Performance Specifications of Market Physiological Sensors for Efficient
Driver Drowsiness Detection System
Messaoud Doudou and Abdelmadjid Bouabdallah
Lab. Heudiasyc, UMR CNRS 7253, Universit
´
e de Technologie de Compi
`
egne, France
Keywords:
Driver Fatigue, Drowsiness Detection, Measurement, Sensors, Physiological Signals.
Abstract:
Significant advances in bio-sensors technologies hold promise to monitor human physiological signals in real
time. In the context of driving safety, such devices are knowing notable research investigations to objectively
detect early stages of driver drowsiness that impair driving performance under various conditions. Seeking
for low-cost, compact yet reliable sensing technology that can provide a solution to drowsy state problem is
challenging. The contribution of this paper is to study fundamental performance specifications required for
the design of efficient physiological signals based driver drowsiness detection systems. Existing measurement
products are then accessed and ranked following the discussed performance specifications. The finding of
this work is to provide a tool to facilitate making the appropriate hardware choice to implement efficient yet
low-cost drowsiness detection system using existing market physiological sensors products.
1 INTRODUCTION
Till now, the total number of serious car crashes is
still increasing regardless of improvements in road
and vehicle design for driver safety. Reduced men-
tal alertness due to drowsy state have been identified
as the greatest safety danger and the major cause of
road traffic accidents (Lal and Craig, 2001). While
each day in the United States 80,000 individuals fall
asleep behind the steering wheel (American Academy
of Sleep Medicine, 2005), 25-30% of driving ac-
cidents in the UK are drowsiness related (ROSPA,
2001), about 35% drivers in the Netherlands and 70%
drivers in Spain have reported falling asleep while dri-
ving (Morales et al., 2015).
The measure of human physiological parameters
allows evaluating objectively cognitive-attentive indi-
cators, in reaction to external perceptual stimuli. The
study of human physiology has showed that mono-
tone driving task and nocturnal driving mostly lead
to sleep deprivation, lacking sleep, and being in a
state of low energy (Morales et al., 2015). These
symptoms decrease cognitive abilities and make dri-
ver more prone to fatal errors. Many drowsiness mea-
surement technologies have been developed to moni-
tor driving behavior and alert drivers when drowsy.
Recently, with the remarkable advance in sensing
and communication technologies, Low-cost wearable
devices are fast becoming a key instrument on bio-
sensors based applications and they have been app-
lied in many fields including industrial, transporta-
tion, medical, daily-life, sport, etc. There are a num-
ber of tentative promoted by shift-work industries to
monitor cognitive state of human-being using these
emerging technologies since they hold the promise of
being objective compared to other measuring techno-
logies. These bio-signals based technologies make it
possible to alert driver at earlier stages of drowsiness
and thereby prevent many drastic accidents providing
a solution to the driver drowsy problem (Sahayadhas
et al., 2012).
In this study, we focus in assessing recent develop-
ment of bio-sensors technologies in the market that
are currently underway to address driver drowsiness
issue, and provide a concise hardware specification
tool for the design of efficient driver alertness mo-
nitoring system. In the following, the key drowsi-
ness detection technologies are presented in section
2. Next, the general architecture of driver drowsiness
monitoring system with different modules are explai-
ned in section 3. Main performance characteristics
that must be met by a drowsiness monitoring techno-
logy are discussed in section 4. Section 5 is devoted
to review potential market physiological sensors pro-
ducts. Ranking methodology is described in section
6 providing a tool to make the appropriate hardware
choice of existing products. Finally, section 7 conclu-
des the paper.
Doudou, M. and Bouabdallah, A.
Performance Specifications of Market Physiological Sensors for Efficient Driver Drowsiness Detection System.
DOI: 10.5220/0006607800990106
In Proceedings of the 7th International Conference on Sensor Networks (SENSORNETS 2018), pages 99-106
ISBN: 978-989-758-284-4
Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
99
2 DROWSINESS DETECTION
TECHNOLOGIES
A plethora of driver fatigue researches exist span-
ning different measurement technologies. The most
commonly used measurement can be categorized
upon the monitoring instrument into: (i) Vehicle-
based sensors, (ii) Video-based sensors, and (iii)
physiological signals sensors such as electrooculo-
graphy (EOG), electromyography (EMG), electrocar-
diography (ECG), and electroencephalography (EEG)
signals where the latter is the most used (Sahayadhas
et al., 2012; Sanjaya et al., 2016).
