as a trigger signal for the EEG
segmentation), or
2) Provide a signal, not notable for the operator
but coinciding with the beginning of the
operation.
While precise triggering is still a hard task, we
believe that introducing a microphone device that
would serve as a reliable reference can solve this
problem.
The logic behind "notable" and "not notable"
triggers is to eliminate (but also investigate)
vigilance dependency on outside factors like this
one.
Figure 4: Realistic "lean" workplace, where simulated
repetitive task is performed.
5.3 Signal Processing
The use precise triggering will enable the
comparison between spectral EEG measures and the
ERPs obtained after the signal segmentation. The
rationale lies in the fact that ERPs, if proven
sufficiently reliable compared to established spectral
measures, can offer much faster detection of the
decrease in vigilance. In this scope, the changes in
the delay or amplitude of e.g. P300 can be related to
the depth of cognitively processing the stimulus that
further can be related to the level of attention
(Murata et al., 2005).
In parallel, we plan to segment the continuous
EEG signal, according to the duration of one
complete operators’ action, in order to investigate
known frequency components of interest, theta (4-8
Hz), alpha (8-13) and beta (13-30 Hz) bands..
Further, due to the non-stationary nature of EEG
signals and in order to achieve precise time-
frequency resolution, we will use decomposition
techniques including Wavelet transform and
Empirical Mode Decomposition (Huang et al. 1998,
Mijovic et al., 2010). Then, spectral correlates for
vigilance monitoring will be traced and the delay in
their detection used to assess their suitability as a
real-time vigilance-tracking marker.
Temporal analysis of ERP- based vigilance
detectors would be achieved by first training a
classifier in case of the present audio stimuli.
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Response time delays would most likely be used as a
measure of decreased attention. Introducing another
triggering system (e.g. precise enough, yet outside of
the intentional human notice) would be used to
assess how vigilance changes in presence/absence of
distraction.
Also, training ERP and spectral methods for
robotic control is going to take place as well to
pinpoint the usefulness of industry BCI devices.
5.4 Real-time Implementation and
Industry Solution Roadmap
Once the algorithms are developed to positively
detect vigilance shift, they are going to be
implemented online. Following this, the complete
system is expected to be tested within the facilities
of our partners, FIAT Serbia and Tetrapak Serbia.
Further, the minimal viable EEG system to
perform these tasks can be identified from here. This
means fewer sensors, on much fewer previously
identified locations. This would serve as a guideline
for future industry vigilance monitoring system.
6 EXPECTED OUTCOME
The proposed aim of this research is to pinpoint to
routines used to attain close to constant vigilance
level of operators performing industrial, repetitive
tasks.
This is done in order to reduce and/or eliminate
potential slips of sustained attention, which is
desirable from both economic (production
efficiency) as well as health related (work injuries)
points of view.
We expect to deliver real-time algorithms for
vigilance monitoring and notification from one hand,
as well as guidelines for improvement of work
routines taking into account the newly available
vigilance monitoring data from the other.
We will use a high-density
electroencephalographic (EEG) sensor to identify
the limits of necessary "resolution" for vigilance
monitoring. We expect to, following the success of
this task, propose an industrial system that would
reduce the costs and increase the work safety, while
being of acceptable price and comfortable to use.
Attaining the individual privacy, we expect it
will be possible to provide the operator with the
information when his alertness level starts to
decrease, which in turn can yield fewer errors
committed and consequently, increase the overall
industrial safety at the workplace and decrease the
economical losses.
In order to achieve the objectives of this work,
we will firstly conduct psycho-physiological
measurements at the improvised, but authentically
replicated workplace using the novel wireless EEG
system. We will then bring these measurements to
real factory conditions due to collaboration with our
partner companies interested in this concept.
ACKNOWLEDGEMENTS
The presented study is fully funded by the European
project framework FP7 InnHF.
REFERENCES
Bonnefond, A., Doignon, C. N., Touzalin, C. P., and
Dufour, A., 2010. Vigilance and intrinsic maintenance
of alert state: An ERP study. Behavioural brain
research, 211(2), 185-190.
Cacciabue, P. C., 2004. Human error risk management for
engineering systems: a methodology for design, safety
assessment, accident investigation and training.
Reliability Engineering & System Safety, 83(2), 229-
240.
Canas, J. J., Velichovsky, B. B., Velichovsky, B. M.,
2011. Book chapter “Human Factors and
Ergonomics”, in Martin, P. R., Cheung, F. M.,
Knowles, M. C., Kyrios, M., Littlefield, L., Overmier,
J. B., &Prieto, J. M. (Eds.), 2011. IAAP handbook of
applied psychology.Wiley.com.
