International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
ISSN 2319 - 4847
Special Issue for International Technological Conference-2014
Classification of EEG Signal for Imagined Left and
Right Hand Movement for Brain Computer
Interface Applications
Indu Dokare1, Naveeta Kant2
1
Department Of Electronics and Telecommunication
Engineering, V.E.S. Institute Of Technology, Mumbai, India
indu_dokare@yahoo.co.in
2
Department Of Electronics and Telecommunication
Engineering, V.E.S. Institute Of Technology, Mumbai, India
shashinaveeta@rediffmail.com
ABSTRACT
Brain Computer Interface (BCI) is a device which provides a non muscular communication channel between the brain and the
environment. It provides an alternate communication channel for people who suffer from severe motor disabilities. The
electroencephalogram (EEG) represents an efficient technique to measure and record brain electrical activity. The intent of the
user can be recorded using EEG and further it is translated into the device commands. This paper gives the results of experiment
performed using Discrete Wavelet Transform (DWT) for feature extraction of EEG signal for motor imagery. Further, these
features has been classified into left and right hand movement using Support Vector Machine (SVM) classifier.
Keywords: Brain Computer Interface (BCI), Electroencyphalography (EEG), Motor Imagery, Support Vector
Machine(SVM).
1. INTRODUCTION
A Brain Computer Interface (BCI) is a non muscular communication and/or control system that allows real-time
interaction between the human brain and external devices. A BCI translates brain signals of a BCI user proportional to
his/her intent into a desired output. The desired output could be used to control external devices like a speller, a
wheelchair, a robotic arm, a neural prostheses and so on. BCI systems represent a communication means for those people
who are unable to express their intents to the external world after a severe neuromuscular disorder caused by diseases
such as amyotrophic lateral sclerosis, spinal cord injury, brainstem strokes etc.[1],[3],[12],[13]. Electroencephalography
(EEG) is widely used method for measuring the brain activity in BCI other than Positron Emission Tomography (PET),
Magneto encephalography (MEG), Functional Magnetic Resonance Imaging (fMRI)[1],[13].
Brain activity in the cortex changes while moving a limb or even contracting a single muscle(for any physical activity).
Brain oscillations are typically categorized according to specific frequency bands (delta: 0.5- 4 Hz, theta: 4–8 Hz, alpha:
8–12 Hz, beta: 12–30 Hz, gamma: > 30 Hz) [7]. Sensorimotor rhythms comprises mu (8-12 Hz) and beta rhythms.
Sensorimotor rhythms (SMR) refers to oscillations in brain activity recorded from somatosensory and motor
areas[1],[13]. Mu and beta rhythms are associated with the cortical areas which are directly connected to the brain‟s
normal motor output channels. Movement or preparation for movement is typically accompanied by a decrease in mu and
beta rhythms, particularly contralateral to the movement[14]. This decrease has been labeled as event-related
desynchronization (ERD). Its opposite, rhythm increase, event-related synchronization (ERS) occurs after movement and
with relaxation. Furthermore, the most relevant for BCI use is that, ERD and ERS do not require actual movement, they
occur also with motor imagery (i.e. imagined movement)[1],[6].
The brain activity from somatosensory and motor areas are recorded using 10-20 electrode system of EEG
[7],[12],[13].The electrodes placed in this area are C3, Cz and C4. Recorded signals from these electrodes reflects the
motor activity of the person like hand movements, foot movements tongue movement etc.
The main issue involved in the motor imagery pattern recognition process is to successfully estimate, visualize and
represent the ERD/ERS phenomenon in a feature vector. Many feature extraction techniques have been used for feature
Organized By: Vivekanand Education Society's Institute Of Technology
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
ISSN 2319 - 4847
Special Issue for International Technological Conference-2014
extraction such as band power, power spectral density, Auto Regressive (AR), Adaptive Auto Regressive (AAR) models
etc. [5],[6]. Wavelet transform has emerged as one of the superior technique in analyzing non-stationary signals like
EEG.
In this experiment Discrete Wavelet Transform (DWT) has been used as a tool for feature extraction of EEG signal. The
extracted features has been classified in right hand and left hand movement using Support Vector Machine (SVM). The
dataset used in this experiment were obtained from BCI Competition II, dataset III provided by Department of Medical
Informatics, Institute for Biomedical Engineering, University of Technology Graz. The dataset used consists of C3,Cz
and C4 electrode signals for imagined right and left hand movements. The electrodes C3 and C4 are considered for
analysis eliminating the electrode Cz, since it does not provide relevant information on imagined left/right hand
movement [9]. Figure 1 shows the steps carried out for the analysis. The analysis was carried out using MATLAB tool.
EEG Signal
Electrode C3, Cz, C4 Signal
Pre-processing the Signal
Feature extraction using Discrete
Wavelet Transform
Feature Classification using SVM
Left Hand
Movement
Right Hand
Movement
Figure 1 Block diagram showing steps of analysis
2. METHODOLOGY
2.1 Wavelet transform
The wavelet transform is a tool which decomposes an input signal of interest into a set of elementary waveforms,
called "wavelets" and provides a way to analyze the signal by examining the coefficients (or weights) of these wavelets.