Vehicle monitoring focuses on driving and vehi-
cle patterns such as steering wheel angles and rever-
sals, the car position with respect the road’s middle
lane and the standard deviation of lane position
(SDLP), etc. However, this technology can operate
reliably only at particular environments (Sahayadhas
et al., 2012) depending on the geometric characteris-
tics of the road and to a lesser extent on the kinetic
characteristics of the vehicle (Vural, 2009) and they
are easily influenced by other factors such as road
marking, climate, lighting and traffic conditions.
Video monitoring measures driver’s facial ex-
pression and detect drowsiness from differentiating its
abnormal behavior such as eye closure (PERCLOS),
head nodding, etc. The common limitation is lig-
hting. The detection rate using this technology was
59% compared to 85% and 97.5% of EEG and ECG
(Sanjaya et al., 2016). (Golz et al., 2010) reported
an accuracy around 74% and 66% using PERCLOS
compared to 87% and 89% from EEG/EOG.
Bio-sensors measure physiological signals from
organs such as brain, eyes, muscles, and heart which
have visual correlation with fatigues/drowsiness. Re-
searchers observed via EEG that drivers had sleep
bursts accompanied by theta waves and K-complexes
while they still had their eyes open, something video-
based monitoring might have missed. Physiological
signals have been shown to be reliable and accurate
since they are less impacted by environmental and
road conditions and thus may have fewer false positi-
ves than other measures (Zilberg et al., 2007).
3 SYSTEM ARCHITECTURE
Due to the increasing interest in the use of wearable
bio-sensor systems, many communication architectu-
res have been proposed depending on the target ap-
plication (Lee et al., 2013). The general architecture
of bio-sensor system is composed by three main mo-
dules: (i) signal acquisition, (ii) data processing, and
Warning/Alert Buzz Speed Control
Control Module
Wirless/Wire Transfert
Translation &
Feature Extraction
Training/Classification
Algorithms
Processing Module
Wirless/Wire Transfert
BioSensors (EEG,
EOG, etc.)
Noise/Artifact
Removal
Acquisition Module
Figure 1: Logical view of different modules of the system.
(iii) control modules as depicted in Fig. 1.
3.1 Acquisition Module
This module is composed of different physiological
wearable sensors such as EMG, ECG, EOG, EEG,
etc. attached to the body which measure physiolo-
gical signals. These sensors form a network and com-
municate with the network coordinator to send data.
The measured signals are then filtered and transfor-
med to remove any noise and artifact that may affect
the quality of sensed data values.
3.2 Processing Module
Signals are received from acquisition module after fil-
tering noise and removing artifacts. As second stage,
signals are processed to extract the main features that
reflect different states of the target application (e.g.
the cognitive states of driver). These features are then
passed to the training and classification algorithms
to determine the new measured states. As for driver
drowsiness, the features can be used to determine in
which level of alertness the driver is.
3.3 Control Module
Driver alertness is monitored in real time using acqui-
sition and data processing modules. Whenever a
drowsy state is identified, the detection event is then
triggered by the control module to make the appro-
priate action in time. This action may be an alarm or
buzz inside the vehicle to alert or wake-up the driver.
The action may take control of the vehicle in order to
speed-down or stop the vehicle.
Many portable systems propose to incorporate the
acquisition and the processing modules into the same
component to compact the system. Hence, there is a
SENSORNETS 2018 - 7th International Conference on Sensor Networks
100
Figure 2: Driver Drowsiness Detection System Architec-
ture.
serious issue with the battery lifetime. In the context
of driver drowsiness detection, the acquisition module
is attached to the driver and the processing module
is installed on the vehicle which has sufficient power
supply. This allows extending the battery lifetime and
keep monitoring for long periods. The control module
is mounted on the vehicle to trigger warning messages
and sound alerts. This module can be even enabled to
control some actions of the vehicle such as accelera-
tion and speed. The system can be extended to sup-
port multi-tiers cloud-based architecture (Zao et al.,
2014). As depicted in Fig. 2, some of data can be
transmitted via 3G/4G/LTE connections to the remote
servers where data analytic algorithms can be used to
train and extract new knowledge. This enables moni-
toring cognitive states during real driving tasks from
large number of drivers and may be explored by the
research community to enrich training sets and im-
prove the accuracy of existing detection algorithms.
