Feature extraction of EEG signal using wavelet transform for autism classification

Abstract

Feature extraction is a process to extract information from the electroencephalogram (EEG) signal to represent the large dataset before performing classification. This paper is intended to study the use of discrete wavelet transform (DWT) in extracting feature from EEG signal obtained by sensory response from autism children. In this study, DWT is used to decompose a filtered EEG signal into its frequency components and the statistical feature of the DWT coefficient are computed in time domain. The features are used to train a multilayer perceptron (MLP) neural network to classify the signals into three classes of autism severity (mild, moderate and severe). The training results in classification accuracy achieved up to 92.3% with MSE of 0.0362. Testing on the trained neural network shows that all samples used for testing is being classified correctly.

Full text

VOL. 10, NO 19, OCTOBER, 2015 ISSN 1819-6608
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FEATURE EXTRACTION OF EEG SIGNAL USING WAVELET
TRANSFORM FOR AUTISM CLASSIFICATION
Lung Chuin Cheong, Rubita Sudirman and Siti Suraya Hussin
Faculty of Electrical Engineering, Universiti Teknologi Malaysia UTM Johor Bahru, Johor, Malaysia
E-Mail: rubita@fke.utm.my
ABSTRACT
Feature extraction is a process to extract information from the electroencephalogram (EEG) signal to represent the
large dataset before performing classification. This paper is intended to study the use of discrete wavelet transform (DWT)
in extracting feature from EEG signal obtained by sensory response from autism children. In this study, DWT is used to
decompose a filtered EEG signal into its frequency components and the statistical feature of the DWT coefficient are
computed in time domain. The features are used to train a multilayer perceptron (MLP) neural network to classify the
signals into three classes of autism severity (mild, moderate and severe). The training results in classification accuracy
achieved up to 92.3% with MSE of 0.0362. Testing on the trained neural network shows that all samples used for testing is
being classified correctly.
Keywords: discrete wavelet transforms (DWT), electroencephalogram (EEG), classification, feature extraction, sensory response.
INTRODUCTION
Electroencephalogram (EEG) is a non-evasive
technique used on the human skull to acquire electrical
impulse produced from neuron activation in the brain.
EEG electrodes are attached to the specific region of the
scalp according to the type of study to be conducted. EEG
is able to measure electrical signal from the human brain
in the range of 1 to 100 microvolt (µV) (Teplan, 2002).
There have been numerous studies on EEG classification,
looking for new possibilities in the field of Brain-
Computer Interface (BCI), neurobiological analysis and
automatic signal interpretation systems (Frédéric et al.,
2006).
EEG signal can be categorized to bands of
different frequency ranges. Delta wave lies below the
frequency of 4Hz. Theta lies in the range of 4Hz to 8Hz
while Alpha wave lies between 8Hz to 13Hz. The range of
Beta wave lies in 14Hz to 32Hz where beyond 32Hz lies
the Gamma wave. These frequency bands each
corresponds to different activities carried out by the
subject (Teplan, 2002).These different band of frequencies
each contains certain information of the brain activity.
However, the information hides within the EEG signal is
not directly analytical by the human eyes. However,
information on neural connectivity may be revealed with
the analysis of signal complexity on multiple scale. The
result of this analysis would be diagnostically useful
(Varela et al., 2001).
Analyzing EEG signals basically involves few
steps of signal processing; usually begin by data collection
which require the subject to perform certain task. In this
study, the selected channel of interest is first artefact-
removed and filtered with a band pass filter with a pass
band frequency of 0.4-60Hz to eliminate the power line
frequency, noise and extremely low frequency.
Given the fact that EEG signals are non-
stationary, time-varying computation is required to extract
the features from the signal in order to be classified
(Suleiman and Fatehi, 2007). Wavelet transform, being
one of the non-stationary time-scale analysis methods, is
used to decompose the signal for feature extraction. The
transient features of EEG signals are able to be accurately
captured (Jahankhani et al., 2006). The extracted features
are then used to train a neural network for classification
purpose. All the processes are performed and encoded in
MATLAB.
Figure-1. Processes involved in this study.
METHOD
Data acquisition and experimental setup
This study utilizes sensory data collected by
Sudirman and Hussin, (2014) from 30 autism children
Raw signal
Preprocessing and
Feature Extraction
DWT Decomposition
NN Training