For analyzing the non-stationary signals like EEG, time–frequency methods such as wavelet transform is the most
suitable method to be used. The Discrete Wavelet Transform (DWT) analyzes the signal at different frequency bands
with different resolutions by decomposing the signal into a coarse approximation and detail information performing
multiresolution analysis. DWT employs two sets of functions, called scaling functions and wavelet functions, which are
associated with low pass and high-pass filters respectively.
Signal
Ca1
Ca2
Ca3
Ca4
Cd1
Cd2
Cd3
Cd4
Figure 2 Decomposition of the signal in four levels
Organized By: Vivekanand Education Society's Institute Of Technology
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
ISSN 2319 - 4847
Special Issue for International Technological Conference-2014
The decomposition of the signal into different frequency bands is simply obtained by successive high-pass and low-pass
filtering of the time domain signal[8]. These decomposed bands are called as sub bands. The output of the low-pass filter
gives the approximation coefficients, while the high pass filter gives the detail coefficients. As shown in the figure 2 Ca 1
is the approximation coefficient and Cd1 is the detail coefficient at first level of signal decomposition.
Selection of appropriate wavelet and the number of levels of decomposition is very important in analysis of signals using
DWT. The number of levels of decomposition is chosen based on the dominant frequency components of the signal. The
levels are chosen such that those parts of the signal that correlate well with the frequencies required for classification of
the signal are retained in the wavelet coefficients.
2.2 Support Vector Machine (SVM)
Support Vector Machines are finding many uses in pattern recognition and classification tasks. Support Vector Machine
(SVM) classifies data by finding the best hyperplane that separates all data points of one class from those of the
other class. The best hyperplane for an for an SVM means the one with the largest margin between the two classes.
Margin means the maximal width of the slab parallel to the hyperplane that has no interior data points. The data points
closest to the hyper plane are called the support vectors ; these points are on the boundary of the slab. A further important
concept in SVM is the transformation of data into a higher dimensional space for the construction of optimal separating
hyperplane. SVM perform this nonlinear mapping into a higher dimensional feature space by means of a kernel function
and then construct a linear optimal separating hyperplane between the two classes in the feature space. Some commonly
used kernels include Gaussian, Radial Basis Functions, and Polynomials.
3. EXPERIMENTAL RESULT AND DISCUSSIONS
3.1 Dataset
The data set used in this experiment has been obtained from BCI Competition II, dataset III provided by Department of
Medical Informatics, Institute for Biomedical Engineering, University of Technology Graz [9],[10],[11]. This dataset was
recorded from a normal subject (female, 25y). The subject sat in a relaxing chair with armrests. The task was to control a
feedback bar by means of imagined left or right hand movements. The order of left and right cues was random. The EEG
recording was made using a G.tec amplifier and a Ag/AgCl electrodes. Three bipolar EEG channels (anterior „+‟,
posterior „-„) were measured over C3, Cz and C4 as shown in figure 3.
1
2
3
5 cm
C3
1
Cz
2
C4
3
Figure 3 Electrode positions
Figure 4 Timing scheme
The experiment consists of 7 runs with 40 trials each. Each trial is of 9s length. All runs were conducted on the same day
with several minutes break in between. The first 2s was quite, at t=2s an acoustic stimulus indicates the beginning of the
trial and a cross “+” was displayed for 1s; then at t=3s, an arrow (left or right) was displayed as cue as shown in figure 4.
At the same time the subject was asked to move a bar into the direction of a the cue. The EEG was sampled with 128Hz,
it was filtered between 0.5 and 30Hz. The trials for training and testing were randomly selected. This experiment consists
of total 280 trials. Among this 140 trials were given as training data and remaining 140 trials as testing data. From the
Organized By: Vivekanand Education Society's Institute Of Technology
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
ISSN 2319 - 4847
Special Issue for International Technological Conference-2014
given dataset the electrodes C3 and C4 were considered for analysis. The electrode Cz was eliminated since it did not
provide relevant information on imagined left/right hand movement.
3.2 Feature Extraction
In this experiment the signal has been decomposed in 4 levels using discrete wavelet transform. The approximate
coefficient Ca4 and each level detail coefficients Cd4, Cd3, Cd2 and Cd1 were used as a features for classification. The
sampling frequency was 128Hz. The frequency ranges of each band is shown in table 1. The size of coefficients Ca 4, Cd4,
Cd3, Cd2 and Cd1 are 50, 50, 98, 194 and 386 respectively with Daubechies 2 (db2).
Table 1: Wavelet coefficients and frequency range after 4 level decomposition
Wavelet coefficients
Frequency Range (Hz)
Cd1
Cd2
Cd3
Cd4
Ca4
32-64
16-32
8-16
4-8
0-4
Figure 5 shows training signal for a trial from 3s to 9s length followed by the wavelet coefficients.
Figure 5 Training signal and its wavelet coefficients
3.3 Feature Classification
Support Vector Machine has been used as a classifier in this experiment. The kernel function used was Radial Basis
Function. The classifier was trained using training data and the parameters of SVM were set using 5 fold cross
validation. Then, the testing data was applied to the classifier and classification accuracy was determined as a
performance measure parameter of the classifier.