4 PERFORMANCE
SPECIFICATIONS
If any bio-sensors system is to prove suitable for de-
tecting driver drowsiness, it must meet some perfor-
mance specifications. These specifications are essen-
tial in making the appropriate bio-sensors hardware
choice for design consideration. In the following, the
major specifications are discussed:
4.1 Multi-sensors Support
Single signal measurement such as EEG may neces-
sitate dense electrode placement in different locations
to accurately capture cognitive states. Hybrid signal
acquisition through simultaneous recording of diffe-
rent bio-signals can yield higher accuracy of the sy-
stem. Combination of multiple bio-signals measure-
ments, such as ECG, EMG, EOG with EEG, the sy-
stem can measure not only brain waves but also heart
rate, eye movements, etc. Research results have sho-
wed that adding either EOG or ECG measurements,
there is further improvements in reduction of error ra-
tes in drowsy state detection (Warwick et al., 2015).
4.2 Type of Electrodes
The choice of electrode technology is very impor-
tant since it represent the sensing component. With
respect EEG measurement, wet electrodes known as
silver-chloride electrodes (Ag/AgCl) are widely used
by current market products. These electrodes are low-
cost, and have low contact impedance, and good sta-
bility in time. Wet electrodes requires removing outer
skin layer of the scalp and filling a special conductor
gels which take long time to prepare and are uncom-
fortable to users. Dry electrodes are other technology
which do not need to use gel and skin cleaning. Ho-
wever the bad signal quality is their main disadvan-
tage. Fig. 3 shows an example of wet and dry electro-
des available in the market.
(a) (b)
Figure 3: (a) Wet electrodes vs. (b) Dry electrodes.
4.3 Electrode Placement
Capturing as much as data from strategically locati-
ons is critical to pinpointing the drowsy related cau-
ses. For each bio-signal, there exists suitable locati-
ons where may be placed to efficiently measure signal
reflecting the drowsy state of driver. For EOG, elec-
trodes are attached to the eye skin (up/down/left/right)
whereas for EMG, they may be placed on the left
bicep, right bicep, left forearm flexor, right forearm
flexor, frontal muscles, or on the deltoid, trapezius
Hostens and Ramon (Hostens and Ramon, 2005).
Performance Specifications of Market Physiological Sensors for Efficient Driver Drowsiness Detection System
101
While 5 & 12 lead electrode placements are gene-
rally used for ECG recording. For EEG, the electrode
placement according to the 10-20 Standard defines
which brain location that serves a specific function
(see Fig. 4). More specifically: Prefrontal Cortex (Fp)
for emotional inhibition and attention; Frontal Lobes
(F) for working memory, metaphorical thinking, sus-
tained attention and judgment; Central Strip (C) for
sensory-motor functions; Temporal Lobes (T) for lan-
guage comprehension and long-term memory; Parie-
tal Lobes (P) for language processing and procedural
memory; Occipital Lobes (O) for visual processing.
Thus, locations concerning various forms of attention
which reflect alertness/drowsiness states must be co-
vered by the hardware.
Figure 4: The 10-20 system of EEG electrode placement.
4.4 Number of Channels
An electrode capturing bio-signal activity is called a
channel. Typical Bio-sensor systems can have as few
as a single channel to as many as channels (256 for
EEG) depending on the required density. The sy-
stem must trade-off between capturing as much as
bio-activities with some performance metrics. For in-
stance, increasing the number of channels will have
significant delay for data processing. Second, more
channels mean higher costs and more difficult expe-
rimental setups. Lastly, by increasing the number of
channels, the huge amount of signals will be trans-
mitted that impairs reliability and battery usage es-
pecially for mobile and low-power systems. On the
other hand, very few channels impair the accuracy of
detection.
4.5 Portability & Mobility
Conventional bio-sensors systems such as actiCHamp
(Brain Products), Neuroscan NuAmps Express (Com-
pumedics Ltd.), and EDVTCS (Neurocom) are wi-
red. The acquisition part of wired systems gene-
rally comes with bulky and heavy amplifiers and pre-
processing units. Connecting wires is usually compli-
cated with a large number of cables between the elec-
trodes and the acquisition part. For these reasons, pre-
paration time for measuring signals is typically very
long. In addition, user movement is limited due to ca-
ble constraints. Therefore, the application of drow-
siness detection based on these systems is difficult
to escape from laboratory scale experiments. With
emerging wearable technologies, biopotential measu-
rements, such as EEG, ECG, EMG, and EOG can be
delivered in real-time via wireless and Low-energy
connections such as WiFi, Bluetooth, ZigBee, etc.
