- Email: [email protected]
EEG electrode selection method based on BPSO with channel impact factor for acquisition of significant brain signal
Accepted Manuscript Title: EEG Electrode Selection Method based on BPSO with Channel Impact Factor for Acquisition of Significant Brain Signal Author: Seung-Min Park Jun-Yeup Kim Kwee-Bo Sim PII: DOI: Reference:
S0030-4026(17)31298-6 https://doi.org/doi:10.1016/j.ijleo.2017.10.085 IJLEO 59821
To appear in: Received date: Accepted date:
21-7-2017 18-10-2017
Please cite this article as: Seung-Min Park, Jun-Yeup Kim, Kwee-Bo Sim, EEG Electrode Selection Method based on BPSO with Channel Impact Factor for Acquisition of Significant Brain Signal, (2017), https://doi.org/10.1016/j.ijleo.2017.10.085 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Seung-Min Park, Jun-Yeup Kim, and Kwee-Bo Sim∗
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EEG Electrode Selection Method based on BPSO with Channel Impact Factor for Acquisition of Significant Brain Signal
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School of Electrical and Electronics Engineering, Seoul, Republic of Korea
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Chung-Ang University1,∗
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Abstract
A brain-computer interface (BCI) based on motor imagery is a system that
M
transforms a subject’s intentions into control signals by classifying electroencephalograph (EEG) signals obtained from imagining the movement of a subjects limbs. On imagining a limbs movement, the primary motor cortex area is
d
prominently activated. For our new paradigm, however, we do not know which positions are activated or not. In that case, a simple approach is to use as
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many electrodes as possible. The problem is that using many electrodes also causes other problems. When applying a common spatial pattern (CSP), which
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is an EEG extraction method, using many electrodes causes an overfitting problem. In addition, there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal electrode selection method using binary particle swarm optimization (BPSO) with a channel impact factor. We examined optimal selected electrodes among all electrodes using four optimization methods and compared the classification accuracy and the number of selected electrodes between BPSO, BPSO with a channel impact factor, a genetic algorithm (GA), and a harmony search (HS) using a support vector machine (SVM). The results showed that BPSO with a channel impact factor I Fully
documented templates are available in the elsarticle package on CTAN. author Email address: [email protected] (Chung-Ang University) URL: alife.cau.ac.kr (Chung-Ang University)
∗ Corresponding
Preprint submitted to Journal of LATEX Templates
July 21, 2017
Page 1 of 17
selected five fewer electrodes and even improved accuracy by 20.4% compared
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with HS, GA, and BPSO. Keywords: brain-computer interface (BCI), electroencephalography (EEG),
binary particle swarm optimization (BPSO), harmony search (HS), optimized
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EEG-electrode selection, channel impact factor
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1. Introduction
Patients suffering from amyotrophic lateral sclerosis (ALS) or locked-in syn-
an
drome are able to see and listen to the environment just like normal people do. The difference between these patients and normal people are that they are 5
conscious but are not able to move their limbs as they want. They have dif-
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ficulty communicating with the world. A brain-computer interface (BCI) is a system that transforms brain signals primarily related to a subjects intent into control signals so that machines such as robot arms or a wheelchair can be con-
10
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trolled by signal processing, pattern recognition, and other processes. A BCI can help patients with ALS or locked- in syndrome communicate with the world.