VOL. 10, NO 19, OCTOBER, 2015 ISSN 1819-6608
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aged between 3 to 10 years old. Among these children, 5
of them have mild autism, 11 have moderate autism and
14 have severe autism. All of them performed tasks on
taste sensory, involving stimulation of three taste, which is
sweet, sour and salty. Stimulation of the three tastes is
done with sugar solution, vinegar solution and salt
solution. While the data is being read, the subjects’ eyes is
blindfolded except during visual task to prevent visual
artefact. In between different taste stimuli, the subjects are
given plain water to rinse away the residual taste stimuli.
The brain waves are recorded using Neurofax JE-921A
EEG machine together with an electrode cap following the
standard 10-20 international electrode placement system.
The data was sampled with an interval of 2ms and was
stored as ASCII files in the recording computer (Sudirman
and Hussin, 2014). Out of the 30 samples, 26 are used for
neural network training and 4 are reserved for testing (1
mild, 1 moderate and 2 severe) on the trained neural
network.
Signal preprocessing
From the collected multichannel signal, only the
parietal lobe channels, and  which is related to the
taste sensory is used for processing. The signal is first
epoched and the epoch with artefact and corrupted signal
are removed automatically using simple voltage threshold
method. The threshold is set to the standard deviation of
the whole signal of a particular channel. Flat lines are
removed using blocking and flat line function. Both are
performed using the source code of ERPLAB.
Then, the signal is filtered using a band pass filter
with pass band frequency of 0.4Hz to 60Hz and filter order
of 60 to remove the extremely low frequency components
such as those caused by movement and breathing (less
than 0.4Hz) (Suleiman and Fatehi, 2007), power line
frequency (60Hz) and noise (more than 60Hz).
Figure-2. Bandpass filter used to filter raw signal.
Feature extraction in time domain using DWT
Wavelet transform is a non-stationary time-scale
analysis method suitable to be used with EEG signals. It is
a useful tool to separate and sort non-stationary signal into
its various frequency elements in different time-scales
(Hazarika et al., 1997).
Quantitatively, discrete wavelet transform can be
applied to decompose a discrete time series, where
 is the discrete signal of  sampled at 500Hz in
this study, to its sub-bands of wavelet coefficients that
contains the feature (Hazarika et al., 1997). The wavelet
coefficients can be computed by dilation and translation of
the mother wavelet  as shown in (1), where
     and  is the wavelet space, while  and 
are the scaling factor and shifting factor respectively
(Murugappan et al., 2010).


 (1)
The decomposition is computed by filtering the
discrete signal  repeatedly up to a predetermined
level. The filter consist of a low pass filter to obtain the
approximation coefficient (CA) and high pass filter to
obtain the detailed coefficient (CD) (Murugappan et al.,
2010). After each level of filter, the signal is down-
sampled by half the sampling frequency in the previous
level    since the frequency element is reduced by
half.
Figure-3. Level 3 decomposing the signal f(n).
Daubechies 4 (db4) wavelet is used as the mother
wavelet in this study since that it is most suitable to
process biomedical signals. The input signal  has a
frequency band of 0-500Hz. With the interest area of 0-
60Hz for EEG signal, the signal should be decomposed up
to level 8 to be fully separated into the lowest frequency
delta band but since the relevant frequency band lies in the
alpha rhythm (8-16Hz), the filtered signal will be
decomposed only up to level 6 to obtain the alpha band in
CD6 as shown in Table-4. The detail coefficient of level 1,
2 and 3 is considered noise as their frequency did not lie
within the EEG frequency of 0-60Hz.