Therefore, these provide the advantages of mobility
and long-term monitoring. Portable systems facilitate
the implementation of driver drowsiness detection sy-
stems and enable in-field experimentation instead of
simulation environment. However, huge volume of
signals may be sampled and need to be transmitted
wirelessly in real-time. Hence, the system must prove
energy-efficient operation for long period to be accep-
ted for continuous monitoring. for example, compres-
sion algorithms can be used to alleviate big data trans-
fer since it is time and energy consuming (Hussein
et al., 2015).
4.6 Artifact Removal
Bio-sensors are prone to various sources of noise
and artifacts. Signal conditioning is essential to ena-
ble transmission of precise bio-signals. Many noise
sources are likely present from physiological inter-
ference and power line noise. Physiological interfe-
rence occurs between EEG, EMG, ECG, EOG and
others. The amplitude of EMG, ECG and EOG is re-
latively lager around 50uV and 20-30mV while that
of EEG is much smaller around 10 100uV. Thus, the
EEG signals are easily buried by these physiological
signals unavoidably. Power line noise (Outlet, USB,
etc.) can also contaminates the EEG signals in the
range of 50 or 60Hz. Furthermore, the measured bio-
signals of mobile systems are also subject to heavy
motion and vibration artifacts. Hence, the presence of
noise and artifact removal mechanism is essential in
such systems.
4.7 System Autonomy
Another important specification is the need of energy-
efficient and long-term wearing system. For systems
that use battery powered bio-sensors, the lifetime of
the system is the critical challenge to ensure conti-
nuous driver monitoring. In fact, wireless transmis-
sions consume the largest amount of device’s energy.
Indeed, the battery autonomy may go from 4 hours
to 24 hours or even more depending on the wireless
SENSORNETS 2018 - 7th International Conference on Sensor Networks
102
Brain Products Biosemi Mind Media ANT Neuro Cognionics G.tec NeuroScan
ActiCAP ActiveTwo NeXus-32 eego sports HD-70 Nautilus QuickCaps
QUASAR - DSI 10/20 NeuroElectrics - Enobio mBrainTrain ABM OpenBCI IMEC OpenEEG
Emotiv - EPOC+ NeuroSky - MindWave Macrotellect - BrainLink InteraXon - Muse Versus Melon Focus
Figure 5: Overview of biosensors market products.
technology (e.g. Bluetooth, Wifi, etc.) and on the
sampling rate. The system must be designed with ef-
ficient usage of sensory and radio components to ens-
ure reasonable monitoring lifetime.
4.8 Software
The software is one of the main part of the system.
Thus it is fundamental to have access to data in or-
der to manipulate and/or analyze the recorded signals.
The market product may provide software develop-
ment kit (SDK) as bio-signal acquisition software or
an application programming interface (APIs) compa-
tible with some known commercial or open source
bio-signals software platforms (e.g. BCI 2000, Open-
Vibe, LabVIEW, etc). This enables to facilitate and
speed-up the development of efficient detection algo-
rithms.
4.9 Product Cost
Making a choice between products must trade-off sy-
stem performance with its cost. Many of existing
market devices are designed for clinical and research
purpose and provide multi-sensor acquisition with a
large number of electrodes/channels and with incre-
dible sensitivity. The cost of such systems is visibly
high due to the full provided functionality. Depen-
ding on the application need like driver drowsy de-
tection, the system cost may be reduced and can be
determined by the performance specifications such as
number of channels, sensors’ type, portability, wire-
less technology, flexibility, and comfort.
5 MARKET PRODUCTS
With the growing progress in sensing technologies,
more smart, compact and user-friendly products have
been increasingly introduced to the market. Each
of existing bio-sensor products provides specific mo-
nitoring functionality of human physiological states.
For example, MySignals
1
provides e-Health platform
that includes several bio-signals measurements such
as ECG, EMG and X-Y-Z Accelerometer but not EEG
and EOG.
