EEG and Eye-Blinking signals through a Brain Computer Interface Based Control for Electric Wheelchairs with Wireless Scheme 2 l' 2 Jzau-Sheng Lin , , Kuo-Chi Chen , and Win-Ching Yang
1' , Department o/Computer Science and Information Engineering, 2 Department 0/Electronic Engineering, National Chin-Yi University o/Technology Taichung, Taiwan 411, R,O.C. *Corresponding Author:
[email protected]
Abstract-This paper mainly use simple unipolar electrode to
LED.
capture EEG from the forehead to build a Brain-Computer
learning algorithm for BCI based on combining features to aim
Interface (BCI) based control for electric wheelchairs through
at reducing the training process.
Bluetooth for paralyzed patients. We have normalized and
S
f3,
a,
f)
Additionally,
incorporate interactivity value in a BCI system to control an
we can also extract the eye-blinking
electric wheelchair. And, the BCI system will be constructed to
signals from BCI. Therefore, attention and eye-blinking signals
allow communication between human and machine and to
can be collected as the control signals through a Bluetooth
supply real-time translation of brain and eye-blinking waves
interface and the electrical interface in electric wheelchair. The
into command like "tum to a direction", "moving forward", or
experimental results confirmed that this system can provide a
"stop" to control wheelchair.
convenient manner to control an electric wheelchair.
This paper is divided as follows: Section 2 introduces the
Keywords-EEG; Bel; Wheelchair
I.
[7] demonstrated a semi-supervised
In this paper, we propose a novel and simple technology to
waves to construct two signals such as meditation and
attention.
Liu and Zhao
architecture of BCI; Section 3 demonstrates the proposed hardware and software structures; The steering control of
INTRODUCTION
electric wheelchair is described in Section 4; and Section 5 is the conclusion.
Recently, the research field on Brain-Computer-Interface (BCI) provides a new direction to construct an interactive system which can translate human brainwave into control signals for computer-application devices [1-11]. BCI provides a
II.
communication channel not based on nerves and muscles to allow users to communicate
without
ARCHITECTURE OF BCI
A BCI is a novel communication system which enables
movement with the
users
external world [1]. BCls are the only means to infer user intent
to
translate
Electroencephalography
through direct measures of brain activity usually via EEG
brain (EEG),
actIVIty, into
measured
a control signal
by and
sending to computers. BCI have been exposed as a promising
signals. A BCI system is just to translate EEG signals from a
tool for paralyzed people and healthy people in several
reflection of brain activity into user action through system's
computer applications such as medical assistant devices [8] and
hardware and software. BCI approaches are based on a variety
video games [9].
of strategies to generate control signals which may be the result of auditive or visual stimulation or of imaginary motor tasks.
Fig. 1 shows a schematic of a BCI system. The system involves EEG acquisition, signal processing, and an application
There is a rising tendency in the growing recognition
interface together with the applications. The block of signal
requirements of BCI system. The paralyzed patients can not
processing
operate obiects or communicate their needs, even though their
includes preprocessing,
feature
extraction,
classification.
mental capabilities are integral. These existing BCI systems
Brain-Computer Interface (BCI)
focus on developing new superior control and communication technologies for those with strict neuromuscular disorders.
I
Applications \/R
Videogame
There have been several studies using the signals of brain
•
activity to control machines. For example, BCI systems were constructed to control electric wheelchair [1-3]. Three BCI
Electric devices
approaches have been implemented at Aalborg University SS YEP, MI, and NMI and discussed in reference [4]. A subject independent BCI based on motor imagery was discussed by Lotte et. al. [5]. In reference [6], Soraghan et. al. proposed optical brain-computer interfaces to drive triple wavelength
731
I
and
Fig. 1 The brain-computer interface system Fig. 3 The flowchart for Bluetooth receiver and EEGs classifier
III.