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Table-1. Wavelet coefficient and its signal information.
Wavelet
coefficient
Frequency (Hz)
Signal
information
D1
250 – 500
Noise
D2
125 – 250
Noise
D3
63 – 125
Noise
D4
32 - 63
Gamma
D5
16 - 32
Beta
D6
8 - 16
Alpha
D7
4 - 8
Theta
D8
0 - 4
Delta
Figure-4. Reconstructed CD6 coefficient containing
alpha band of the signal.
The wavelet coefficient of the decomposed signal
is still too large and not suitable to be directly used for
pattern recognition with neural network. Therefore, feature
extraction is done to reduce the signal to its representation
set of features vector by simplifying the description of a
large set of data (Nandish et al., 2012).
The feature can be extracted into time domain
feature and frequency domain feature. The most simple
and commonly used feature to represent the large set of
data is by statistical approach of the time domain feature.
Statistical feature such as mean, median, mode, standard
deviation, maximum and minimum can be used. In this
study, standard deviation of the wavelet coefficient
discrete-time series is computed using (2), where 
represents the discrete signal length while  represents the
signal level of the particular .

   


Other methods such as those in frequency domain
can also be used for feature extraction. For example,
previous study by Suleiman and Fatehi, (2007) uses STFT
and FFT to extract feature in the frequency domain. The 2
different methods yields different result of classification
accuracy (Suleiman and Fatehi, 2007).
Classification
Neural network are composed of interconnecting
artificial neurons, modelling in the way of how human
brain works. Various neural network architecture have
been developed over the years for different functions,
where one of the most popular architecture is the feed
forward network. Feed forward network is commonly
known for its ability to recognize pattern, predict and fit
nonlinear function (Nandish et al., 2012).
Figure-5. Feed forward neural network.
This work involve the use of multilayer
perceptron (MLP) feed forward neural network as the
signal classifier. It doesn’t require a large training set to
learn and hence reducing the operation overhead
(Jahankhani et al., 2006). Training the neural network
require two sets of data, which is the input data that
represents the information of the signal and the target data
that defines desired output of the neural network.
In this study, features of the discrete-time wavelet
coefficient CD6 is presented to the neural network for
training with scaled-conjugate backpropagation algorithm.
The accuracy of the neural network is measured by the
percentage of correct classification shown in (3).
   
    
The computation of the accuracy takes in account
of the true positive (TP), true negative (TN), false positive
(FP) and false negative (FN):
 TP = Number of correctly classified positive samples
 TN = Correctly classified negative samples while
 FP = Negative sample being classified as positive
 FP = Positive sample classified as negative.

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Neural network training parameters used in this
study is shown in Table-2. Training stops when any of the
parameter is fulfilled. Default data division setup (75%
training, 15% validation and 15% testing) and 10 hidden
layer is used to obtain the best cross entropy and percent
error in the neural network training GUI. A script is then
generated and performance is further improved by using
command line approach until a desirable accuracy and
MSE is obtained.
  


 
Table-2. Training parameters of the neural network.
Maximum number of epochs
1000
Minimum performance gradient
0.000001
Performance goal
0
Maximum validation failures
5
RESULTS AND DISCUSSIONS
Figure 6(a) shows one of the raw signal acquired.
The signal after artefact removal, rejection of corrupted
epochs and removal of flat line is shown in Figure-6(b)
while filtering gives a clean signal as in Figure-6(c).
(a)
(b)
(c)
Figure-6. (a) Raw EEG signal, (b) Removed artefact,
corrupted signal and flat line, (c) Clean signal after
filtering.
DWT decomposition is performed on and
 channel of the clean signal to obtain the alpha band
which contains information that reflects the sensory
responsiveness during a relaxed state. The level 6
decomposition yields 6 detailed coefficients containing
different band of frequencies as shown in Figure-7. Alpha
band signal as shown in Figure-4 lies in the detailed
coefficient at the 6th level decomposition (CD6).
Figure-7. Level 6 decomposition of the signal.