ActiCHamp cap from Brain Products is destined
for EEG signal acquisition. ActiCHamp exists with
different channel and sampling rate configurations
ranging from 32 to 160 channels and from 10 to 100
kHz receptively. ActiCAP express is light head cap
version with 16 channels with active electrodes. Bio-
semi developed Active Two which is a 8/16/32 chan-
nels acquisition cap system with wet electrodes. The
eego/rt sports from ANT Neuro is a portable head
cap with up to 64 channels for rehabilitation mental
states studies, and can work without conductive gel
electrodes. NeXus-32 and Nexus-4 (Mind Media) are
32 channels (heavy) and 4 channels (portable) bio-
sensors head cap with wet electrodes. ANT Neuro
developed wireless eego/rt sports with 7-32 channels
and dry electrodes.
Cognionics developed 64 channels headset Dry
electrodes for general signals measurement. Quick-
20/30 is light version with 20 channels and pos-
sibility to integrate 8 channels from auxiliary
EOG/ECG/EMG/PPG bio-sensors. Cognionics also
designed Sleep HeadBand with 10 channels for sleep
monitoring. G.tec designed g.nautilus with 8/16/32
1
http://www.my-signals.com/
Performance Specifications of Market Physiological Sensors for Efficient Driver Drowsiness Detection System
103
channels and wet/dry electrodes for clinical and re-
search bio-signals measurement. Quick Cap is 256
wet electrode head cap from NeuroScan capable of
measuring EEG, ECG, EMG, and EOG bio-signals.
QUASAR designed DSI-10/20 head cap with 21 dry
electrodes. While Enobio from NeuroElectrics is a
bio-signals acquisition head-cap with 8/16/32 chan-
nels. mBrainTrain designed 24 channels EEG wire-
less cap with wet electrodes as a research tool for Psy-
chology, Sport, sleep, and Serious gaming/VR stu-
dies. ABM realized B-Alert X10 (13 channels) and
B-Alert X24 (24 channels) which are portable bio-
sensors headsets that can measure EEG combined
with some other bio-signals such as ECG and provide
quick and valuable insight into the cognitive function
and mental state of the user.
OpenBCI is an open-source bio-sensors board
capable of measuring EEG, EMG, and ECG sig-
nals. OpenBCI can support 4/8/16 and wet/dry elec-
trodes which are sold separately. IMEC develo-
ped EEG headset with 8 channels to monitor Emer-
gency Room and Intensive Care Unit patients. Omi-
lex sold ModularEEG which is a 2 channels open-
source hardware known as OpenEEG. Neurosky de-
signed MindWave; a single channel EEG using one
dry electrode on the forehead (FP1) for everybody
use. Emotiv is another company that developed mo-
bile bio-sensors. EPOC+ is a 14 channels and In-
sight is a 5 channels from Emotiv that use dry elec-
trodes and are capable of providing the following me-
trics to the users: (i) Engagement/Boredom which re-
flects long-term alertness and the conscious direction
of attention towards task-relevant stimuli, (ii) Excite-
ment (Arousal) that reflects the instantaneous arousal
towards stimuli associated with positive valence, (iii)
Stress (Frustration), and (iv) Meditation (Relaxation).
Versus is EEG headset with 5 channels and dry elec-
trodes designed for athletic peak-performance neuro-
feedback training through customized exercise pro-
tocols to improve mental acuity, concentration, and
sleep management. Muse Headset from Interaxon is
an easy-to-use 4 channels headband for concentration
and meditation training. Melon is a slim EEG he-
adset with 4 dry electrodes for focus neurofeedback.
iFocusBand is a neoprene headband with 3 flexible
woven electrodes targeted primarily for sports perfor-
mance training. Table 2 provides a review of existing
market bio-sensors and highlights the main perfor-
mance specifications including the number of chan-
nels, electrode placement, data transfer technology
and sampling rates as well as the battery autonomy
and the corresponding cost whenever provided.
6 PRODUCTS RANKING &
DISCUSSION
Notable efforts are taking place to promote bio-
sensors technologies for pioneer applications. To
our knowledge, there exists practically very few bio-
sensors product intended for driver drowsiness de-
tection on the market. Most of existing products
provide bio-signals monitoring for general research
usage or for medical and neurofeadback applications
such as training, sport, gaming, etc. In the context of
driving monitoring, more efforts are needed to meet
performance specifications to develop efficient drow-
siness detection system. For instance, high precision
products are bulky and rely upon a large number of
channels (e.g., 64-256), which is cost non-effective
and makes it difficult to do fast artifact removal. Furt-
hermore, electrode placement is too technical due to
the requirement for electrodes, gel, wiring, etc. The
use of dry electrodes is promising to reduce the cost
and time required for data collection but novel techni-
ques are needed to improve the accuracy of measured
signals. Lower cost products come with reduced reso-
lution (e.g., 4-16 channels) but with increased porta-
bility. Although, these devices are cost effective and
more comfortable, they either suffer from low accu-
racy and require additional signal inputs such as EOG,
ECG, EMG to maintain high accuracy.