Debener, S., Minow, F., Emkes, R., Gandras, K., and Vos,
M., 2012.How about taking a lowcost, small, and
wireless EEG for a walk?.Psychophysiology, 49(11),
1617-1621.
Dockree, P. M., Kelly, S. P., Foxe, J. J., Reilly, R. B., and
Robertson, I. H. 2007. Optimal sustained attention is
linked to the spectral content of background EEG
activity: greater ongoing tonic alpha ( 10 Hz) power
supports successful phasic goal activation. European
Journal of Neuroscience, 25(3), 900-907.
Gevins, A., Leong, H., Du, R., Smith, M. E., Le, J.,
DuRousseau, D., Zhang, J., and Libove, J.
1995.Towards measurement of brain function in
operational environments. Biological Psychology,
40(1), 169-186.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H.
H., Zheng, Q., Yen, N. C., Tung, C. C., and Liu, H. H.,
1998.The empirical mode decomposition and the
Hilbert spectrum for nonlinear and non-stationary
time series analysis. Proceedings of the Royal Society
of London. Series A: Mathematical, Physical and
Engineering Sciences, 454(1971), 903-995.
PsychophysiologicalMeasurementsinRealWorkingEnvironments-WirelessEEGStudyoftheOperators'Vigilance
65
Huang, R. S., Tsai, L. L., and Kuo, C. J., 2001. Selection
of valid and reliable EEG features for predicting
auditory and visual alertness levels. Proceedings-
national science council republic of china part b life
sciences, 25(1), 17-25.
Jung, T. P., Makeig, S., Stensmo, M., and Sejnowski, T. J.
1997. Estimating alertness from the EEG power
spectrum. Biomedical Engineering, IEEE
Transactions on, 44(1), 60-69.
Lin, C. T., Ko, L. W., Chiou, J. C., Duann, J. R., Huang,
R. S., Liang, S. F., Chiu, T.W., and Jung, T.P. 2008.
Noninvasive neural prostheses using mobile and
wireless EEG. Proceedings of the IEEE, 96(7), 1167-
1183.
Mijovic, B., De Vos, M., Gligorijevic, I., Taelman, J., and
Van Huffel, S. 2010. Source separation from single-
channel recordings by combining empirical-mode
decomposition and independent component analysis.
Biomedical Engineering, IEEE Transactions,57(9),
2188-2196.
Murata, A., Uetake, A., Takasawa, Y., 2005. Evaluation of
mental fatigue using feature parameter extracted from
event-related potential”, International Journal of
Industrial Ergonomics, 35(8),,761–770.
Oken, B. S., Salinsky, M. C., and Elsas, S. M., 2006.
Vigilance, alertness, or sustained attention:
physiological basis and measurement. Clinical
Neurophysiology, 117(9), 1885-1901.
Parasuraman, R., 2003.Neuroergonomics: Research and
practice. Theoretical issues in ergonomics science, 4,
5-20.
Parasuraman, R., Rizzo, M., 2007.Neuroergonomics: The
Brain at Work. Oxford University Press, Oxford.
Pattyn, N., Neyt, X., Henderickx, D., and Soetens, E.,
2008. Psychophysiological investigation of vigilance
decrement: Boredom or cognitive
fatigue?.Physiology&Behavior,93(1), 369-378.
Reason, J. T., 1990. Human Error. Cambridge University
Press, Cambtidge.
Sheridan, T., 1980. ‘Supervisory Control’ in Handbook of
Human Factors, ed G Salvendy, Willey, New York,
pp. 1243-1268.
Usakli, A. B., 2010. Improvement of EEG signal
acquisition: An electrical aspect for state of the art of
front end. Computational intelligence and
neuroscience, volume (2010), article ID 630649.
Warm, J.S., Parasuraman, R., and Matthews, G.,
2008.Vigilance requires hard mental work and is
stressful. Human Factors: The Journal of the Human
Factors and Ergonomics Society, 50(3), 433-441.
Wiegmann, D. A., and Shappell, S. A., 2001. Human error
analysis of commercial aviation accidents:
Application of the Human Factors Analysis and
Classification System (HFACS). Aviation, Space, and
Environmental Medicine, 72 (11), 1006-1016.
Yu, H., Shi, L. C., and Lu, B. L., 2007. Vigilance
Estimation Based on EEG Signals. In Proceedings of
the International Conference on Mechanical
Engineering 2007 (ICME2007).
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