Engineering, 2013, 5, 93-97
doi:10.4236/eng.2013.55B019 Published Online May 2013 (http://www.scirp.org/journal/eng)
Development of 16-channels Compact EEG System Using
Real-time High-speed W ir eless Transmission
B. R. Myung1, S. K. Yoo2
1Graduate Programs of Biomedical Engineering, Yonsei University, Seoul, Korea
2Correspondence author, Department of medical Engineering, College of Medicine, Yonsei University, Seoul, Korea
Received 2013
ABSTRACT
For the convenien ce of people with disability and for normal people, a demand fo r intelligent interfaces is ev er increas-
ing and therefore related studies are actively being conducted. Recently a study is being conducted to develop an inter-
face through face expression, movement of the body and eye movements, and further more active attempts to use elec-
trical signals(brainwave, electrocardiogram, electromyogram) measured from the human body is also actively being
progressed. In addition, the development and the usage of mobile devices and smart devices are promoting these re-
search activities even more. The brainwave is measured by electrical activities between nerve cells in the cerebral cor-
tex using scalp electrodes. The brainwave is mainly used for diagnosis and treatment of diseases such as epilepsy, en-
cephalitis, brain tumors and brain damage. As a result, the brainwave measurement methods and analytical methods
were developed. Interface using the brainwave will not go through language or body behavior which is the result of the
information processed by the brain but will pass directly to the system providing a brain-computer interface (BCI). This
is possible because a variety of the brainwave appears depending on the human’s physical and mental state. Using the
brainwave with the intelligent brain-computer interface or combining it with mobile devices and smart devices, regard-
less of space constraints, the brainwave measurement should be possible.[4,7] In this study, in order to measure the
brainwave without spatial constraint, 16 channel compact brainwave measurements system using a high-speed wireless
communications were designed. It was designed with a 16 channel to classify the various brainwave patterns that appear
and for estimating the location of the nerve cells that triggered the brainwave. And in order to transmit the brainwave
data within the channel without loss, a high-speed wireless communication must be possible that can enable a
high-speed wireless transmission more sufficient than the Bluetooth, therefore, 802.11 compliant Wi-Fi communication
methods were used to transfer the data to the PC. In addition, by using an analog front-end IC having a single-chip con-
figuration with real-time digital filters, the miniaturization of the system was implemented and in order to verify the
system Eye-blocking was used to observe the changes in the EEG signal.
Keywords: Electroencephalography; High-speed Wireless Transmission; Real-time
1. Introduction
The brainwave(EEG: Electroencephalography) means
the measurement of the displacement using the electrode
that appears as a microscopic signal on the surface of the
brain in forms of synthesized electrical signal which have
occurred by the numerous nerve cells in the brain. The
brainwave signal has temporal and spatial variation de-
pending on the b rain activ ity, cond itio n of the bod y at the
time of measurement and brain function. The measure-
ment of the brainwave is essential for the diagnosis of
brain function and dysfunction. Currently used mainly in
hospitals and doctors will measure and analyze the
brainwave for the diagnosis of diseases such as epilepsy
and brain damage. In the recent years, research has been
actively conducted in order to utilize the brainwave with
the intelligent brain-computer interface (BCI) and hu-
man-computer interface (HCI). For each part of the cere-
bral cortex where most of the nerve cells exist, functions
that appear physically and mentally are different and
estimating the lo cation o f th e activated nerve cells can be
used in a variety of research. In the past, it was verified
by opening the skull but currently a noninvasive method
of medical equipments such as X-ray, CT and MRI is
used. But MRI and CT cause a behavioral constraint to
the patients when measuring because it requires over 30
minutes of measuring time and it is in adequ ate to meas-
ure in daily life. Whereas the brainwave measurement
equipment can be manufactured inexpensively and by
wearing the helmet with attached electrodes can lessen
the constraints on behavior enabling a continuous health

B. R. MYUNG, S. K. YOO
94
monitoring. EEG measurement system used for heath
status monitoring systems such as BCI and HCI are to be
used during a daily life therefore it should be simple to
wear and provide maximum activity.[3,5,6,7]
In this study, while ensuring the user’s activity, a min-
iaturized 16-channel EEG system that uses Wi-Fi module
for transferring the measured EEG data at high speed will
be developed. This EEG system will be de- signed with
16 channels for estimating the location of the various
brainwave pattern category and nerve cells and will be
able to transmit measured brainwave data within the
channel without loss using 802.11 compliant high speed
Wi-Fi modules. Also it will be designed with an analog
front-end IC having a sing le-chip configuration with dig-
ital filters to enable portability and wear. Also in this
article, based on 10-20 system to reference A1 will verify
the system by observing the changes in the EEG signal
by attaching electrodes.