In the context of driver drowsiness detection, it
would be preferable that the bio-sensor system is less
obtrusive and composed with multiple bio-sensors es-
pecially EEG and EOG (Golz et al., 2010), with few
but sufficient number of channels, active electrodes,
low-power communication technology with accepta-
ble sampling rate and battery autonomy. To facilitate
the choice of suitable hardware for drowsiness de-
tection, we have ranked the reviewed bio-sensor pro-
ducts in Sec. 5 using the performance specifications
discussed in Sec. 4. As multiple bio-sensors are nee-
ded, we ranked with (1,2,3,4) whenever EEG, ECG,
EMG, EOG are supported. Electrode type is ranked
with 1 for wet and 2 for dry. Electrode placement is
ranked only for EEG from 1 to 6 for (Fp) (F) (C) (T)
(P) (O) locations. We ranked the number of channels
with 1/2/3/4/5 for 64-256/32-64/16-32/8-16/1-8 chan-
nels. Portability is ranked following the data transfer
technology as 1/2/3/4 for USB, Wifi, BLE, RF
2
. Arti-
fact removal is ranked with 0 or 1 for the existence of
the mechanism. Battery lifetime is ranked as 1/2/3/4/5
for 1-4/4-8/8-12/12-16/16-24 hours autonomy. The
2
BLE: Bluetooth Low Energy marketed as Bluetooth
Smart. RF: Proprietary RF refers to any radio frequency
specific to an original equipment manufacturer OEM and it
is under 928MHz.
SENSORNETS 2018 - 7th International Conference on Sensor Networks
104
software is ranked with 0/1/2 when the signal proces-
sing software is provided and wither is commercial
or open-source software. Finally, the cost is ranked
with 1/2/3/4/5 for price ranging in [50k 100k]/[25k
50k]/[10k 25k]/[1k 10k]/[0 1k]$.
Table 1 shows the results of existing bio-sensor
products ranking. It can be observed that Open-
BCI, Enobio, and DSI10/20 are the top ranked pro-
ducts that met the required performance specifications
among others. This ranking is based on our study of
physiological based-sensors technologies and we be-
lieve that it is not the only evaluation and ranking met-
hod to access the performance of such technologies.
Although some performance metrics were not taken
into consideration in our ranking (e.g., device com-
fort), we think that the proposed ranking tool help in
choosing the most appropriate hardware products to
develop efficient drowsiness detection system.
7 CONCLUSION
Driver drowsiness poses a major danger for public
safety. Monitoring driver’s alertness is of high im-
portance to prevent grand number of incidents. Exis-
ting drowsiness detection technologies such as vehi-
cle and video-based have limited accuracy and work
well in specific conditions. Recently, a number of
portable bio-sensor devices have rapidly attracted the
research interest to circumvent the drive drowsy pro-
blem under any condition. These promising devices
can objectively capture the drowsiness state by moni-
toring physiological signals of drivers and alert them
in real-time. However, the choice of the hardware
must trade-off some performances such as signal qua-
lity and the cost. This paper discusses a number of
performance specifications required by such systems
and rank the existing market physiological sensor pro-
ducts following these specifications providing the re-
search community with a tool to make the appropriate
hardware choice for design consideration of efficient
yet low-cost driver drowsiness detection. We plan to
perform experimental comparison tests between some
existent market platforms in our research agenda.
ACKNOWLEDGEMENTS
This work was part of WISSD Project carried out
in Heudiasyc Lab and was co-funded by the French
Regional Program (Hauts-de-France), and the Euro-
pean Regional Development Fund through the pro-
gram FEDER.
Table 1: Bio-Sensors Products Ranking.
