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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5276_Библиотеки_им_академика_М_И_Перельмана
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functions cheby1, ellipord (generates filter minimum order) and ellip were used to compute
the filters coefficients and the function filtfilt was used for filtering. The data is already
filtered in the headset and the clearer band is from 0.16Hz to 43Hz, but the data was
transmitted to the PC as an unsigned integer and some offsets were added in the device for
each channel. So, by using extra high-pass filter with cut-off frequency of 0.16Hz we insure
the suppression of the DC component. For the first approach, the EEG raw data is low-pass
filtered with 30Hz cut-off frequency where this enhances the performance of ICA. In the
second approach the high pass filtering is carried out in the DWT sub-coding process.
ICA: The filtered EEG data is applied to the ICA algorithm to extract or separate
independent sources. The FastICA MATLAB open source package provided by the
Laboratory of Information and Computer Science in the Helsinki University of Technology is
used [77]. The FastICA algorithm is described in Chapter 3, Section 3.2.
Feature Extraction: Piccione et al., [28] used fuzzy control method to choose the best P300
like component of the ICA algorithm output to extract its features and apply them to the
classifier. Some other studies used the manual method for this process. Since we are using a
noisy commercial BCI device, we do not expect to have a component from the ICA stage that
has characteristics close to those of the P300 as by using a research electrodes cap as in
Piccione et al., [28]. Also, as we mentioned before, this method minimizes the dimensions of
the input vector of the classifier, but it may increase the processing time. In addition, Wang
and James et al., [71], showed that the ICA output components that are not like the P300
wave are similar in both target and non-target trials since they are components of the same
artifacts sources detected in similar conditions and eliminating these components does not
have a high contribution in the accuracy improvement. For the first approach, the resultant
components of the ICA stage are applied to a 3-level DWT algorithm where approximated
coefficients represent signal energy in the band of P300 wave including signal energy in the
delta, theta, and part of alpha bands as suggested by several studies [45]. In the second
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approach DWT is directly applied to the EEG data. For both approaches experiments,
Daubechies (Db4) wavelet was used as the mother wavelet since it is widely used in the
literature for similar purposes. The approximated coefficients were used as the features from
the DWT algorithm and were concatenated into feature vectors. The dimensionality of the
feature vectors was N
e
× N
f
, where N
e
denotes the number of components or electrodes and
N
f
denotes the number of features in one trial. 9 morphological features were used in addition
to the DWT. Due to these morphological features, the trial duration of 1500 ms, and the
downsampling in the 3-level DWT, N
f
always equaled to 18 + 9 =27. Depending on the
chosen electrodes, Ne equaled to ten or 8. MATLAB wavelet toolbox was used to perform
the DWT. wavedec function was used to perform multi-level DWT either on an electrode
signal or an ICA output component, the function appcoef was used to extract approximated
coefficients, and the function detcoef can be used to extract detailed coefficients.
Normalization: The extracted features were normalized to zero mean and unit variance. The
normalization was carried out as we mentioned in the previous experiment.
Machine Learning and Classification
After preprocessing the data and extracting distinct features, the classifier (SVM or NN) is trained
and validated as mentioned before using these features. For the neural network, a feedforward
artificial neural network with back-propagation was used. As we mentioned above, the algorithm
of the NN is implemented with MATLAB. The structure of the network consisted of input layer,
one hidden layer with different number of neurons, and one output layer with one neuron. The
output node returned a floating-point value used as the detection criterion as in the previous
experiment. For the support vector machine, the Libsvm open source was used. Kernel function
with second order polynomial was used and different values of hyperparameter C were tried as
mentioned in [6].
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System Real-Time Test
To start the online control process, start button need to be pressed. The process starts by initiating
the EEG raw data acquiring thread, then a stimulus is randomly flashed for 250 ms and the EEG
data collection continues 1 sec after stimulus intensification. The EEG raw data corresponds to
this time period is fed to the Matlab code where it is processed as in the training session and its
features are extracted and then these features are fed to the trained classifier to make the decision.
If the P300 wave was detected, i.e. classifier output was 1, the blue cubic will be moved one step
in the direction corresponds to the flashed stimulus. The user should concentrate on the stimulus
corresponds to the required direction or application and ignore all of other stimuli. On the other
hand if the P300 wave was not detected, i.e. classifier output was -1, no action will be performed
and the next random stimulus was flashed. This process continues until the subject successfully
reaches the goal point, then the process automatically stops. Also, at any time the user can
interrupt the process and stop it by clicking the stop button.
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CHAPTER 5
RESULTS
5.1 Introduction
The results of the experiments that were described in the previous chapter are presented and
discussed in this chapter. While the observations of the performance trends are noted with the
results, a more in-depth discussion is presented in the next chapter. First, the results of the
experiments that use BCI competition III, 2004 data sets to test and compare different P300 wave
detection algorithms are presented. Then, the last Section describes the results that explore the
reliability and performance of a real-time P300-based BCI system using Emotiv Epoc headset for
acquiring EEG raw signals.
5.2 Results with BCI Competition Data Sets
In this section the results from the experiments that ran on four different set of electrode
configurations are presented for both subjects, A and B. For all the figures below, the accuracies
of each approach using one SVM classifier are shown with a solid black line, using ensemble
SVM classifiers are shown with dash-dash red line, and using one NN classifier are shown with
dash-dot blue line.
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5.2.1 Full Set of Electrodes (64 electrodes)
(a) Subject A (b) Subject B
Figure 5.1: Comparison of the three different classifiers for the first approach as applied to the full set of
electrodes data of both subjects.
(a) Subject A (b) Subject B
Figure 5.2: Comparison of the three different classifiers for the second approach as applied to the full set
of electrodes data of both subjects.