THE PROPOSED STRUCTURE
(
)
Start
As shown in Fig. 2, the framework for the signal processing of EEGs with Bluetooth interface is expressed. The EEG signal was extracted from EEG acquisition. In this system, we used the NeuroSky's headset MindSet to capture EEG and eye
Initialize compass mode to
blinking signals. With headphones on the sensor to read brain waves, the brainwaves were transmitted by the Bluetooth wireless modules. In the receiving part,
,------,---1
we integrated a
Bluetooth module in a personal computer with a software
"direction"
Read EEG value
1---------,
No
�
interface organized by using of MA TLAB. Two types of EEG signal were classified such as raw EEG and long EEG. The raw
v••
EEG signal was used to convert electrical voltage to control the electric wheelchair. Then we classified two kinds of digital signal such as Attention, Meditation from long EEG brainwave. The flowchart for the Bluetooth receiver and EEGs classifier is shown as in Fig. 3. The flowchart of OSP block in Fig. 2 is shown as in Fig. 4. In this block, a compass was used to decide the forward direction of electric wheelchair.
Bluetooth
EEG acquisition
Transm itter Fig. 4 The flowchart of DSP block in figure 2
Wheelchair
\\\
Bluetooth Receiver
�
and
OSP Block (MATLAB
�
h
Control
:
�
Interface
0 .!!
Fig. 2 The framework for the signal processing of EEG Signals with Bluetooth interface
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GOOD
0 '().5
.,
OK]
8 EEGda'a
"0"
594
596
Attention
jC
598 600 Timers Meditation
602
lT�=31':EJ
R eadEEG signal
�
Error
596
598 Timers
600
�
�
596
598 Timers
600
�
Fig. 5 The GUl for the direction selection,EEG raw data,
Yeo
In Fig. 4, we set the operation modes of electric wheelchair
RAW EEG or LongEEG?
RAW EEG
as "forward" and "direction". Initially, the compass was set to "direction" mode. Then we read EEG signals from Bluetooth interface. If a correct brainwave was received, we rotated
Long EEG
compass and displayed EEG raw data, "meditation", and "attention" signals on the GUI panel. The GUI panel for rotating compass, EEG raw data, "meditation", and "attention" signals is shown as in Fig. 5. Additionally, we checked the correct EEG signal to be an eye-blinking or not. If it was not, then we switched power on to order the wheelchair moving forward if the mode is "forward"
732
and changing the mode to "direction". Otherwise, the system
signal through RE-232C and converted the digital value to
set the mode as "forward" mode. Fig. 6 shows the completed
analog volt to provide the speed and torque.
experimental system. The commands for the operation to control the electric wheelchair are shown as in Table 1. In this system, we used a
CONCLUSION
V.
compass panel and one eye-blinking pulse to decide the
In this paper, EEG and Eye-Blinking signals through a
moving direction. Additionally, it will be stopped if the
BCI interface based control for electric wheelchairs with
electric wheelchair detected an obstacle or two eye-blinking
wireless scheme is proposed. In a conventional system, uses
pulses. Then, the electric wheelchair moves forward if a high
require hand to control electric wheelchair. In the proposed
value of "attention" signal is calculated.
system, a simple unipolar electrode was used to capture EEG
In the experimental system, a notebook computer was
and eye-blinking signals from the forehead and transmitted to
used to as the block of Bluetooth receiver and EEGs Classifier,
a notebook computer through a Bluetooth interface. The brain
DSP block, and wheelchair control interface in Fig. 2. Then
and eye-blinking signals were calculated by the notebook
the commands are transmitted to the electric wheelchair
computer and transmitted through RS-232C interface.. The
system through RS-232C interface.
proposed
system
is
a
low
cost
and
easy
control
with
"attention" and eye-blinking signals. NeuroSky's headset MindSet
RS-232C
r
Sunplus SPCE061A
Fig. 7 The architecture of the wheelchair system
ACKNOWLEDGMENT
This work was supported by the National Science Council,
Fig. 6 The experimental system
Taiwan, under the Grant NSC98-2221-E-167-016-MY2. TABLE I THE COMMANDS FOR THE PROPOSED ELECTRIC
RE FERENCES
WHEELCHAIR
IV.