Product
Number of Channels
Electrode Placement
Type of Electrode
Multi-Sensors
Portability
Artifact Removal
Battery Autonomy
Software
Cost
Total
ActiCHamp-64 2 6 1 1 1 1 2 2 2 18
ActiCHamp-32 3 6 1 1 1 1 2 2 2 19
ActiCap 4 6 2 1 1 1 2 2 3 22
eego/rt sports 2 6 1 3 1 1 2 1 2 19
Active Two-128 1 6 1 3 1 1 2 2 2 19
Active Two-64 2 6 1 3 1 1 2 2 2 20
Active Two-32 3 6 1 3 1 1 2 2 3 22
Active Two-16 4 6 1 3 1 1 2 2 3 23
Active Two-8 5 6 1 3 1 1 2 2 3 24
Cognionics-70 1 6 2 3 3 1 2 1 2 21
Cognionics-40 2 6 2 3 3 1 2 1 2 22
Cognionics-32 3 6 2 3 3 1 2 1 3 24
Cognionics-24 3 6 2 3 3 1 2 1 3 24
Quick-20 3 6 2 3 3 1 2 1 3 24
Sleep Headband 4 3 2 1 3 1 2 1 4 21
G.tec SAHARA-32 3 6 2 1 4 1 2 1 3 23
G.tec SAHARA-16 4 6 2 1 4 1 2 1 3 24
G.tec SAHARA-8 5 6 2 1 4 1 2 1 4 26
Q. DSI10/20 3 6 2 4 3 1 5 1 3 28
Enobio-8 5 6 2 4 3 1 2 1 4 28
Enobio-20 3 6 2 4 3 1 2 1 3 25
Enobio-32 3 6 2 4 3 1 2 1 3 25
B-Alert X10 4 3 1 4 3 1 2 1 4 23
B-Alert X24 3 4 1 4 3 1 2 1 3 22
Quick Caps 1 6 1 4 1 1 1 1 1 17
NeXus-32 3 6 1 4 3 1 5 1 3 27
mBrainTrain Cap 3 6 1 1 3 1 2 1 4 22
OpenBCI-4 5 4 2 3 4 1 5 2 5 31
OpenBCI-8 5 6 2 3 4 1 5 2 5 33
OpenBCI-16 4 6 2 3 4 1 5 2 5 32
IMEC 5 4 2 1 3 1 5 1 3 25
OpenEEG 5 2 2 1 1 0 1 0 5 17
Emotiv EPOC+ 4 4 1 1 4 1 3 1 5 24
Emotiv Insight 5 3 2 1 4 1 1 1 5 23
NeuroSky 5 1 2 1 4 1 2 1 5 22
BrainLink 5 1 2 1 3 1 1 0 5 19
Muse 5 3 2 1 3 1 2 2 5 24
Versus 5 2 2 1 3 1 2 0 5 21
Melon 5 1 2 1 3 1 2 0 5 20
Focus 5 1 2 1 3 1 3 0 5 21
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Performance Specifications of Market Physiological Sensors for Efficient Driver Drowsiness Detection System
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Table 2: Review of well-known Bio-Sensors market products with major performance specifications.
Company Product Channel Electrodes EEG Placement Bio Sensors Data
Transfer
Sampling
rate
(kHz)
Battery
Auto-
nomy
System
Cost ($)
Brain Products ActiCHamp 160 Wet (Fp) (F) (C) (T) (P) (O) EEG USB 10-25 6 hr 96,500
128 Wet (Fp) (F) (C) (T) (P) (O) EEG USB 10-25 6 hr 80,000
96 Wet (Fp) (F) (C) (T) (P) (O) EEG USB 10-25 6 hr 66,200
64 Wet (Fp) (F) (C) (T) (P) (O) EEG USB 25-50 6 hr 49,900
32 Wet (Fp) (F) (C) (T) (P) (O) EEG USB 50-100 6 hr 35,600
ActiCap 16 Wet/Dry (Fp) (F) (C) (T) (P) (O) EEG USB 2-20 6 hr 11,375
ANT Neuro eego/rt sports 64+24 Wet (Fp) (F) (C) (T) (P) (O) EEG EMG EOG USB 2.048 6 hr > 25,000
Biosemi Active Two 256+7 Wet (Fp) (F) (C) (T) (P) (O) EEG ECG EMG USB 2-16 5 hr 75,000
160+7 Wet (Fp) (F) (C) (T) (P) (O) EEG ECG EMG USB 2-16 5 hr 52,000
