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Cursor Control Using Bio-potential Signals
for People with Motor Disabilities
Himabindu J., Vivekanandan S., Sourabh Kanwar and Emmanuel D.S.
School of Electrical Engineering, VIT University, Vellore 632014, India
ABSTRACT: With the increasing prominence of computerization
in the society, the role played by Human Computer Interface
(HCI) has been very important. Many physically disabled
individuals are deterred from using computers due to their
inability to utilize their hand for cursor control. In recent years,
many intuitive interfaces have been developed which make use
of minute movements in various parts of the body and translate
them into machine commands. In this paper we discuss the
acquisition of Bio-potential signals like Electrooculography
(EOG) and Electromyography (EMG) signals from eye
movements and facial muscle contractions. The directional
discrimination achieved can be used for a real time hands free
cursor control. The four input EOG system (with the eye
movements) provides the movement of the cursor in real time
and the two input EMG system (with the jaw clenching) gives
the clicking action for cursor control. The physiological signals
are acquired using the electrodes and processed using the
hardware portion of the design. Based on the variation in
frequency response and threshold values, the signals are
analyzed. The simulation of the mouse control is done using
LabVIEW Data acquisition software. The result is viewed in
comparison with the other attempts made in the field with
possible future advancements in the concept.
Index Terms: Cursor control, EMG, EOG, Human Computer
Interface.
1. INTRODUCTION
In today’s silicon world, computer is an integral part of every
individual’s life. Medical records show that unfortunately there are
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many individuals suffering from physical disabilities [11]. With the
spread of globalization and increased use of computer based
technologies in social life, the motivation for devising alternative
means for communicating with the computer is this group of people
who wish to lead an independent, regular life. The first step lies in
developing an alternate interface with the computer for cursor
movement and selection. Numerous techniques have already been
devised for human computer interface. The group of people aimed
at suffers from severe motor disabilities like spinal cord injuries,
paralysis and so forth that render them incapable of moving their
affected parts, which at times may be the whole body.
1.1 Existing Technologies
One approach is the use of line of sight concept wherein the direction
of gaze is tracked by camera and the appropriate feedback is given
for cursor control, eye gaze tracking (EGT) [4,6,7,8,13]. A relationship
is established between the geometric properties of the eye and the
line of sight with the help of video images captured. This was a
natural method of moving the cursor by just looking at the desired
area on the screen. This method gave quick results but drawbacks
like ‘Midas Touch’ [6,7] i.e. movement of the cursor and selection
for any random stare, were evident. A faster method of tracking
was using the arc length between the pupil and the glint of the eye
as a parameter for determining the line of gaze. But for an object to
be focused, it must fall within one degree of visual arc. This physical
constrain and unintentional selections limit the accuracy of EGT
approach.
Another approach is the development of Brain Computer
Interfaces (BCI) by the utilization of electrophysiological signals from
the brain to control the various commands that may be given to the
computer. Signals like slow cortical rhythms, evoked potentials (EP),
P300 neuronal activity recorded from electrodes implanted in the
scalp have been studied[15]. For instance, P3 is a cognitive EP that
appears approximately 300ms after a task relevant stimulus. As it is
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a non specific response, many responses may be used enabling
control through a variety of modalities. Hence control of the cursor
is possible by finding effective EP combinations.
It has been found that a decrease in mu and beta rhythms is
always associated with movements, especially in the region of the
brain contra lateral to the movement i.e. Event Related Desynchronisation (ERD). After the movement and with relaxation;
an increase in the mu rhythm has been observed i.e. Event Related
Synchronisation. This phenomenon may be accompanied with
imaginative movements as well. These facts make mu/beta rhythms
suitable for input into a BCI. And work by Fabiani et al. [3], and
Pfurtscheller et al. [12] has focused on their use as a source of cursor
control. BCI is very useful as the normal output path ways of brain
are not required for the control signals. It is only limited by the speed
constraints.
Another prominent approach is the use of bio-potential signals
from the various parts of the body measured directly from the skin.
EMG and EOG are two of the techniques used effectively. An
electromyography system detects the electric potential generated
by muscle cells when these cells are electrically or neurologically
activated. When a motor unit (one motor neuron and all the muscle
fibers it innervates) fires, the action potential is carried down the
motor neuron to the muscle. After the action potential is transmitted
across the neuromuscular junction, an action potential is elicited in
all the innervated muscle fibers of that particular motor unit. The
sum of all the electrical activity, known as a motor unit action
potential (MUAP), from multiple motor units is the signal typically
evaluated during an EMG. Hence the acquisition of this potential
from minute facial contractions like jaw clenching with a proper
analysis is an effective input mode for cursor movement control by
people with severe disabilities.
EOG is another technique wherein the resting potential of the
retina is measured as a parameter for cursor control. The increased
negativity of the membrane potential caused by the light rays falling
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on the photoreceptor cells result in a charge separation between the
cornea and the retina of the eyeball, which can vary between
50-3500microvolt from peak to peak. The DC voltage generated by
the eye radiates into the adjacent tissues, which produces a
measurable electric field in the vicinity of the eye. The EOG is
measured by placing a pair of electrodes at opposite sides of the
eyeball and differentially amplifying the signal to obtain the DC
voltage between two sides of the eyeball. The amplitude of this biopotential signal varies as the eye ball rotates towards or away from
each electrode, and thus can be used too determine the horizontal
and vertical eye movements that can be harnessed as control signals.
This paper outlines the use of EOG and EMG with the help of a
six electrode system placed appropriately for acquiring the
respective signals and simulating the accuracy of cursor control with
the help of LabVIEW software. There is an almost linear relationship
between horizontal angle gaze and EOG output up to approximately
±300 of arc. Section 2 details the specifications considered, system
implementation and the hardware design as well as software
implemented. Section 3 explains the classification algorithm;
Section 4 gives tabulated results derived from the tests. Section 5
provides conclusion and recommendations.
2. DESIGN IMPLEMENTATION PROCEDURE
As illustrated in the figure 1-1 of the block diagram, there are three
stages of implementation. The EOG bio-potential amplifier is capable
of detecting frequencies between 0.01-10 Hz, the range at which most
ocular movements operate. Similarly the EMG bio-potential
amplifier should detect frequencies between 70 -5000Hz, the rate at
which action potential are fired during muscle contraction in the
jaw. Sufficient gain is necessary to obtain a strong usable signal and
separately analyze both.
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Figure 2.1: Block Diagram
2.1 Electrode Selection
Surface electrodes were preferred for the purpose. Silver/Silverchloride solid gel electrodes were chosen because the half cell
potential was the closest to zero. These electrodes provide accurate
and clear transmission of surface bio-potentials used when closely
spaced bio-potentials are required.
Figure 2.2: Electrodes
Dimensions: 7.2 mm outer diameter, 4 mm recording diameter: 6 mm high; Lead wire
diameter: 1 mm OD ; Lead length: 1 meter
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2.2 Electrode Placement
The EOG is measured by placing two electrodes in the opposite sides
of the eyes. Electrodes are placed above and below for vertical
movements detection and to the left and right of the eye (the outer
canthi of the eye) for horizontal movement detection. Care is taken
when placing the electrodes to choose a location that will minimize
EMG interference, which may occur when the subject frowns or
speaks. The EMG is measured by using two electrodes on each side
of the jaw towards the end in the cheek area. The placement of the
electrodes on the face is shown in Figure 2.3.
Figure 2.3: Electrode Placement
2.3 Hardware Design Procedure
A three stage differential bio-potential instrumentation amplifier is
designed that amplifies the signals measured from electrodes. EOG
signal is in the micro-volt range (50-3500mV) with frequencies
between 0.01 and 10Hz. The first stage of any EOG bio-potential
amplifier is the instrumentation amplifier which provides the initial
amplification while reducing the effect of the signals such as powerline interference and skin muscle artifacts owing to its high Common
Mode Rejection Ratio (CMMR). These stages serve as a high input
impedance load to the electrodes. In order to have low noise, the
initial gain is kept low around 25 (combined gain of both the stages).
In addition, the two stages are then capacitor coupled to the third
stage to prevent the dc offset with a gain of around 750. This gives
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an overall gain of 18750. This is followed by an appropriate band
pass filter.
Table 2.1
Settings of the Bio-amplifier
High Pass Frequency
Low Pass Frequency
Gain
EOG
Bio-potential
Amplifier
EMG
Bio-potential
Amplifier
0.01 Hz
10 Hz
18,750
70 Hz
5000 Hz
10,000
Similar circuit is used for acquiring the EMG signal which is in
the range of 0.1 to 5mV with frequencies between 70 and 500 Hz.
The AC preamplifiers were set to pre-process the signals with analog
anti-aliasing filters with a gain of 10,000. This is done by changing
the values of resistors and capacitors. The output is obtained in few
volts.
The CMMR of the circuit is maintained large and common mode
gain is kept at minimum for initial stages. Differential gain should
not saturate the bio-potential amplifier.
2.4 Software Design Procedure
The software choice for data acquisition is LabVIEW selected for its
vast graphical capabilities and flexibility in programming. The biopotential amplifier voltage output from the EMG, the vertical EOG,
and the horizontal EOG were fed into channels 0, 1, and 2 of the
DAQ board respectively. The NI DAQ 6024E was the data acquisition
board that took the amplified analog signals. A timer was used to
read about 10 sets of data every second. One sample of data was
read from each channel and stored into an array buffer. Immediate
acquisition was made possible for analysis of each data point. The
acquired signals were then displayed in the input data graph to
give real time plots of the signals that were generated. The array
buffer into which the scaled data is read into was then parsed into
single indexed arrays, which correspond to the individual channels
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Figure 2.4: Bio-potential Amplifier Circuit Diagram
of data read in by the DAQ board. This data samples were run
through a series of comparisions run in a sequence as per the
proposed algorithm.
3. CLASSIFICATION ALGORITHM
The EOG circuits were tested with electrodes for determining the
threshold values. As when the eye is moved left, right, up or down,
the potential generated at the electrode was roughly around 12 to
15 volts. When accounting for signal drift and arbitrary eye
movement in the neutral direction, the EOG output fluctuates about
zero volts, but will not exceed 5V in magnitude. Thus a threshold
magnitude level of 7 was used for indicating eye movement. With
the EMG circuit, the clenching of the jaw muscle indicated a change
in voltage from 0V (relaxed). A threshold of 2.8 was chosen so that
any external noise or arbitrary movement of the jaw will not trigger
the indicator.
Table 3.1
Threshold Values
Voltage
16
Right
Left
Upward
Downward
>7V
< –7V
>7V
< –7V
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The channel data is compared with the threshold values. A value
from the vertical index greater than 7 while the horizontal index
value lies between –7V to 7V indicated vertical movement (Up, in
this case). Similarly vertical index value less than –7V would indicate
down. To determine if the eye had moved right, the comparison
sequence would run check to see if the signal was less than –7, and
that the vertical detection was between 7 and –7. This would indicate
that the eyes had moved right. For various combinations the
movement in eight directions (right, left, up, down, bottom-left,
bottom-right, top-left and top-right) was mapped.
Similarly for the EMG signal, any value above 2.8 indicated true
i.e. ‘click’ action else false. For test simulations, an LED matrix was
designed to check if the correct LED glowed for the given input.
Each direction has its own indicator light and would light up if the
signal was found match with any of the eight indicator sequences.
These indicator lights serve as a way to indicate the working of cursor
control.
4. RESULT AND DISCUSSION
The setup was tested by fixing the electrodes on the subject’s face.
The indicator lights worked very well and responded immediately
to give the user an immediate response. Though the precision was
not achieved, the user could move the mouse cursor within
reasonable proximity of where the mouse cursor is desired and at a
fast rate. The subject when asked to clench the jaw muscles casually
and purposefully, the difference was detected due to the threshold
value and the click function was implemented. Table 4.1 verifies
that the software logic for the portion of the program simulating
directional control of a computer mouse works as proposed and
designed.
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Table 4.1
Verification of Directional Logic in LabVIEW Program
Vertical
EOG Output
Voltage(volt)
Horizontal
EOG Output
Voltage(volt)
0
+7
–7
0
0
+7
+7
–7
–7
0
0
0
+7
–7
+7
–7
+7
–7
Directional cell turned
On
None
V+
V–
H+
H–
V+H+
V+H–
V–H+
V–H–
Up
Down
Right
Left
Top-Right
Top-Left
Bottom-Right
Bottom-Left
Figure 4.1: Indicator Grid Showing Bottom-left Direction for
Some Sample Signal Input
The indicator lights can eventually be used as a calibration tool.
Although it is desirable to have a mouse control sensitive enough to
be moved with the slightest movements of the eye, this could not be
achieved due to the ability of the human eye to focus on different
depths or use peripheral vision to see. As the resolution of the
movement of the eyes is not mapped as precisely to the monitor as
desired, there is no real way to measure the exact location of where
the user is looking at. However, the program was successful at
mapping the eye movements to relatively close proximity of where
the user would like to have the cursor.
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This novel method of using EMG signal for clicking action
overcomes many problems like unintentional selection, fixed
position of head and others. Compared to EGT technique, the use
of EOG for cursor control is faster and more accurate. Many People
with severe disabilities are found to have the ability to control eye
movements, where EOG plays a major role. Compared with EEG,
EOG signals have relatively high amplitude, relationship between
EOG and eye movements is linear, and the waveform is easy to
detect (Zhao et. Al., 2008).
However, the power consumption as well as the fabrication cost
is increased with the effective suppression of the DC drifts, artifacts
and interference calls for the implementation of very sharp, high
order filters.
5. CONCLUSION AND FUTURE PROSPECTS
Developing HCI using different biosignals will improve the quality
of living of the disabled people. A general mouse control was
realized by harnessing the physiological signals thus justifying a
real time hands free cursor control. The system is able to continuously
acquire EOG and EMG signals from eyes and the jaw to simulate
low resolution directional control and clicking action of the computer
mouse. In the bio-potential amplifiers, excess noise is sufficiently
cut off via band pass filters and there is sufficient gain to provide a
usable signal for data analysis using LabVIEW.
Many experiments have already been carried out in realizing a
hands free cursor control. From the various data, it is observed that
the precision and efficiency of each one various depending on the
input mode used and the signal processing methods applied later.
In future, efficient signal acquisition would be possible with
improvement in the properties of the electrodes used. Alternative
modes of signal acquisition like magnetic sensors can be used in
place of electrodes which may even help minimize the skin-electrode
attachment. Research may be carried out for finding innovative ways
of placing the electrodes for getting better signals keeping the
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discomfort of the patient in the mind and allowing more scope for
the patient to move.
To improve the resolution a more sensitive method of recording
the signal from the eye is needed. The quantization of the signal
acquired can be increased in order to improve the precision with
increased grid levels and making the movement of the cursor
proportional to the varying voltage. Better algorithms should be
found out for the expected functionality. Application of Artificial
Neural Networks (ANN) for classification of the signal is a promising
area of research. For marketability purposes, suitable software
mouse drivers have to be created for the operating system to support
the cursor control outside the LabVIEW environment such that data
can be given as an understandable input to the computer.
However, the overall design successfully supports the idea that
physiological signals act as an effective input for hands free cursor
control.
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