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A portable wireless eye movement-controlled
Human-Computer Interface for the Disabled
ABSTRACT
A portable wireless eye movementcontrolled Human-Computer Interface
which can be used for the disabled who
have motor paralysis and who cannot
speak in multiple applications (such as
communication aid and smart home
applications) is described here. This
Interface consists of four major parts:
(1) Surface electrodes
(2) A two-channel amplifier
(3) A laptop (or a micro-processor)
(4) A ZigBee wireless module.
Horizontal
and
vertical
ElectroOculography (EOG) signals are measured
using five surface electrodes placed on
the head .The vertical electrodes are
placed about 1.0 cm above the right
eyebrow and 2.0 cm below the lower lid
of the right eye, the horizontal electrodes
are placed 2.0 cm lateral to the each side
of outer canthi and the last electrode is
placed on user’s forehead to serve as a
ground. The two-channel amplifier is
comprised of instrumentation amplifiers,
band-pass filters and shift circuits. The
EOG signals are sampled at the rate of
250Hz and then sent to a laptop or a
micro-processor for signal processing
which is based on the method of
mathematical morphology to recognize
the direction of eye movements and
voluntary eye blink. The ZigBee wireless
communication technology, which is
proved to be reliable, low-power and
cost-efficient, is used in the portable
interface. This interface provides a
flexible method for the disabled to
improve the life quality.
I. INTRODUCTION
Persons with severe diseases, such as
amyotrophic lateral sclerosis (ALS),
brainstem stroke, brain or spinal cord
injury,
cerebral
palsy,
muscular
dystrophies, multiple sclerosis, etc., have
difficulty conveying their intentions and
communicating with other people in daily
life. With the development of HumanComputer Interface (HCI), methods have
been developed to help these people for
communication. Unlike traditional HCIs (a
keyboard, or a mouse, etc.), modern HCIs
have played an important role in the area
of rehabilitation.
HCIs can be divided into cortical (all
interfaces that exploit information
collected from the human brain cortical
relays) and non-cortical (all interfaces
that do not access the signals generated
by the human cortex directly). In the
present study, we describe a novel
portable wireless eye movementcontrolled HCI for the disabled. This
interface is a real-time communication
control system based on EOG signals.
There are two main differences between
our system and others mentioned above:
(1) Designing and implementing a
mathematical morphology method to
preprocess original EOG signals.
(2) Including a wireless module based
on the ZigBee protocol to increase the
scope of applications (communication aid,
smart home.
applications, etc.) of this system.
II. SYSTEM OVERVIEW
The system we have developed consists of
four major parts:
1. Five surface electrodes
2. A two-channel amplifier,
3. A laptop (or a micro-processor)
4. A ZigBee wireless module.
Fig 1: Overview of the EOG-based
wireless Human-Computer Interface.
Fig 1 is the schematic diagram of this
system and the whole system adopts the
star topology which is also used. In this
system, horizontal and vertical EOG
signals are measured by five surface
electrodes placed around eyes. After a
two-channel amplifier, the EOG signals
are sampled at the rate of 250 Hz and
then sent to a coordinator node which is
connected with a laptop or a microprocessor through ZigBee wireless
communication technology. The software
on the laptop or micro-processor
recognizes the direction of eye movement
and voluntary eye blinking. Programs
(typewriter, patient assistant software,
etc.) in laptop or remote devices (TV,
lamps, telephone, etc.) can be controlled
by the recognized results.
III.ELECTRODES
PRINCIPLE
AND
THE
The cornea of the eye is electrically
positive relative to the retina of the eye
and the potential is slowly varying when
eyes move. The standing potential can be
measured by electrodes placed around
the eyes. The EOG value varies from 0.053.5 mV with a frequency range of about 0100 Hz. In this paper, there are five
electrodes in all which are classified as
horizontal, vertical
and reference
(ground) electrodes. As showed in Fig 1,
the vertical electrodes are placed about
1.0 cm above the right eyebrow and 2.0
cm below the lower lid of the right eye,
the horizontal electrodes are placed 2.0
cm lateral to the each side of outer canthi.
And the last electrode is placed on user’s
forehead to serve as a ground.