2. Materials
EEG system which was developed in this study uses 802.11
compliant Wi-Fi modules for high speed wireless com-
munications and was designed by using Analog front-end
IC and digital filter for miniaturization.
It is important for wireless EEG system for BCI and
HCI health status monitoring to have low power con-
sumption, so devices with low power consumption such
as Wi-Fi module, A/D converter, memory, micro con-
troller, Analog front-end IC that includes UART module
which can operate under a low power was used.[6,7]
The Figure 1 is a block diagram of the 16 channel
EEG system. This system is configured with EEG meas-
urement part (Analog front-end IC), microprocessor part
and Wi-Fi module part.
The electrodes that are attached to the user’s head for
EEG measurement will be disc electrodes which are at-
tached to the area by paste. Signals received from the
electrodes will be amplified through the Analog Front-
end IC and converted to a digital signal through A/D
converter which will be transferred to the micro process-
sor using the SPI communication. A signal processing
will take place within the microprocessor with an income-
ing signal through the digital filter. This signal through
Figure 1. 16 channel EEG system block diagram.
the UART communication will be transferred as Wi-Fi
communication packets and the Wi-Fi module will use
its wireles s communication to tr ansfer the data to th e PC
and the program will display the measured EEG signal
from the user’s head by channel.
Transferring of data between the microcontroller and
Wi-Fi module is made possible through UART commu-
nication and the transfer rate is 115,200 bps. Wi-Fi can
have maximum transfer rate (802.11 b) of 11 Mbps. 16
channel EEG system has maximum power consumption
of 80 mA, A/D’s resolving ability of 24 bit and sampling
frequency of 512 Hz.[4,5,7]
2.1. Analog front-end
Analog Front-end IC used in this study is developed with
8 channel 24 bit dedicated for EEG/ECG which is
integrated version of Analog Front-end product. It has
power consumption of only 1mW per channel and can
save up to 95% of power with high channel density and
equipment portability which is very suitable IC for this
study. The Figure 2 is an internal block diagram for
Analog Front-end IC.
Analog Front-end IC has 8 input channels. In this
study, EEG is measured by arranging the electrodes in
common reference method thus each input channel’s
negative inp ut is binding with refer ence and EEG’s GND
channel is connected to the Right-Leg-Driven(RLD) cir-
cuit built in the Analog Front-end IC. EEG input signal
measured through the electrodes will go through the in-
ternal amplifier within Analog Front-end IC to be ampli-
fied 12 times which then the signal will be transferred
through the Delta-sigma A/D converter to the micro-
processor through the built in SPI communications mod-
ule. At such time, A/D converter’s sampling frequency is
512 Hz.
2.2. Microprocessor
The microprocessor uses SPI communications to control
and receive data from the Analog Front-end IC and
transfers the data through Wi-Fi module using UART
communication.
Figure 2. Analog front-end block diagram.

B. R. MYUNG, S. K. YOO 95
For the microprocessor, STM32F103 series of ST
Microelectronics was used. STM32F103 series provides
16 MHz, 3.3 v with typical 25 uA current consumption
which has low power characteristics and includes SPI
and UART communications module and suitable to be
utilized for this study. Also with JTAG emulation mode
built in, without the need of surrounding circuits for the
emulation, software upgrade and debugging is possible
even with the chip mounted on the target board.
The Figure 3 is a program flowchart for the KEIL
uvision 4 compiler coded microprocessor.
2.3. Digital Filter
Signal received through the SPI communication is stored
in the internal buffer then through digital filter signal
processing is performed. The brainwave has the fre-
quency band of 0.1 - 100 Hz and the Table 1 shows each
divided f requenci es of t he brainwave .[1]
Figure 3. Micro processor flow chart.
Table 1. EEG wave.
Wave Frequency band
Delta Below 4 Hz
Theta 4 - 7 Hz
Alpha 8 - 13 Hz
Beta 14 - 30 Hz
Gamma Over 30 Hz
When measuring the EEG, in order to see the fre-
quency band of preference, EEG system is equipped with
Low Pass Filter (LPF), High Pass Filter (HPF) and No tch
Filter. Using a program, filter setting value can be se-
lected at the microprocessor.(Table 2)
Each filter is configured with 4th LPF, 2nd HPF, 6th
Notch Filter and designed with the Direct Form-II IIR
Filter.[2]
2.4. Wi-Fi Module
Wi-Fi is grafting of a wireless technology onto a Hi-Fi,
which is a wireless LAN technology enabling high per-
formance communication. It uses the frequency band of
2.4 Ghz and using this technology, a notebook can be
used around the house with a wireless connection. Also
using this high speed technology, 5 PC’s can be con-
nected to share files, graphics, videos and audios. Wi-Fi
can provide 11Mbps of speed and at optimal environment
can be used anywhere in the radius of 500m. Wi-Fi mod-
ule used in this study is WizFi210 by Wiznet. Wizfi210
is Serial to Wi-Fi product that can easily and rapidly
change the previous serial application to Wi-Fi capable
solution. WizFi210 provides the AT Command Set
which allows all functions of the module to be selected
using a serial interface. Therefore, not only the serial
equipment but for microprocessor as well, Wi-Fi can be
easily set-up using the UART. WizFi210 is 801.11b
compliant and supports up to 11Mbps of speed during a
wireless interface. Also uses low power wireless com-
munications technology with an optimal hardware pro-
vides stability and also provides Serial to Wi-Fi solution
with dramatically improved power consumption, there-
fore it is suitable for th is study.[8]
The Figure 4 is an internal block diagram of WizFi-
210.