0 5 10 15
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
ICA + DWT + SVM
ICA + DWT + SVMs
ICA + DWT + NN
0 5 10 15
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
ICA + DWT + SVM
ICA + DWT + SVMs
ICA + DWT + NN
0 5 10 15
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
DWT + SVM
DWT + SVMs
DWT + NN
0 5 10 15
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
DWT + SVM
DWT + SVMs
DWT + NN
88
5.2.2 Subsets of Electrodes
Predefined 8 electrodes set
(a) Subject A (b) Subject B
Figure 5.3: Comparison of the three different classifiers for the first approach as applied to the predefined
8 electrodes data set for both subjects.
(a) Subject A (b) Subject B
Figure 5.4: Comparison of the three different classifiers for the second approach as applied to the
predefined 8 electrodes data set for both subjects.
0 5 10 15
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
ICA + DWT + SVM
ICA + DWT + SVMs
ICA + DWT + NN
0 5 10 15
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
ICA + DWT + SVM
ICA + DWT + SVMs
ICA + DWT + NN
0 5 10 15
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
DWT + SVM
DWT + SVMs
DWT + NN
0 5 10 15
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
DWT + SVM
DWT + SVMs
DWT + NN
89
8 electrodes set similar to Emotive default position
(a) Subject A (b) Subject B
Figure 5.5: Comparison of the three different classifiers for the first approach as applied to the 8 electrodes
data set similar to Emotiv default position for both subjects.
(a) Subject A (b) Subject B
Figure 5.6: Comparison of the three different classifiers for the second approach as applied to the 8
electrodes data set similar to Emotiv default position for both subjects.
0 5 10 15
10
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
ICA + DWT + SVM
ICA + DWT + SVMs
ICA + DWT + NN
0 5 10 15
10
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
ICA + DWT + SVM
ICA + DWT + SVMs
ICA + DWT + NN
0 5 10 15
10
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
DWT + SVM
DWT + SVMs
DWT + NN
0 5 10 15
10
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
DWT + SVM
DWT + SVMs
DWT + NN
90
8 electrodes set approximately similar to Emotive reversed position
(a) Subject A (b) Subject B
Figure 5.7: Comparison of the three different classifiers for the first approach as applied to the 8 electrodes
data set similar to Emotiv reversed position for both subjects.
(a) Subject A (b) Subject B
Figure 5.8: Comparison of the three different classifiers for the second approach as applied to the 8
electrodes data set similar to Emotiv reversed position for both subjects.
The accuracies of all experiments after 5 and 15 trials as required by the competition are listed in
table 5.1 below.
0 5 10 15
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
ICA + DWT + SVM
ICA + DWT + SVMs
ICA + DWT + NN
0 5 10 15
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
ICA + DWT + SVM
ICA + DWT + SVMs
ICA + DWT + NN
0 5 10 15
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
DWT + SVM
DWT + SVMs
DWT + NN
0 5 10 15
20
30
40
50
60
70
80
90
100
Averaged trials
Accuracy %
DWT + SVM
DWT + SVMs
DWT + NN
91
Subject
Approach
Classifier
Electrodes set
Full set
8 Pre. Def
8 Em-1 set
8 Em-2 set
Sequences
Sequences
Sequences
Sequences
5
15
5
15
5
15
5
15
Subject A
ICA + DWT
SVM
66
84
38
74
29
47
34
70
SVMs
63
86
39
75
28
48
35
73
NN
64
85
39
75
30
48
38
71
DWT
SVM
60
83
36
72
26
45
35
71
SVMs
60
84
38
73
27
46
38
69
NN
59
83
40
73
24
44
33
67
Subject B
ICA + DWT
SVM
52
83
38
73
29
46
34
69
SVMs
53
87
39
75
31
48
34
72
NN
54
86
41
74
31
47
35
73
DWT
SVM
51
81
37
71
30
44
34
66
SVMs
52
82
39
73
31
46
36
68
NN
52
84
36
73
31
45
32
70
Table 5.1: Classification accuracy out of 100 for each subject using the two approaches with different
electrodes configurations after 5 and 15 trials.
5.2.3 Discussions
The first observation is that both methods showed good results depending on the number and the
location of electrodes of the subset. When using the 64 electrodes, approach-1 (ICA +DWT +
classifier) reached an accuracy of 87%, while approach-2 (DWT + classifier) led to an accuracy
of 84%. Both results are comparable to the results achieved by Rakotomamonjy et al., [6], the
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winner of the BCI competition III. For the predefined electrodes subset, the accuracy decreased as
in all related studies in the literature due to the less number of electrodes, but both approaches led
to close results.
The accuracy when using electrodes similar in location to the ones of the Emotiv headset
with the default position was low since only few electrodes positions were within some of the
P300 sources head regions. However, for the electrodes similar to the reversed position the
accuracy reached 73% where more electrodes cover the visual cortex. The results from the two
methods were comparable to each other. As shown in figure 4.6 in Chapter 4, the difference
between the target and non-target signals in the data collected from some electrodes that were
covering the visual cortex such as Cz was clear and that led to the ability of using only DWT for
feature extraction and avoid the time delay caused by using ICA algorithm.
Consequently, using only DWT takes less time in the real-time P300 detection process
and that speeds up the process which is a demand in most of the real-time control applications.
However, in the noisy commercial EEG sensors such as Emotiv headset the signal to noise ratio
is always expected to be low and clear discrimination between brain responses is not achievable.
As a result, the question here is whether the use of the DWT alone is enough to draw distinct
features that let the classifier reach reasonable decisions. Therefore, both approaches will be
applied in the next Emotiv experiments.
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