If the eyes move left, horizontal EOG
(HEOG) signal which is the difference
between signals collected by electrode
HEOL and HEOR acquires a positive
voltage value. If the eyes turn right, HEOG
signal changes into a negative voltage
value. Identically, if the eyes move from
the central position towards upside,
vertical EOG (VEOG) signal which is the
difference between signal collected by
electrode VEOU and VEOL acquires a
positive voltage value. If the eyes move
downside, VEOG signal changes into a
negative voltage value. An eye blinking
can be described by EOG signals as a peak
in VEOG but a flat in HEOG. We can
distinguish the voluntary and involuntary
blinking by the value and duration of the
peak mentioned above.
IV.AMPLIFIER
The horizontal and vertical eye
movement signals captured by the
electrodes were then transmitted to a
two-channel amplifier which consists of
(1) preamplifiers
(2) band-pass filters
(3) shift circuits
(4) right-leg driven circuits
(5) power supply.
The preamplifier is a micro-power
instrumentation amplifier for accurate
and low noise differential signal
acquisition. The gain of the preamplifier is
set to be 21 with a single external
resistor. The band-pass filter (0.01-41 Hz)
is provided with two Sallen-Key filters
(One second-order high-pass filter and
one fourth-order low-pass filters). The
following circuits are secondary amplifier
with variable gain and shift circuit to
transform the signal level into the range
Fig 1: Overview of the EOG-based
wireless Human-Computer Interface.
Fig 2: EOG signals during eye movement
and blanking. (a) HEOG signals. (b) VEOG
signals. of 0 V to 3 V for adapting the
following analog-to-digital converter
(ADC). Right-leg driven circuit connected
with the reference electrode is used to
reduce the common-mode components in
the signal. Power for the board is supplied
by one common 6V battery, which is then
transformed into ± 3.3 V with AMS1117
and MAX828 respectively.
V. WIRELESS MODULE
The Wireless module takes responsibility
for transmitting two-channel EOG signals
from one node attached to the user’s body
to the coordinator node connected with
the laptop. Meanwhile, the coordinator
can send messages to other remote
controllers (TV, lamp, telephone, etc). The
ZigBee
wireless
communication
technology, which is proved to be reliable,
low-power and cost-efficient, is used in
this system.
The module is established using CC2430,
which is a true System-on-Chip solution
specifically tailored for IEEE 802.15.4 and
ZigBee
applications.
The
CC2430
combines RF transceiver with an
industry-standard enhanced 8051 MCU,
32/64/128 KB flash memory, 8 KB RAM
and many other powerful features. At the
transmission node, analog EOG signals
from amplifiers are sampled at the rate of
250Hz and transmitted. At the reception
node, EOG signals are transported to
laptop with RS232-USB interface for
signal processing. In the prototype
software, the protocol is based on a
ZigBee stack called MSSTATE_LRWPAN
which implements a ZigBee subset
wireless stack. The program in CC2430 is
based on this protocol completely.
VI. EOG SIGNAL PROCESSING
The method is based on the mathematical
morphology (MM), differential and
integral algorithms to recognize the
direction of eye movement and voluntary
blinking. VEOG signals are used to detect
up/down movement and voluntary eye
blinking, while HEOG signals are used to
detect left/right movement.
Fig 3: The flowchart of EOG signal
processing.
MM Algorithm: The method of MM is
widely used in ECG signal processing and
other fields . It provides a good way to
remove drift and magnify feature of the
signal. The operators of MM are dilation,
erosion, opening and closing. Opening
generally smoothes the contour of an
object, breaks narrow isthmuses, and
eliminate thin protrusions. Closing also
tends to smooth sections of contours and
which generally fuses narrow breaks and
long thin gulfs, eliminates small holes, and
fills gaps in the contour. In our work, we
only used symmetrical sequences as the
structuring element. The result of VEOG
signals after MM filter.
Differential Algorithm: The VEOG
signals, after MM filter, are feed into the
differential module implemented.
9
19
Y(n)= ∑
𝑖=0
x(n + i + 10) − ∑𝑖=0 x(n + i)/20
---------------> (1)
Where x (n) is the VEOG signals after MM
filter, and y (n) is the result after the
differential module.
Integral Algorithm: The difference of
original VEOG signals (delay 2N points, N
is the length of the structuring element in
MM algorithm) and signals after MM filter
can be used for eye blinking recognition.