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Table-3. Features extracted from 26 subjects and their corresponding autism classes.
Expected
class
Salty,  (µV)
Sour,  (µV)
Sweet,  (µV)
C3
CZ
C4
C3
CZ
C4
C3
CZ
C4
Severe
117.14
119.44
113.38
130.90
189.93
142.32
134.51
140.91
144.04
Moderate
84.71
89.74
68.81
88.52
100.06
102.00
60.73
68.42
58.84
Moderate
98.54
146.20
131.41
46.69
33.24
32.57
93.21
100.74
118.98
Severe
179.89
178.90
214.50
198.89
196.99
195.94
122.93
137.09
130.12
Moderate
154.25
129.66
162.06
67.55
55.05
62.23
101.77
102.74
98.93
Moderate
149.32
126.15
108.94
47.18
47.80
51.07
47.03
55.41
50.17
Moderate
71.49
75.75
77.86
92.15
85.48
89.25
82.04
81.59
79.86
Severe
124.61
222.02
261.51
81.44
44.88
88.70
96.11
91.68
101.73
Mild
52.10
48.23
45.08
46.41
48.96
40.36
36.69
36.29
43.41
Severe
172.26
183.89
222.51
133.83
146.68
134.89
157.43
210.36
151.54
Moderate
100.96
98.86
130.14
78.63
80.87
82.06
93.58
78.41
90.30
Mild
80.59
58.48
64.34
67.34
76.55
93.04
75.12
72.15
74.34
Severe
314.29
296.12
337.38
165.43
153.63
147.12
82.04
81.59
79.86
Severe
244.48
305.36
209.36
272.81
370.34
283.65
247.92
283.59
254.78
Moderate
37.31
38.55
45.93
216.21
177.18
81.10
147.16
38.17
29.99
Moderate
146.46
136.62
138.21
71.86
82.07
79.64
59.90
52.18
46.55
Severe
96.97
106.01
109.01
163.21
149.82
157.79
98.48
97.13
103.48
Severe
122.88
130.59
125.47
103.28
107.98
99.93
124.46
148.08
143.33
Mild
88.40
77.24
52.96
37.02
52.20
47.46
115.17
42.12
153.41
Severe
57.84
51.92
60.38
166.46
181.28
191.80
99.05
102.99
116.65
Severe
210.84
183.59
202.89
72.74
72.81
70.84
52.60
52.11
49.91
Severe
166.66
176.92
162.85
94.77
98.34
97.65
116.03
120.38
151.40
Mild
52.31
51.65
61.11
49.03
45.71
57.76
42.30
47.65
54.93
Moderate
113.31
111.26
111.83
99.77
112.47
114.09
85.74
94.33
85.71
Moderate
55.22
68.88
55.58
59.98
67.53
66.51
114.88
103.73
101.37
Severe
202.06
200.13
258.68
192.18
179.16
199.35
233.97
222.31
202.41
Mean
126.73
131.24
135.85
109.40
113.73
108.04
104.65
102.39
104.46
SD
66.32
71.52
78.25
61.81
73.64
59.38
50.96
60.02
52.50
Feature extraction is performed in time domain
by computing the standard deviation of the discrete signal
level of the alpha band (D6) in microvolt (µV) using
equation (2) for all 3 taste sensory with 3 channels each.
The extracted features of the 3 taste sensory are shown in
Table-3 with mean and standard deviation of the features
in each channel.