Organized By: Vivekanand Education Society's Institute Of Technology
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
ISSN 2319 - 4847
Special Issue for International Technological Conference-2014
The different
follows.
wavelet
coefficients were used as the features and the classification accuracy was determined as
Classification Accuracy
(%)
1) The wavelet coefficients at all decomposition level Ca4, Cd4, Cd3, Cd2 and Cd1 were used as a feature vector and were
classified into two classes left/right hand movement using Support Vector Machine classifier. Figure 6 shows the testing
classification accuracy using different wavelet families.
90
80
70
60
50
40
30
20
10
0
db2
db4
Sym2
Sym4
Coif2
coif4
All Wavelet Coefficients as a feature vector
Figure 6 Classification accuracy of all wavelet coefficients as a feature vector using different wavelet families
2) The feature vector consisting of all coefficients forms a large dimension vector. Hence, to reduce the feature vector
dimension detail coefficients Cd2 and Cd3 were only considered. The coefficient Cd2 and Cd3 has frequency range 16-32
Hz and 8-16 Hz respectively which comes in the range of mu band and beta band. The testing classification accuracy
using different wavelet families was determined and plotted as shown in Figure 7.
Classification Accuracy (%)
90
80
70
db2
60
db4
50
sym2
40
sym4
30
coif2
20
coif4
10
0
Wavelet coiefficients Cd2 and Cd3 as a feature vector
Figure 7 Classification accuracy of detail coefficients Cd2 and Cd3 as a feature vector using different wavelet families
Organized By: Vivekanand Education Society's Institute Of Technology
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
ISSN 2319 - 4847
Special Issue for International Technological Conference-2014
3) The feature vector dimension was also reduced by determining the statistical parameters of the all wavelet coefficients
like :
i. Maximum of wavelet coefficients of each sub band
ii. Minimum of wavelet coefficients of each sub band
iii. Mean of wavelet coefficients of each sub band and
iv. Standard deviation of wavelet coefficients of each sub band
Figure 8 shows the testing classification accuracy for this feature vector.
Classification Accuracy (%)
90
80
db2
70
db4
60
Sym2
50
40
Sym4
30
Coif2
20
coif4
10
0
Statistical parametesr of all coefficients as a feature vector
Figure 8 Classification accuracy of statistical parameters of all coefficients as a feature vector using different wavelet
families
4) Statistical parameters like maximum, minimum, mean and standard deviation of detail coefficients Cd2 and Cd3 were
determined and these features are fed to the classifier. Testing classification accuracy was estimated and plotted as
shown in figure 9.
Classification Accuracy (%)
90
80
70
db2
60
db4
50
sym2
40
30
sym4
20
coif2
10
coif4
0
Statistical parameters of wavelet coefficients Cd2 and Cd3 as a
feature vector
Figure 9. Classification accuracy of statistical parameters of detail coefficients Cd2 and Cd3 as a feature vector using
different wavelet families
Organized By: Vivekanand Education Society's Institute Of Technology
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
ISSN 2319 - 4847
Special Issue for International Technological Conference-2014
Table 2: Classification accuracy for different feature vectors
Feature Vector
4.
Wavelet Families
db2
db4
sym2
sym4
coif2
coif4
Ca4,Cd4, Cd3, Cd2 and Cd1
70
69.28
70
68.57
67.8
67.14
Cd2 and Cd3
68.57
70.7
68.57
69.29
68.57
69.29
Statistical parameter of all Coefficients
70.72
70
70.7
66
64.28
69.28
Statistical parameter of Cd2 and Cd3
71.42
68.57
71.4
70.7
68.7
69.28
CONCLUSION
In this paper results of experiment for BCI Motor Imagery using discrete wavelet transform as a feature extraction tool
and support vector machine as a feature classifier has been discussed. The aim of this experiment was to discriminate
between left hand and right hand imagined movement. EEG signals were decomposed upto level four using discrete
wavelet transform. For the analysis different wavelet families like Daubechies 2, Daubechies 4, Symlet 2, Symlet 4,
Coiflet 2 and Coiflet 4 were used. Different cases of using wavelet coefficients as features for classification were
evaluated and the results are summarised in table 2. In the first case, all wavelet coefficients Ca4, Cd4, Cd3, Cd2 and Cd1
were used as features, in the second case wavelet coefficients Cd2, Cd3 were considered and in the third and the fourth
cases maximum, minimum, mean and standard deviation of the first and the second cases coefficients were considered
respectively. The classification accuracy ranges from 66% to 71.42%. Maximum classification accuracy was estimated
when the statistical parameters of coefficients Cd2 and Cd3 were used as the features for classification. Further, the work
on determining the wavelet coefficients whose frequency correlates with the signal frequency of this motor imagery is
under progress. In addition the experiments for comparison purposes using different classification methods are currently
under progress.
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Organized By: Vivekanand Education Society's Institute Of Technology
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
ISSN 2319 - 4847
Special Issue for International Technological Conference-2014
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Organized By: Vivekanand Education Society's Institute Of Technology