Because the peak value of voluntary
blinking is much larger than involuntary
blinking, we can distinguish those two
kinds of blinking by the integral module
using (2) and the threshold.
19
y(n) = ∑ x(n + i)/20
𝑖=0
------------> (2)
Where x(n) is the difference of original
VEOG signals (delay 2N points) and
signals after MM filter, y(n) is the result
after the integral module. Decision
Module: In Fig. 4, S1, S2 and S3 are the
results by the methods mentioned above.
Threshold1 is the voluntary eye blinking
threshold, Threshold2 is the involuntary
eye blinking threshold, Threshold3 is the
movements (up/down) threshold, and
Threshold4 is the movements (left/right)
threshold. We can distinguish eye
blinking (voluntary and involuntary) and
eight-direction movement through these
thresholds.
Fig 4:Example of VEOG signal processing.
VII.APPLICATION SOFTWARE
TEST
We have developed two application
programs to test this system: the
typewriter and the patient assistant
software. The typewriter user interface is
showed in Fig. 7a. Users make the cursor
move up, down, left and right to select a
letter in the table. The letters selected (by
voluntary eye blinking) are showed above
the table. The patient assistant software is
showed in Fig. 7b. In this application,
users move the cursor by eightdirectional eye movements, and the size
of icon selected is enhanced. At the same
time, the LED which indicates the
direction of eye movement is lighted by
the controlling of the remote ZigBee
module. These two applications indicate
that the portable wireless eye movementcontrolled we developed is doable. The
performance (bit rate and latency) of this
system can be calculated as the following
paragraphs.
Where T is the delay time, N is the
number of sampled points delayed, and Fs
are the sampling frequency.
VIII. BIT RATE
Ttotal=T1+max (T2, T3) ----------> (5)
The bit rate of this system is calculated.
Where B is the bit rate per selection, N is
the possible choices, and P is the
accuracy. Bit rate per minute of the
system can be obtained from B multiplied
by the number of selections in a minute.
The sampling frequency is 250 Hz, and
100 sampled points are delayed during
the method of mathematical morphology.
20 sampled points are delayed both in the
processing of differential and integral
algorithms. Thus, the total time delay
Ttotal calculated by using (5) is 0.48 s.
B=log2 ∗ 𝑁+Plog2 ∗p+(1-p)log2 (1-p)/(n-1)
----------> (3)
Several subjects were asked to test the
patient assistant software (N in this
application is 8). They got accuracies all
above 80% and could make at least 10
selections per minute. Therefore, the bit
rate of this system is above 17.17 bits per
minute.
IX. LATENCY
The Latency of this system is related with
the method of real-time signal processing.
The delay time of mathematical
morphology, differential, and integral
algorithms is T1, T2 and T3, respectively.
The delay time of each algorithm is
calculated by using (4). Thus, T1 = 0.4 s,
T2 = T3 = 0.08 s.
T = N / Fs
---------> (4)
X.FACTORS
INFLUENCING
PERFORMANCE
The EOG signals are mostly concentrated
on the low frequency, especially near the
DC component where lots of useful
information locates. Therefore, the cut-off
frequency of the high-pass filter should be
set as low as possible (0.01 Hz in this
system) otherwise the eye movement
signals would decline rapidly rather than
be hold for a long time. Because of the MM
method, the influences of the drift and
other noise were reduced. However, slow
involuntary blinking (duration above 0.8
s) would be recognized as eye movement
(Up), which is a mistake. So when using
this system, slow involuntary blinking
should be performed as few as possible.
XI. IMPROVEMENT OF THE
SYSTEM IN FUTURE
Four thresholds which are measured in
advance were set manually at the
initialization stage of the software.
Clearly, it is time-consuming and may
result in unnecessary errors. Later
evelopment should make the thresholds
set automatically through a test program
before use. Meanwhile, the thresholds can
be updated during test period autoadaptively according to the user’s current
state. For instance, the amplitude of the
EOG signals would change slowly in the
latter stage because of fatigue. The
current signal process program is
implemented on a laptop which also
provides user interface on the screen. If
we do not need user interfaces (e.g.
controlling remote devices), the process
program can be carried out only in a
micro-processor which is integrated in
the coordinator node. Then the processed
results were sent wirelessly to the remote
device which is attached with a ZigBee
reception node.