Commands
Extracted signals
Tum to a direction
1 eye-blinking pulse
Moving forward
attention
Stop
2 eye-blinking pulse
THE STEERING CONTROL OF ELECTRIC WHEELCHAIR
[I]
S-. Y. Cho, A. P. Vinod, and K. W. E. Cheng, "Towards a Brain Computer Interface Based Control for Next Generation Electric Wheelchairs ",Int. Can! on Power Electronics Systems and Applications, pp. 1-5,2009.
[2]
K. Tanaka, K. Matsunaga, and H. O. Wang, "Electroencephalogram Based Control of an Electric Wheelchair ",IEEE Trans. on Robotics, vol. 21,no. 4,pp. 762-766,2005.
[3 ]
F. Gal'an, M. Nuttin, E. Lew,P. W. Ferrez, G. Vanacker,J. Philips,and J. d. R. MiII'an, "A brain-actuated wheelchair: Asynchronous and non invasive brain-computer interfaces for continuous control of robots," Clinical Neurophysiology, vol. 119,pp. 2159-69,2008.
[4]
A. F. Cabrera, O. F. do Nascimento, D. Farina, and K. Dremstrup, "Brain-Computer Interfacing: How to Control Computers with Thoughts," First International Symposium on Applied Sciences on Biomedical and Communication Technologies, pp. 1-4,2008. F. Lotte,C. Guan, and K. K. Ang, "Comparison of Designs Towards a SUbject Independent Brain-Computer Interface based on Motor Imagery," 31" Annual Int. Can! of the IEEE EMBS Minneapolis, Minnesota, pp. 4543 4546,Sep. 2-6,2009.
[5]
C. J. Soraghan, C. Markham, F. Matthews and T. E. Ward, "Triple Wavelength LED for Optical Brain-Computer Interfaces," Electronic Letters, vol. 45,no. 8,pp. 3 92-394,April,2009.
[6]
M. Liu and M. Zhao, "A Semi-Supervised Learning Algorithm for Brain-Computer Interface Based on Combing Features," 4'h Int. Can! on Neural Computation, pp. 3 86-390,2008.
The wheelchair is controlled trough two motor drivers to operate
two
DC
motors
providing
both
of steering
and
propulsion. The electric wheelchair has two 12-volt batteries with 31-50 Amp-hr to support 24 V for the whole system motors. The 24 V is directly connected to the motor driver to provide the main power. They have a continuously variable speed from 0 to 4 MPH. The motor driver provides the power to the motor. The block diagram of electric wheelchair system is shown as in Fig. 7. In which the sunplus SPCE61A is used as a
central
control
unit
(CCU)
platform
that
contains
all
algorithms and electronics. The CCU accepted commands
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[7]
J. R. Wolpae,N. Birbaumer,D. J. Mc Farland,G. Pfurtschelier,and T. M. Vaughan, Brain-Computer Interfaces for Communication and Control, Clin. Neurophsiol., vol. 113,no. 6,pp. 767-791,2002.
[8]
A. Lecuyer, F. Lotte, R. B. Reilly, R. Leeb, M. Hirose, and M. Slater, Brain-Computer Interfaces, Virtual Reality and Videogames: Current Applications and Future Trends, IEEE Comput. , vol. 41, no. 10, pp.6672,2008.
[9]
[10]
K. Nielsen,A. Cabrera, and O. Nascimento,"Eeg based bci - towards a better control : Brain-computer interface research at aalborg university," IEEE Transactions on Neural Systems and Rehabilitation Engineering. , vol. 14,no. 2,pp. 202-204,2006.
[11] G. E. Fabiani, D. J. Mc Farland, J. R. Wolpaw, and G. Pfurtscheller "Conversion of EEG activity into cursor movement by a brain-computer interface (BCI), IEEE Trans. on Neural Systems and Rehabilitation Engg.,vol. 12,no. 3,pp. 331-338,Sep. 2004.
A. Cabrera and K. Dremstrup, "Auditory and spatial navigation imagery in brain computer interface using optimized wavelets," Journal of Neuroscience Methods,vol. 174,no. 1,pp. 135-146,2008.
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