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Figure-8. Mean and standard deviation of features across channels and taste.
From Figure-8, it was observed that the mean of
the 3 features of salty taste is slightly higher (126.73 µV,
131.24 µV, 135.85 µ V) compared to that of sweet taste
(104.65 µV, 102.39 µV, 104.46 µ V), which indicates that
the feature value acquired by salty taste is higher. This is
potentially due to the children being not comfortable with
the taste of salt (Sudirman and Hussin, 2014).
Generally, it can be seen that feature of subjects
with mild autism generally have a lower value, which also
has higher coherence across different type of taste sensory.
Subjects with severe autism has higher standard deviation,
where the coherence of the standard deviation across
different type of taste sensory is lower. Standard deviation
is the lowest at the C4 channel of subject 3 (32.57 μV)
with sour taste and highest in C4 channel of subject 19
(337.38 μV) with salty taste. Finding of the highest feature
value on salty taste is similar to the study by Sudirman and
Hussin, (2014), where the highest standard deviation
obtained is 336.83 μV from salty taste.
This dataset is used as an input data consisting of
26 samples with 9 elements and is fed into the neural
network for training. Trial and error is performed to obtain
the suitable data division ratio and number of hidden
neurons. The settings that gave the best performance in
cross entropy and percent error is shown in Table-4. The
neural network is designed to have 9 input neurons for the
9 features, 8 hidden neurons, and 3 output neurons for the
3 output classes, which is mild, moderate and severe
autism.
Table-4. Network setup that gives best performance.
Data division setup
Training percentage
65 %
Validation percentage
25 %
Testing percentage
10 %
Hidden layer setting
Hidden neurons
8
Figure-9. Architecture of the neural network.
Training of the neural network with settings
shown in Table-4 yields accuracy of 92.3%. Despite the
high accuracy, the mean squared error (MSE) is quite high
at 0.0362 with the cross entropy at 0.15822. This is
probably due to the large number of features and the
limited amount of samples for the neural network to
generalize the data.
The confusion matrix shown in Figure-10 shows
that only 1 sample from moderate autism and 1 from
severe autism is wrongly classified during training and
testing. The best performance is obtained after 18
iterations with the best validation performance obtained at
epoch 12 and gradient of 0.0729 as shown in the
performance plot in Figure-11. The constantly decreasing
cross-entropy indicates that the cross-entropy performance
is decreased as the training proceeds.
0
20
40
60
80
100
120
140
160
C3 Cz C4 C3 Cz C4 C3 Cz C4
Salty Sour Sweet
Mean Std

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Figure-10. Confusion matrix showing output of training.
Figure-11. Performance plot of the training.
The trained neural network is tested with the 4
samples reserved earlier. These samples perform the
similar preprocessing and feature extraction steps. Then,
they were classified with the trained neural network.
Classification shows that all 4 samples is correctly
classified as shown in Table-5.
Table-5. Output of classification testing.
Subject
number
Expected
severity
Classification
output (%)
Output
class
6
Mild
Mild
65.20
Mild
Moderate
34.76
Severe
0.04
10
Moderate
Mild
6.77
Moderate
Moderate
92.36
Severe
0.86
26
Severe
Mild
0.06
Severe
Moderate
6.27
Severe
93.66
36
Severe
Mild
0.30
Severe
Moderate
11.06
Severe
88.64
Previous study by Suleiman and Fatehi, (2007)
who performed feature extraction with STFT to perform
classification with MLP for BCI purpose achieve average
classification accuracy of 85.99% for all channels which is
slightly lower than by using DWT. While wavelet
transform is a time-scale analysis method, this simple
comparison of feature extraction with frequency analysis
might suggest that time domain features provides a
slightly clearer class boundary than frequency domain
features. However, the difference might also due to the
difference in training parameters being used during neural
network training and different linearity of dataset.
CONCLUSIONS
As EEG signal analysis is gaining popularity in
the field of neuroscience, brain-computer interface and
physiological evaluation, a robust method of feature
extraction must present to increase the reliability of the
method in providing a representation of the data.
DWT’s ability to decompose a signal down to its
frequency components shows that it is a simple and direct
method to analyze EEG signals in different frequency
band representing different activities in the brain. Results
shows that features extracted with DWT is able to display
various correlations between standard deviation of the
alpha band and the feature characteristics of different taste
sensory and also the severity of autism. This makes DWT
a suitable tool to analyze EEG signal of autism patients.
Training of the neural network with features extracted
with DWT shows that the network is able to achieve
classification accuracy at 92.3% despite having high MSE
of 0.0362. The trained network is able to classify all
testing data correctly.

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In future, researchers are suggested to find the
best combination of feature extraction method and
classifier that give the best accuracy and performance.
This can maximize the potential of using EEG
classification as a reliable method to diagnose autism.
ACKNOWLEDGEMENT
This work is supported by the Faculty of
Electrical Engineering, Universiti Teknologi Malaysia
with funding from the Ministry of Science, Technology
and Innovation of Malaysia (MOSTI) under Vot. 4S094.
The author would like to express gratitude to those who
provided support, guidance and technical knowledge
during the course of this work.
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