Wi-Fi module receives EEG data from the microproc-
essor and transfers the data to the PC through wireless
communication. Wi-Fi module operates with the Limited
AP Mode where the wireless communication was possi-
ble by connecting through the AP set by the Wi-Fi mod-
ule using a PC.
2.5. PC EEG Viewer Program
To check the changes in EEG signal measured from the
EEG System developed through this study, PC Viewer
Program was realized using the C# and this program was
created by Visual Studio 2010.
Table 2. EEG frequency setting value.
Filter Frequency value
High Pass Filter 0.159 Hz, 0.53 Hz, 1.09 Hz, 5.31 Hz
Low Pass Filter 30 Hz, 50 Hz, 70 Hz, 100 Hz
Notch Filter 60 Hz

B. R. MYUNG, S. K. YOO
96
The Viewer program function receives the EEG data
sent from the Wi-Fi module and displays the received
signal by channel. After initiating this program, clicking
the button on top left corner, Wi-Fi modul e and connection
will be conducted and it will analyze the header and tail
information of the transferred data packet to display the
EEG signal by channel.
Signals that are displayed can be stored for EEG signal
analysis and the stored signals in file format can be ana-
lyzed by using the Tool. Also High Pass Filter, Lo w Pass
Filter, Notch Filter and Gain can be adjusted to select th e
desired frequency ba n d.
The Figure 5 shows the EEG Viewer Program devel-
oped by this study.
3. Methods
The following is the 16 channel miniaturized EEG sys-
tem developed by this study.
Figure 4. Wizfi210 block diagram.
Figure 5. PC Viewer program.
Similar to the Figure 6 above, using the EEG system
with confirmed Sine Wave through the simulator, have
measured the EEG by having a stable normal person with
its eye open. A paste was attached to the area where the
measurement of the Glass Corporation’s Disc electrodes
was necessary. International 10 - 20 system was based to
attach the electrodes and the EEG was measured as seen
in the Figure 7, Figure 8 and the measured signal is
shown on the Figure 9. Then, the reference electrodes
and the ground electrodes were attached to the A2.[7]
Figure 6. 16 channels EEG system using Wi-Fi.
Figure 7. International 10 - 20 System.
Figure 8. Setup Electrode using EEG Cap.

B. R. MYUNG, S. K. YOO
97
at O2 a signal was measured near the 13Hz and thus Al-
pha Wave measurement was checked.[2]
4. Results
In this study, 16 channel miniaturized EEG system using
the Wi-Fi module’s real-time high speed wireless com-
munication was designed and produced. And using this
system to measure the EEG we have conducted an
Eye-blocking test. Measuring the brainwave to be used
with BCI and HCI health monitoring, it was necessary to
develop a system that is easy to wear having the mobility
during a daily life. To achieve this, in this study, a wire-
less module was used to configure the system to reduce
the activity constraint and also we have selected a single
chip for configuration in order to miniaturize the system
and to reduce power consumption controlling the single
chip using a microprocessor. Also, using a high speed
Wi-Fi module, a lot of data within the 16 channel was
transferred without loss to the PC.
Figure 9. Measurement EEG Signal.
5. Acknowledgements
This work was supported by the National Research
Foundation of Korea(NRF) grant funded by the Korea
government(MEST) (No.2010-0026833) and supported
by the Industrial Strategic Technology Development
Program (10031977-2012-23) funded by the Ministry of
Knowledge Economy (MKE, Korea)
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Figure 11. Power Spectrum of EEG Signal. [6] Ching-Sung Wang, “Design of a 32-Channel EEG Sys-
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In order to analyze the signal by storing it as the data,
the data was brought from the Complexity. Complexity is
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As in Figure 11, a power spectrum for the stored sig-
nal was checked. The Figure 11 is a measurement of
EEG Signal(top) from Fz and EEG signal(bottom) from
O2. Both graphs were measured with an eyes closed and [8] Wiznet, “WizFi210 User Manual V1.14,” 2013,
www.wiznet.co.kr.