128+7 Wet (Fp) (F) (C) (T) (P) (O) EEG ECG EMG USB 2-16 5 hr 45,000
64+7 Wet (Fp) (F) (C) (T) (P) (O) EEG ECG EMG USB 2-16 5 hr 27,000
32+7 Wet (Fp) (F) (C) (T) (P) (O) EEG ECG EMG USB 2-16 5 hr 21,000
16+7 Wet (Fp) (F) (C) (T) (P) (O) EEG ECG EMG USB 2-16 5 hr 17,000
8+7 Wet (Fp) (F) (C) (T) (P) (O) EEG ECG EMG USB 2-16 5 hr 13,500
Cognionics Dry Head Set 16+8 Dry (Fp) (F) (C) (T) (P) (O) EEG ECG EMG BLE 0.262 6 hr 15,500
24+8 Dry (Fp) (F) (C) (T) (P) (O) EEG ECG EMG BLE 0.262 6 hr 20,500
32+8 Dry (Fp) (F) (C) (T) (P) (O) EEG ECG EMG BLE 0.262 6 hr 26,500
64+8 Dry (Fp) (F) (C) (T) (P) (O) EEG ECG EMG BLE 0.262 6 hr 42,600
Quick-20 20+8 Dry (Fp) (F) (C) (T) (P) (O) EEG ECG EMG BLE 0.262 6 hr 20,600
Sleep Headband 10 Dry (Fp) (F) (T) EEG BLE 0.262 6 hr 3,800
G.tec g.sahara/nautilus 8 Wet/Dry (Fp) (F) (C) (T) (P) (O) EEG RF 0.25/0.5 8 hr 4,500
16 Wet/Dry (Fp) (F) (C) (T) (P) (O) EEG RF 0.25/0.5 8 hr 9,500
32 Wet/Dry (Fp) (F) (C) (T) (P) (O) EEG RF 0.25/0.5 8 hr 25,000
QUASAR DSI10/20 21 Dry (Fp) (F) (C) (T) (P) (O) EEG ECG EMG EOG BLE 0.24/0.9 24 hr 22,500
NeuroElectrics Enobio 8 Wet/Dry (Fp) (F) (C) (T) (P) (O) EEG ECG EMG EOG BLE 0.25 8 hr 4,995
20 Wet/Dry (Fp) (F) (C) (T) (P) (O) EEG ECG EMG EOG BLE 0.25 8 hr 14,495
32 Wet/Dry (Fp) (F) (C) (T) (P) (O) EEG ECG EMG EOG BLE 0.25 8 hr 24,995
ABM B-Alert X10 9+4 Wet (F) (C) (P) EEG ECG EMG EOG BLE 0.256 8 hr 9,950
B-Alert X24 20+4 Wet (F) (C) (P) (O) EEG ECG EMG EOG BLE 0.256 8 hr 19,950
NeuroScan Quick Caps 256 Wet (Fp) (F) (C) (T) (P) (O) EEG ECG EMG EOG USB 02/0.5 0 81,396
Mind Media NeXus-32 21 Wet (Fp) (F) (C) (T) (P) (O) EEG ECG EMG EOG BLE 2.048 20 hr 23,995
mBrainTrain EEG Cap 24 Wet (Fp) (F) (C) (T) (P) (O) EEG BLE 0.25/0.5 5 hr 6,925
OpenBCI Head Set 4 Wet/Dry (Fp) (F) (C) (T) EEG ECG EMG RF/BLE 0.20 26 hr 199+60
8 Wet/Dry (Fp) (F) (C) (T) (P) (O) EEG ECG EMG RF/BLE 0.25 26 hr 499+60
16 Wet/Dry (Fp) (F) (C) (T) (P) (O) EEG ECG EMG RF/BLE 0.25 26 hr 899+60
IMEC EEG Headset 8 Dry (F) (C) (T) (P) EEG BLE 22 hr 25,000
Olimex OpenEEG 2 Wet/Dry (Fp) (F) EEG USB 0.19/0.5 0 119
Emotiv EPOC+ 14 Wet (F) (T) (P) (O) EEG RF 0.128 12 hr 799
Insight 5 Dry (F) (T) (P) EEG RF 0.128 4 hr 299
NeuroSky Mind Wave 1 Dry (Fp) EEG RF 0.25 8 hr 130
Macrotellect BrainLink 1 Dry (Fp) EEG BLE 0.512 4 hr 373
InteraXon Inc. Muse 5 Dry (Fp) (P) (O) EEG BLE 0.22 5 hr 299
SensLabs Versus 5 Dry (F) (C) EEG BLE 0.25/1.28 5 hr 399
Melon Inc. Head band 1 Dry (Fp) EEG BLE 0.25 8 hr 149
Focus IFocusBan 2 Dry (Fp) EEG BLE 0.25 12 hr 500
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