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Electroencephalography (EEG) is a method that records brain activity; in this method, electrodes are installed on the skin to measure the activity between
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the brain and those electrodes. This is a non-invasive method. The measured signals have weak amplitudes, many artifacts, and significant noise compared
15
with invasive methods, in which electrodes are implanted on the surface of the cortex surgically. One of the major challenges in for a BCI is that it is a personal dependent system. That is, different brain regions are activated for each person even though the same paradigm and environment are applied. The solution for this challenge is that we use as many electrodes (equal to electrodes)
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as possible. However, the use of many electrodes causes other problems, such as computational complexity and noise that originates from electrodes that are not related to the experiment. In [1], researchers investigated electrode selection by recursive feature elimination and zero-norm optimization. In [2], researchers proposed channel selection with a genetic neural mathematic method. In [3],
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researchers combined a common spatial pattern (CSP) with particle swarm optimization (PSO) for channel selection. As for a motor imagery experiment of
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left or right hand movement imagery experiment of left or right hand movement,
channels C3 or C4 which are located at the center of motor imagery cortex and
30
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depicted Fig. are the most important. We assume that only using the elec-
trodes that are located near C3 and C4 can improve accuracy. We set a channel
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impact factor and modified the position update equation in BPSO. We suggest an optimization- based electrode selection method with high accuracy and a small number of electrodes. We computed accuracy and compared electrode
35
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selection methods using binary PSO (BPSO), BPSO with a channel impact factor, a genetic algorithm (GA) [4], and harmony search (HS) [5]. CSP [6] was applied as an extraction method, and SVM [7] was applied for classification and
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to determine one of the parameters of a fitness function. We applied principal component analysis (PCA) [8] to reduce the dimensionality when it was too large. This paper is structured as follows: section 2 describes related works such as preprocessing, feature selection, and feature extraction. The proposed
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method is described in section 3. Experimental data and results are provided
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in sections 4 and 5, respectively.
2. Related Works
2.1. Linear Filtering
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An EEG contains many artifacts, which can originate from eye blinking
and unintentional movements. Those artifacts need to be removed for better classification performance. Linear filtering can be used to diminish artifacts that are located at a specific frequency. Electromyography (EMG) artifacts can be removed by a low-pass filter, and Electrooculography (EOG) artifacts can
50
be removed by a high-pass filter [9]. Linear filtering was commonly used in early studies to remove artifacts in EEG signals [10]; however, this algorithm is unsuitable for use when the neurological phenomenon of interest and the EOG or EMG overlap in the same frequency band.
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2.2. Particle Swarm Optimization PSO was developed by Kennedy and Eberhart in 1995 [11]. It is a meta-
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heuristic optimization technique based on the simulation of social behavior of flocks. Even though this algorithm has fewer variables to set and is considerably
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simpler than GA, it performs as well as a GA. This algorithm has received
attention throughout the world and has been applied to many research fields [12]. Particles that are equivalent to individuals in a GA are randomly distributed in
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a hyper-dimensional search space and fly through the search space at a certain velocity. The swarm consists of particles that have the ability to memorize
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previous experiences, which makes it possible to consider the present optimal solution as well as the previous optimal solution when each particle is searching 65
for the most optimal solution in the search space. Thus, the velocity of each
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particle is influenced by its own best position found so far and the best solution that was found so far by its neighbors. The best solution of the swarm is called gbest, and the best solution of each particle is called pbest. Each particle
position and velocity updates. The position and velocity of the particles are
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compares its fitness value and continues to search for an optimal solution with
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updated as follows:
i vk+1 = wvki + c1 r1 (pik − xik ) + c2 r2 (gbest − xik )
(1)
xi (t + 1) = xi (t) + vi (t + 1)
(2)
where c1 and c2 are real numbers, and r1 and r2 are random values between 0
and 1. Eventually, the swarm will converge to an optimal position. 2.3. Binary Particle Swarm Optimization
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BPSO is a discrete version of PSO [13]. The way to update a particle’s velocity in BPSO is the same as PSO. The difference between PSO and BPSO is that the particles in BPSO are comprised of either a 0 or 1 unlike PSO. This
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differentiates position update equation in BPSO from that in PSO. The update
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equations in BPSO are as follows S(v) = (1 + e−v )−1
xik+1 = 0
i if τ > S(vk+1 )
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i if τ < S(vk+1 )
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xik+1 = 1
(4)
(5)
where τ is a random value between 0 and 1. After evaluating velocity, we apply
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(3)
this value to a sigmoid function and then compare the output of sigmoid function and random value τ . If the output of the sigmoid function is larger then τ , the
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particle’s position is updated to 1, and vice versa.
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3. Proposed Methods
3.1. BPSO for Electrode Selection
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We applied a BPSO algorithm for electrode selection that performs feature selection in a BCI system. The flow chart of BPSO applied in BCI is depicted
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in Fig. 1. In order to reduce overfitting in feature extraction, features that could be artifacts and include noise need to be removed. The BPSO method
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for electrode selection selects important features among all features for better feature extraction.
We keep doing feature selection until we select optimal features. We regard
the electrode space as the solution space, and each particle’s position is assigned to a value of either 0 or 1, where 0 means that we do not use the electrode, and
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1 means that we do use the electrode. The first task is to initialize particle position, velocity, c1 , and c2 . After initialization, we choose particles whose position value is 1. Before feature extraction performed by CSP, the inputs of feature extraction consist of features whose particle position value is equal to 1. An SVM is applied for classification after feature extraction, and accuracy
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is calculated. When the dimensionality was too large to calculate, we applied
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Figure 1: Flow chart of binary particle swarm optimization applied in BCI system.
PCA in order to reduce the number of dimensions. We set the fitness function according to three factors, which are accuracy, the number of electrodes used for feature extraction, and the slack variables measured in the SVM. Of the three factors, we assign the strongest impact to accuracy and smallest impact
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to slack variables. The fitness function is as follows:
F itness = 0.6 ×
accuracy 100
accuracy =
+ 0.2 ×
NT + 1 − NS NT
# of correctly classified data # of test data
+ 0.2ξ
(6)
(7)
where parameter ξ, NT , NS represents the slack variables, total number of electrodes, and the number of selected electrodes, respectively. The particle
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position, velocity, pbest, and gbest are then updated during iterations, unless the number of iterations exceeds the maximum set value. The particle position and velocity are updated by equations (1), (3), (4) and (5). When the number of
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iterations exceeds the maximum value, we stop the electrode selection method
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and consider gbest to be the optimal electrode combination. The parameters are set as follows: the number of particles is set to 50, the maximum number of
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iterations was set to 500, c1 and c2 were set to 2, and r1 , r2 , and τ were set to random values between 0 and 1. The n-th inertial weight is as follows : n h
(8)
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w(n) = wmax − (wmax − wmin )
where wmax is set to 0.9 ; wmin is set to 0.6; and h represents the maximum
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number of iterations.
3.2. BPSO with Channel Impact Factor for Electrode Selection In order to apply BPSO with a channel impact factor in a BCI system, we also set the electrode space to be the solution space, and each particles
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position can have a value of either 0 or 1, where 0 means that we do not use the electrode, and 1 means that we do use the electrode. The position value of
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the particle is determined by equations (4) and (5), and τ is the criteria used to determine the particle’s position in those equations, that is , the smaller τ is,
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the higher the probability is that the position will have a value of 1, and vice versa. When performing motor imagery experiments, we know that left hand movement imagery is mostly related to electrode C4 ; on the other hand, right hand movement is mostly affected by electrode C3 . We assume that it could have higher accuracy with electrode combinations that consist of electrodes C3
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and C4 , as well as electrodes located near C3 and C4 . In order to confirm this
assumption, we divide five distance sections based on C3 and C4 . The closer the electrode is to C3 or C4 , the lower value we use for δ, which is the impact factor for the electrode. The farthest distance from C3 or C4 is 0.9, so we divide five distance groups into 0.18, 0.36, 0.54, 0.72 and 0.9. We set δ as 0.7, 0.8, 0.9,
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1 and 1.1, respectively, according to the group. We set δ as 0.6 for electrode C3
i if θ < S(vk+1 )
xik+1 = 0
if
i θ > S(vk+1 )
(9)
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xik+1 = 1
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or C4 . We modified equations (4) and (5) as follows:
(10)
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where θ is δ×τ . The remaining process is the same as that described in section A. We also use the same fitness function described in equation (6), and parameters
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are set the same as in BPSO for electrode selection. 3.3. Genetic Algorithm for Electrode Selection
We applied a GA algorithm for electrode selection in a BCI system. The
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process of GA for electrode selection is the same as the process of BPSO. In a GA algorithm, we also use the same fitness function described in equation (6). The parameter setting is carried out as follows. We used 50 individuals and set the maximum number of iterations to 500. We set the crossover probability to
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0.8 and the mutation probability to 0.011. 3.4. Harmony Search Algorithm for Electrode Selection
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The HS algorithm was applied for electrode selection in the same way as
BPSO and GA were, as described above. The process of HS is the same as the
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process of BPSO and GA. In the HS algorithm, we set the aesthetic standard to that of equation (6), which is used as a fitness function in BPSO and GA. We used 50 harmonic memories and 59 kinds of instruments and set the maximum practice to 500.
4. Experimental Data and Results
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4.1. Experimental Data and Environment In order to obtain the acquisition data of brain signal, we needed shield room to acquire the EEG data. It was built with sound insulation and absorption materials, shielding materials to keep and obtain the pure EEG signal 8
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Figure 2: Shield Room for Brain-Computer Interface in URIS Lab., Chung-Ang
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University, KOREA
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in URIS lab., Chung-Ang University, KOREA, 2011. In this paper, we used compumedics neuroscan synamps2 (64-ch EEG acquisition system), and curry7
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as software. Before experimenting on our paradigms, we used BCI competition IV datasets, general data, about motor imagery for brain-computer interface system. Its dataset 1 is used in this study [14]. Several healthy subjects took part in the experiment, and the 59 electrodes. For each subject, two classes
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of motor imagery were selected from the three classes (left / right hand, and foot). Motor imagery was performed without feedback throughout the whole session. As a visual cue, arrows pointing left, right, and down were displayed on the screen. While the cue was on the screen, the subject was instructed to perform the cued motor imagery task. These periods were interleaved with 2sec
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of a blank screen and 2sec with a fixed cross shown in the center of the screen. The fixed cross was superimposed on the cues, i.e., it was shown for 6sec. These data sets are provided with complete marker information.
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4.2. Experimental Results
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With BPSO, BPSO with a channel impact factor, GA and the HS algorithm, we executed the experiment 20 times for each subject. Among the seven subjects who imagined left or right hand or foot movements, we selected three
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subjects (subjects b, d, and e) who were instructed to imagine either left or right hand movements Table 1. illustrates the comparison of the number of selected
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electrodes and accuracy between BPSO, BPSO with a channel impact factor, GA and HS. CH stands for electrode.
The results of the selected electrode number and accuracy are an average
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over 20 repetitions of the experiment. As we can see, the selected electrodes are different for each subject even though we used the same optimization algorithm. In the case of subject d, on the one hand, BPSO with a channel impact factor had the highest classification accuracy with the lowest number of
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selected electrodes. HS, on the other hand, had the lowest classification accuracy with a large number of electrodes. HS used five more electrodes and had
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20.4% lower accuracy than BPSO with a channel impact factor. In the cases
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of subject b and subject e, BPSO with a channel impact factor also had the highest classification accuracy. From Table 1, we can confirm the assumption
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that we can have higher accuracy with electrode combinations that consist of electrodes C3 and C4 as well as electrodes located near C3 and C4 . Even though we applied the same optimal algorithm to the same subject, we found different
Table 1: Comparison of the number of selected electrodes and accuracy between GA, HS, BPSO and BPSO with channel impact factor.
Subject b
Subject d
Subject e
# of CH
ACC
# of CH
ACC
# of CH
ACC
GA
29
60.55
28.65
63.95
29
65.55
HS
30
58.55
29
60.55
31
64.55
BPSO
29
75.55
29
75.55
29
75.55
BPSO with CIF
23
78.55
24
80.95
23
77.55
10
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Figure 3: (a) Histogram of selected electrodes for subject d by BPSO with channel
impact factor. (b) Selected electrodes for subject d that were selected more than 10 times. (c) Bounding box for the actually selected electrode. (top view) (d) Bounding box for the actually selected electrode. (rear view)
numbers of selected electrodes. In order to overcome this problem, we executed
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as many experiments as we could and averaged the results. As we executed the experiment 20 times, we estimated how many times each electrode was selected. The maximum frequency was set to 20, and we chose the electrodes that were selected more than or equal to 10 times. Fig. 4(a) illustrates the frequency of the selected electrodes for subject d by BPSO, and the inside of the red square
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Figure 4: Comparison between accuracy of BPSO with selected electrodes for each
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trial and accuracy with selected electrodes that were selected more than 10 times.
box has electrodes that were selected more than or equal to 10 times, and the
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corresponding electrodes are depicted Fig. 4(b). The number of electrodes that were selected more than 10 times is 25. Before using electrodes that were selected more than 10 times, we need to
confirm whether or not better accuracy can be attained with electrodes other
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than those selected using proposed methods such as BPSO and BPSO with a channel impact factor during one experiment. Fig. 4. depicts accuracies with five kinds of selected electrodes by BPSO
with a channel impact factor and accuracy with selected channels depicted in Fig. 3 (b). As we can see from Fig. 4, using the channels that were selected more
210
than 10 times can improve accuracy 6% more than using electrodes selected by one experiment of BPSO with a channel impact factor. Fig. 5(a) illustrates the frequency of the selected electrodes for subject d
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Figure 5: (a) Histogram of selected electrodes for subject d by BPSO with channel
impact factor. (b) Selected electrodes for subject d that were selected more than 10 times. (c) Bounding box for the actually selected electrode. (top view) (d) Bounding box for the actually selected electrode. (rear view)
by HS. electrodes inside the red box were selected more than or equal to 10 times, and the corresponding electrodes are depicted Fig. 5(b). The number of
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electrodes that were selected more than 10 times is 30. Fig. 6. depicts accuracies with five kinds of selected electrodes by HS with a channel impact factor and accuracy with the selected electrodes depicted in Fig. 6(b). As we can see from Fig. 6, using the electrodes that were selected more
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Figure 6: Comparison between accuracy of HS with selected electrodes for each
d
trial and accuracy with selected electrodes that were selected more than 10 times.
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than 10 times can improve accuracy 6% more than using cannels selected by BPSO with a channel impact factor. From Fig.4. and 6 we can confirm that the
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selected electrodes that were selected by BPSO with a channel impact factor and were selected more than 10 times can be used in the next experimental session instead of using all electrodes and that these selected electrodes can improve accuracy well.
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5. Conclusion
This paper proposed the use of an optimal electrode instead of using all
electrodes for BCIs. For this purpose, optimal electrode selection methods employing BPSO, BPSO with a channel impact factor, GA, and HS are proposed. By applying these four methods, we selected few electrodes with high accuracy.
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BPSO with a channel impact factor outperformed BPSO, GA, and HS in that it achieved the highest accuracy with the smallest channel number. HS performed
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the worst among four optimization algorithms. We also selected channels that were selected at least 10 times in 20 experiments and found out that these
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channels have better accuracy than the selected channels that were determined
from one trial of the proposed method. In the future, we will adjust the fitness
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function in order to obtain the same optimal channel combination every time the calculation is performed. We can also customize EEG cap with optimized
imagery and steady state visual evoked potentials.
Acknowledgments
an
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electrodes. The EEG-electrode cap can be used to suit your needs such as motor
This research was supported by the National Research Foundation of KO-
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REA[NRF] grant funded by the KOREA government [MEST] [2012-0008726]
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[12] M. R. AlRashidi, M. E. El-Hawary, A survey of particle swarm optimization applications in electric power systems, IEEE Transactions on Evolutionary Computation 13 (4) (2009) 913–918. doi:10.1109/TEVC.2006.880326. [13] J. Kennedy, R. C. Eberhart, A discrete binary version of the particle swarm algorithm, ICSMC-97 5 (1997) 4104–4108 vol.5.
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S0030-4026(17)31298-6 https://doi.org/doi:10.1016/j.ijleo.2017.10.085 IJLEO 59821
To appear in: Received date: Accepted date:
21-7-2017 18-10-2017
Please cite this article as: Seung-Min Park, Jun-Yeup Kim, Kwee-Bo Sim, EEG Electrode Selection Method based on BPSO with Channel Impact Factor for Acquisition of Significant Brain Signal, (2017), https://doi.org/10.1016/j.ijleo.2017.10.085 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Seung-Min Park, Jun-Yeup Kim, and Kwee-Bo Sim∗
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EEG Electrode Selection Method based on BPSO with Channel Impact Factor for Acquisition of Significant Brain Signal
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School of Electrical and Electronics Engineering, Seoul, Republic of Korea
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Chung-Ang University1,∗
an
Abstract
A brain-computer interface (BCI) based on motor imagery is a system that
M
transforms a subject’s intentions into control signals by classifying electroencephalograph (EEG) signals obtained from imagining the movement of a subjects limbs. On imagining a limbs movement, the primary motor cortex area is
d
prominently activated. For our new paradigm, however, we do not know which positions are activated or not. In that case, a simple approach is to use as
te
many electrodes as possible. The problem is that using many electrodes also causes other problems. When applying a common spatial pattern (CSP), which
Ac ce p
is an EEG extraction method, using many electrodes causes an overfitting problem. In addition, there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal electrode selection method using binary particle swarm optimization (BPSO) with a channel impact factor. We examined optimal selected electrodes among all electrodes using four optimization methods and compared the classification accuracy and the number of selected electrodes between BPSO, BPSO with a channel impact factor, a genetic algorithm (GA), and a harmony search (HS) using a support vector machine (SVM). The results showed that BPSO with a channel impact factor I Fully
documented templates are available in the elsarticle package on CTAN. author Email address: [email protected] (Chung-Ang University) URL: alife.cau.ac.kr (Chung-Ang University)
∗ Corresponding
Preprint submitted to Journal of LATEX Templates
July 21, 2017
Page 1 of 17
selected five fewer electrodes and even improved accuracy by 20.4% compared
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with HS, GA, and BPSO. Keywords: brain-computer interface (BCI), electroencephalography (EEG),
binary particle swarm optimization (BPSO), harmony search (HS), optimized
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EEG-electrode selection, channel impact factor
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1. Introduction
Patients suffering from amyotrophic lateral sclerosis (ALS) or locked-in syn-
an
drome are able to see and listen to the environment just like normal people do. The difference between these patients and normal people are that they are 5
conscious but are not able to move their limbs as they want. They have dif-
M
ficulty communicating with the world. A brain-computer interface (BCI) is a system that transforms brain signals primarily related to a subjects intent into control signals so that machines such as robot arms or a wheelchair can be con-
10
d
trolled by signal processing, pattern recognition, and other processes. A BCI can help patients with ALS or locked- in syndrome communicate with the world.
te
Electroencephalography (EEG) is a method that records brain activity; in this method, electrodes are installed on the skin to measure the activity between
Ac ce p
the brain and those electrodes. This is a non-invasive method. The measured signals have weak amplitudes, many artifacts, and significant noise compared
15
with invasive methods, in which electrodes are implanted on the surface of the cortex surgically. One of the major challenges in for a BCI is that it is a personal dependent system. That is, different brain regions are activated for each person even though the same paradigm and environment are applied. The solution for this challenge is that we use as many electrodes (equal to electrodes)
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as possible. However, the use of many electrodes causes other problems, such as computational complexity and noise that originates from electrodes that are not related to the experiment. In [1], researchers investigated electrode selection by recursive feature elimination and zero-norm optimization. In [2], researchers proposed channel selection with a genetic neural mathematic method. In [3],
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researchers combined a common spatial pattern (CSP) with particle swarm optimization (PSO) for channel selection. As for a motor imagery experiment of
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left or right hand movement imagery experiment of left or right hand movement,
channels C3 or C4 which are located at the center of motor imagery cortex and
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depicted Fig. are the most important. We assume that only using the elec-
trodes that are located near C3 and C4 can improve accuracy. We set a channel
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impact factor and modified the position update equation in BPSO. We suggest an optimization- based electrode selection method with high accuracy and a small number of electrodes. We computed accuracy and compared electrode
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selection methods using binary PSO (BPSO), BPSO with a channel impact factor, a genetic algorithm (GA) [4], and harmony search (HS) [5]. CSP [6] was applied as an extraction method, and SVM [7] was applied for classification and
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to determine one of the parameters of a fitness function. We applied principal component analysis (PCA) [8] to reduce the dimensionality when it was too large. This paper is structured as follows: section 2 describes related works such as preprocessing, feature selection, and feature extraction. The proposed
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method is described in section 3. Experimental data and results are provided
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in sections 4 and 5, respectively.
2. Related Works
2.1. Linear Filtering
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An EEG contains many artifacts, which can originate from eye blinking
and unintentional movements. Those artifacts need to be removed for better classification performance. Linear filtering can be used to diminish artifacts that are located at a specific frequency. Electromyography (EMG) artifacts can be removed by a low-pass filter, and Electrooculography (EOG) artifacts can
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be removed by a high-pass filter [9]. Linear filtering was commonly used in early studies to remove artifacts in EEG signals [10]; however, this algorithm is unsuitable for use when the neurological phenomenon of interest and the EOG or EMG overlap in the same frequency band.
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2.2. Particle Swarm Optimization PSO was developed by Kennedy and Eberhart in 1995 [11]. It is a meta-
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heuristic optimization technique based on the simulation of social behavior of flocks. Even though this algorithm has fewer variables to set and is considerably
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simpler than GA, it performs as well as a GA. This algorithm has received
attention throughout the world and has been applied to many research fields [12]. Particles that are equivalent to individuals in a GA are randomly distributed in
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a hyper-dimensional search space and fly through the search space at a certain velocity. The swarm consists of particles that have the ability to memorize
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previous experiences, which makes it possible to consider the present optimal solution as well as the previous optimal solution when each particle is searching 65
for the most optimal solution in the search space. Thus, the velocity of each
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particle is influenced by its own best position found so far and the best solution that was found so far by its neighbors. The best solution of the swarm is called gbest, and the best solution of each particle is called pbest. Each particle
position and velocity updates. The position and velocity of the particles are
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compares its fitness value and continues to search for an optimal solution with
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updated as follows:
i vk+1 = wvki + c1 r1 (pik − xik ) + c2 r2 (gbest − xik )
(1)
xi (t + 1) = xi (t) + vi (t + 1)
(2)
where c1 and c2 are real numbers, and r1 and r2 are random values between 0
and 1. Eventually, the swarm will converge to an optimal position. 2.3. Binary Particle Swarm Optimization
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BPSO is a discrete version of PSO [13]. The way to update a particle’s velocity in BPSO is the same as PSO. The difference between PSO and BPSO is that the particles in BPSO are comprised of either a 0 or 1 unlike PSO. This
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differentiates position update equation in BPSO from that in PSO. The update
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equations in BPSO are as follows S(v) = (1 + e−v )−1
xik+1 = 0
i if τ > S(vk+1 )
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i if τ < S(vk+1 )
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xik+1 = 1
(4)
(5)
where τ is a random value between 0 and 1. After evaluating velocity, we apply
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(3)
this value to a sigmoid function and then compare the output of sigmoid function and random value τ . If the output of the sigmoid function is larger then τ , the
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particle’s position is updated to 1, and vice versa.
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3. Proposed Methods
3.1. BPSO for Electrode Selection
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We applied a BPSO algorithm for electrode selection that performs feature selection in a BCI system. The flow chart of BPSO applied in BCI is depicted
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in Fig. 1. In order to reduce overfitting in feature extraction, features that could be artifacts and include noise need to be removed. The BPSO method
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for electrode selection selects important features among all features for better feature extraction.
We keep doing feature selection until we select optimal features. We regard
the electrode space as the solution space, and each particle’s position is assigned to a value of either 0 or 1, where 0 means that we do not use the electrode, and
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1 means that we do use the electrode. The first task is to initialize particle position, velocity, c1 , and c2 . After initialization, we choose particles whose position value is 1. Before feature extraction performed by CSP, the inputs of feature extraction consist of features whose particle position value is equal to 1. An SVM is applied for classification after feature extraction, and accuracy
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is calculated. When the dimensionality was too large to calculate, we applied
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Figure 1: Flow chart of binary particle swarm optimization applied in BCI system.
PCA in order to reduce the number of dimensions. We set the fitness function according to three factors, which are accuracy, the number of electrodes used for feature extraction, and the slack variables measured in the SVM. Of the three factors, we assign the strongest impact to accuracy and smallest impact
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to slack variables. The fitness function is as follows:
F itness = 0.6 ×
accuracy 100
accuracy =
+ 0.2 ×
NT + 1 − NS NT
# of correctly classified data # of test data
+ 0.2ξ
(6)
(7)
where parameter ξ, NT , NS represents the slack variables, total number of electrodes, and the number of selected electrodes, respectively. The particle
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position, velocity, pbest, and gbest are then updated during iterations, unless the number of iterations exceeds the maximum set value. The particle position and velocity are updated by equations (1), (3), (4) and (5). When the number of
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iterations exceeds the maximum value, we stop the electrode selection method
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and consider gbest to be the optimal electrode combination. The parameters are set as follows: the number of particles is set to 50, the maximum number of
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iterations was set to 500, c1 and c2 were set to 2, and r1 , r2 , and τ were set to random values between 0 and 1. The n-th inertial weight is as follows : n h
(8)
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w(n) = wmax − (wmax − wmin )
where wmax is set to 0.9 ; wmin is set to 0.6; and h represents the maximum
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number of iterations.
3.2. BPSO with Channel Impact Factor for Electrode Selection In order to apply BPSO with a channel impact factor in a BCI system, we also set the electrode space to be the solution space, and each particles
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position can have a value of either 0 or 1, where 0 means that we do not use the electrode, and 1 means that we do use the electrode. The position value of
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the particle is determined by equations (4) and (5), and τ is the criteria used to determine the particle’s position in those equations, that is , the smaller τ is,
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the higher the probability is that the position will have a value of 1, and vice versa. When performing motor imagery experiments, we know that left hand movement imagery is mostly related to electrode C4 ; on the other hand, right hand movement is mostly affected by electrode C3 . We assume that it could have higher accuracy with electrode combinations that consist of electrodes C3
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and C4 , as well as electrodes located near C3 and C4 . In order to confirm this
assumption, we divide five distance sections based on C3 and C4 . The closer the electrode is to C3 or C4 , the lower value we use for δ, which is the impact factor for the electrode. The farthest distance from C3 or C4 is 0.9, so we divide five distance groups into 0.18, 0.36, 0.54, 0.72 and 0.9. We set δ as 0.7, 0.8, 0.9,
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1 and 1.1, respectively, according to the group. We set δ as 0.6 for electrode C3
i if θ < S(vk+1 )
xik+1 = 0
if
i θ > S(vk+1 )
(9)
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xik+1 = 1
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or C4 . We modified equations (4) and (5) as follows:
(10)
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where θ is δ×τ . The remaining process is the same as that described in section A. We also use the same fitness function described in equation (6), and parameters
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are set the same as in BPSO for electrode selection. 3.3. Genetic Algorithm for Electrode Selection
We applied a GA algorithm for electrode selection in a BCI system. The
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process of GA for electrode selection is the same as the process of BPSO. In a GA algorithm, we also use the same fitness function described in equation (6). The parameter setting is carried out as follows. We used 50 individuals and set the maximum number of iterations to 500. We set the crossover probability to
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0.8 and the mutation probability to 0.011. 3.4. Harmony Search Algorithm for Electrode Selection
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The HS algorithm was applied for electrode selection in the same way as
BPSO and GA were, as described above. The process of HS is the same as the
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process of BPSO and GA. In the HS algorithm, we set the aesthetic standard to that of equation (6), which is used as a fitness function in BPSO and GA. We used 50 harmonic memories and 59 kinds of instruments and set the maximum practice to 500.
4. Experimental Data and Results
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4.1. Experimental Data and Environment In order to obtain the acquisition data of brain signal, we needed shield room to acquire the EEG data. It was built with sound insulation and absorption materials, shielding materials to keep and obtain the pure EEG signal 8
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Figure 2: Shield Room for Brain-Computer Interface in URIS Lab., Chung-Ang
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University, KOREA
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in URIS lab., