J. Biomedical Science and Engineering, 2013, 6, 1050-1055 JBiSE
http://dx.doi.org/10.4236/jbise.2013.611131 Published Online November 2013 (http://www.scirp.org/journal/jbise/)
Influence of stimuli color on steady-state visual evoked
potentials based BCI wheelchair control
Rajesh Singla1, Arun Khosla2, Rameshwar Jha3
1Department of Instrumentation and Control Engineering, Dr BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India
2Department of Electronics and Communication Engineering, Dr. B. R. Ambedkhar National Institute of Technology Jalandhar,
Punjab, India
3Director General, IET Bhaddal, Ropar, Punjab, India
Received 21 August 2013; revised 25 September 2013; accepted 8 October 2013
Copyright © 2013 Rajesh Singla et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
In recent years, Brain Computer Interface (BCI) sys-
tems based on Steady-State Visual Evoked Potential
(SSVEP) have received much attention. This study
tries to develop a SSVEP based BCI system that can
control a wheelchair prototype in five different posi-
tions including stop position. In this study four diffe-
rent flickering frequencies in low frequency region
were used to elicit the SSVEPs and were displayed on
a Liquid Crystal Display (LCD) monitor using Lab-
VIEW. Four stimuli colors, green, red, blue and violet
were used to investigate the color influence in SS VEP s.
The Electroencephalogram (EEG) signals recorded
from the occipital region were segmented into 1 sec-
ond window and features were extracted by using
Fast Fourier Transform (FFT). One-Against-All ( O A A ) ,
a popular strategy for multiclass SVM, is used to clas-
sify SSVEP signals. During stimuli color comparison
SSVEP with violet color showed higher accuracy than
that with green, red and blue stimuli.
Keywords: Steady-State Visual Evoked Potential; Brain
Computer Interface; Support Vector Machines
1. INTRODUCTION
The Brain Computer Interface (BCI) system provides a
direct communication channel between human brain and
the computer without using brain’s normal output path-
ways of peripheral nerves and muscles [1,2]. By acquiring
and translating the brain signals that are modified accor-
ding to the intentions, a BCI system can provide an alter-
native, augmentative communication and control options
for individuals with severe neuromuscular disorders,
such as spinal cord injury, brain stem stroke and Amyo-
trophic Lateral Sclerosis (ALS).
Electroencephalography (EEG) is a non-invasive way
of acquiring brain signals from the surface of human
scalp, which is widely accepted due to its simple and safe
approach. The brain activities commonly utilized by EEG
based BCI systems include Event Related Potentials
(ERPs), Slow Cortical Potentials (SCPs), P300 potentials,
Steady-State Visual Evoked Potentials (SSVEPs) etc.
Among them SSVEPs are attracted due to its advantages
of requiring less or no training, high Information Trans-
fer Rate (ITR) and ease of use [1,3].
SSVEPs are oscillatory electrical potentials that are elic-
ited in the brain when the person is visually focusing his/
her attention on a stimulus that is flickering at frequency
6 Hz or above [4]. These signals are strong in occipital
region of the brain and are nearly sinusoidal waveform
having the same fundamental frequency as the stimulus
and including some of its harmonics. By matching the
fundamental frequency of the SSVEP to one of the stimu-
lus frequencies presented, it is possible to detect the tar-
get selected by the user. Considering the amplitudes of
SSVEPs induced, the stimuli frequencies are categorized
into three ranges, centred at 15 Hz low frequency, 31 Hz
medium frequency and 41 Hz high frequency respec-
tively [5].
There are many research groups that are designing
SSVEP based BCI systems. Lalor et al. [6] developed the
control for an immersive 3D game using SSVEP signal.
Muller and Pfurtscheller [7] used SSVEPs as the control
mechanism for two-axis electrical hand prosthesis. Re-
cently, Lee et al. [8] presented a BCI system based on
SSVEP to control a small robotic car.
One of the main considerations during the develop-
ment of a BCI system is to improve the classifiers accu-
racy, as that can affect the overall system accuracy and
thus the ITR. In this research work the Support Vector

R. Singla et al. / J. Biomedical Science and Engineering 6 (2013) 1050-1055 1051
Machine (SVM) method was carried out for the classifi-
cation of a multiclass SSVEP signal.
The retina of human eye contains rod and cone cells.
The rod cells detect the amount of light and cone cells
distinguish the color. There are three kinds of cone cells
and are conventionally labeled as Short (S), Medium (M),
and Long (L) cones according to the wavelengths of the
peaks of their spectral sensitivities. S, M and L cone cells
are therefore sensitive to blue (short-wavelength), green
(medium-wavelength) and red (long-wavelength) light re-
spectively. The brain combines the information from each
cone cells to give different perceptions to different colors;
as a result, the SSVEP strength elicited with different co-
lors of the stimuli will be different.
2. MATERIALS AND METHODS
2.1. System Configuration
Figure 1 illustrates the block diagram of the proposed
SSVEP based wheelchair control system, which includes
visual stimuli developed using LabVIEW and displayed
on a Liquid Crystal Display (LCD) monitor, EEG acqui-
sition unit, signal processing unit with feature extraction
and classification algorithms, hardware interface and a
wheelchair prototype.
2.2. Subject
Ten right handed healthy subjects (seven males and three
females, aged 22 - 27 years), with normal or corrected to
normal vision participated in the experiment. All of them
had normal color vision and not had any previous BCI
experience. Prior starting, subjects were informed about
the experimental procedure and required to sign a consent
form.
2.3. Stimuli
The RVS for eliciting SSVEP responses can be presented
Figure 1. Conceptual block diagram of the proposed SSVEP
based wheelchair control system.
on a set of Light Emitting Diodes (LEDs) or on a Liquid
Crystal Display (LCD) monitor [9]. In this study RVS
displayed using LCD monitor due to the flexibility in
changing the color of flickering bars, and were designed
using LabVIEW software (National Instrument Inc., USA).
Four colors: green, red, blue and violet were included in
the experiment. Background color selected as black. Four
frequencies 7, 9, 11 and 13 Hz, in the low frequency
range were selected, as the refreshing rate of LCD moni-
tor is 60 Hz [10] and the high amplitude SSVEPs are ob-
tained at lower frequencies [5]. The visual stimuli were
square (4 cm × 4 cm) in shape and were placed on four
corners of the LCD screen.
2.4. Experimental Setup
The subjects were seated 60 cm in front of the visual
stimulator as shown in Figure 2. EEG signals were re-
corded using RMS EEG-32 Super Spec system (Record-
ers and Medicare System, India). The SSVEP potential
recorded from occipital region using Ag/AgCl electrodes
were amplified and connected to the adaptor box through
head box. Adaptor box consist the circuitry for signal
conditioning and further connected to the computer via
USB port. This system can record 32 channels of EEG
data. The electrodes were placed as per the international
10 - 20 system. The skin-electrode impedance was main-
tained below 5 KΩ. The EEG signals were filtered by us-
ing a 3 - 50 Hz band pass filter and a 50 Hz notch filter.
Signals were sampled at 256 Hz and the sensitivity of the
system was selected as 7.5 µV/mm.
In training session the electrodes were placed at the O1,
O2 and Oz regions of the scalp. The reference electrodes
were placed on the right and left earlobes (A1 and A2)
and ground electrode on Fpz. First collected the SSVEP
data for all the four frequencies with green color and
then repeated the experiment for red and violet colors in
another session. Subject 2 performed with blue color
Figure 2. Experimental set up for SSVEP data acquisition
(Courtesy-Department of Instrumentation and Control Engi-
neering, National Institute of Technology, Jalandhar).

R. Singla et al. / J. Biomedical Science and Engineering 6 (2013) 1050-1055 1052
stimuli in order to analyze the effect of blue stimuli on
SSVEP. The interval between the sessions was 10 min-
utes. Initially the subjects were required to close their
eyes for recording 2 minutes of baseline signal and then
given 5 minutes to adapt to the flickering stimulus placed
in front of them.
During experiments, the subjects were directed to focus
on a particular frequency for 5 second duration followed
by 5 second rest period. During focusing, the subjects
were instructed to avoid eye movements or blinking. The
event markers were used to indicate the starting and
ending time of each frequency. In a single trial, each of
the four frequencies was performed three times and the
same procedure was repeated for another three trials. 5
minutes break was given in between each trial. The time
for completing one session was about 30 minutes.
2.5. Feature Extraction
The frequency features of SSVEPs can easily extracted
by using Fast Fourier Transform (FFT) [11]. The EEG
signals recorded from Oz-A2 channel were digitized and
segmented into 1 second time window in every 0.25 se-
conds. MATLAB was used for developing the FFT pro-
gram. Figure 3 shows the amplitude spectra of SSVEP
induced by 7 Hz stimulation. The coefficients at the fun-
damental and second harmonics of all the four target fre-
quencies obtained from the amplitude spectra were con-
sidered as the feature vector for classification.
2.6. Classification
The SVM technique introduced by Vapnik in [12] is ba-
sically a binary classifier which can discriminate between
two classes by using an optimal hyperplane which maxi-
mize the margin between the two classes. Kernel func-
tions provide a convenient method for mapping the data
space into a high-dimension feature space without com-
puting the non-linear transformation [13]. The common
kernel functions are linear, quadratic, polynomial and ra-
dial basis function (rbf).
Figure 3. Amplitude spectra of SSVEP in response to 7 Hz, re-
corded from Oz-A2 channel of subject 4. First and second har-
monics can be found clearly.
SVM training and classification was done by using
MATLAB Bioinformatics toolbox. One-Against-All (OAA)
method [12] was adopted for getting a multiclass SVM.
The formulation of this mode states that a data point
would be classified under a certain class if that class’s
SVM accepted it while rejected by all other classes SVMs.
In this mode four binary SVMs were trained, each for
one of the four frequencies. After training, there develop
a structure having the details of the SVM like the number
of support vectors, alpha, bias etc.
2.7. Hardware and Implementation
The wheelchair prototype is shown in Figure 4. Motor
driver IC (IC L293D) was used to control two motors
(M1 and M2) of the wheelchair. By changing the polarity
of the signal given to the motors, through the motor
driver IC, it is possible to move the motors in both for-
ward and backward directions.
The parallel port of the computer was used to send out
eight data bits. The first four data pins i.e. D0, D1, D2,
and D3 were used to interface the control signal to the
motor IC. Positive and negative of the right motor was
given through D0 and D1 and that of left motor was by
using D2 and D3. Rest of the data pins was not used. In-
terfacing program was developed using MATLAB.
The control commands used to change the polarity of
the motors for each movement of the wheelchair were
presented in Table 1. Forward movement of both right
Figure 4. Wheelchair prototype for SSVEP based BCI control.
Table 1. Control logic for wheelchair movements.
Right Motor (M1)Left Motor (M2)
+ − + − Movement Direction
1 0 1 0 Forward (F)
0 1 0 1 Backward (B)
0 0 1 0 Right (R)
1 0 0 0 Left (L)
0 0 0 0 Stop

R. Singla et al. / J. Biomedical Science and Engineering 6 (2013) 1050-1055 1053
(M1) and left (M2) motor results in the forward direction
motion. Left motor forward and stop position of right
motor will provide right movement of the wheelchair.
Left motor stopped and a forward movement of right
motor results left rotation of the wheelchair. The back-
ward movement of both motor together provides the de-
vice to move backward. The stop positions of both the
motor together results in the stopping of wheelchair.
data were normalized in the range of [−1, +1] Individual
SVMs were trained with different kernel functions. The
kernels with maximum accuracies were selected for OAA-
SVM formulation. For 7 Hz the Polynomial kernel with
order 3 had got an accuracy of 100% for violet color and
for 9 Hz quadratic kernel provides an accuracy of 98.69%
for the same. Higher accuracy for 11 Hz was provided by
linear kernel and is 95.43% for violet color. For 13 Hz
violet color got an accuracy of 100% by using linear
kernel. The OAA-SVM designed with optimal kernels
provides an overall accuracy of 98.53% for violet during
training.
The classifier outputs for each of the four frequencies
and relax state were assigned to the five different move-
ments of the wheelchair. For 7 Hz detection, the output
of the parallel port is [1 0 1 0] and will move the wheel-
chair in forward direction. 9 Hz would give [0 0 1 0] and
will cause a right movement. 11 Hz detection delivers an
output of [1 0 0 0] and will result in the left movement of
the wheelchair. For 13 Hz the parallel port output is [0 1
0 1] which results in a backward movement of the wheel-
chair. The classifier result for the relax state of the user is
[0 0 0 0] and it will stop the wheelchair.
Figure 5 presents the regression plots during training
of SSVEPs elicited by green, red and violet stimuli. The
regression value for green is 0.94021 and that of red is
0.95162. The violet got a regression value of 0.98532.
This proves the superior performance of violet stimuli
over green and red for eliciting SSVEPs.
3.2. Wheelchair Interface
3. RESULTS AND DISCUSSIONS In testing session the subjects were directed to perform
two different sequences i.e. path A and path B, each one
with 8 movements including stop command. Each of the
sequence was performed three times, thus each subject
performed a total of 48 movements. The shape of the
paths is shown in Figure 6.
After the text edit has been completed, the paper is ready
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style your paper. Table 2 presents the sequences required to complete
the paths and corresponding frequencies. The SSVEP data
recorded from Oz-A2 channel was filtered, digitized and
segmented into one second window in every 0.25 sec-
onds and transformed into frequency domain using FFT.
3.1. Classifier Testing Results
The feature vector extracted using FFT were used for
classification. There have two separate data sets each for
two different stimuli colors. The training dataset for each
color consist 150 samples (30 samples for each of the
four frequencies and 30 for rest signal) from each subject
data i.e. a total of 1500 samples in a complete set. The
To reduce the number of wrong selections, a require-
ment of three continuous detection of the same target
was set to produce the particular command to the wheel-
chair.
Accuracy of the system is measured with the accurate
(a) (b) (c)
Figure 5. Comparison of regression plots for SSVEPs elicited by green, red and violet stimuli during classification of SSVEP data
using OAA SVM.

R. Singla et al. / J. Biomedical Science and Engineering 6 (2013) 1050-1055 1054
detections made by the subject out of the total number of
the detections. The accuracy and the ITR of the system
for 10 subjects with three stimuli colors using OAA-
SVM classifier are presented in Table 3. Subject S4 got
100% accuracy by using OAA-SVM classifier with all
the three stimuli colors. Result also indicates that violet
color stimuli promise a SSVEP-BCI wheelchair control
with good accuracy.
Wheelchair with violet stimuli achieves accuracy rang-
ing from 79% - 100% and in which nine subjects got
accuracy higher than 89%. Subject S9 showed compara-
tively poor performance and got accuracy 79.17% with
violet stimuli.
The system was further experimented with OAA SVM
classifier and blue stimuli for subject 2 and obtained 46
correct detections out of 48 total detections. The accu-
racy is 95.83% and ITR is 23.86 bits/min respectively.
4. CONCLUSIONS
In this research OAA-SVM was constructed for SSVEP
data classification. The motivation of this work is to im-
prove the accuracy of SSVEP based BCI system by using
optimal stimuli color. EEG signals were recorded by using
RMS EEG-32 Super Spec system and SSVEP features
extracted using FFT. SSVEPs were elicited using four
different frequencies. Four different stimuli colors green,
red, blue and violet were compared to get better perfor-
mance. The amplitudes of first and second harmonics of
SSVEP data were successfully used as the feature vector
to train the classifier models. The result showed that
SSVEPs with violet stimuli are better than that with
Figure 6. Shape of the paths used in testing session.
Table 2. Sequence of movements and corresponding frequencies.
Movement F R F R F L F Stop
Path A Frequency (Hz) 7 9 7 9 7 11 7 Relax
Movement F R F L F L F Stop
Path B Frequency (Hz) 7 9 7 11 7 11 7 Relax
Table 3. Number of correct detections (score), accuracy [%] and itr [bits/min] for 10 subjects with green, red and violet stimuli using
oaa-svm classifier.
Green Red Violet
Subjects Score Acc. ITR Score Acc. ITR Score Acc. ITR
S1* 46 95.83 23.86 46 95.83 23.86 47 97.92 25.61
S2 45 93.75 22.32 46 95.83 23.86 48 100 27.86
S3 40 83.33 16.06 41 85.42 17.17 43 89.58 19.58
S4* 48 100 27.86 48 100 27.86 48 100 27.86
S5 44 91.67 20.9 44 91.67 20.9 46 95.83 23.86
S6 43 89.58 19.58 44 91.67 20.9 46 95.83 23.86
S7 42 87.5 18.34 36 75 12.13 43 89.58 19.58
S8 47 97.92 25.61 47 97.92 25.61 48 100 27.86
S9 34 70.83 10.41 35 72.92 11.25 38 79.17 14.01
S10* 45 93.75 22.32 46 95.83 23.86 46 95.83 23.86
Average 43.4 90.42 20.73 43.3 90.21 20.74 45.3 94.38 23.39
*Female Participants.

R. Singla et al. / J. Biomedical Science and Engineering 6 (2013) 1050-1055 1055
green, red and blue stimuli.
The future work may include the development of a
SSVEP based BCI application system that can provide
higher accuracy by using violet color as the stimuli color.
5. ACKNOWLEDGEMENTS
The author would like to thank the subjects who participated in the
EEG recording session.
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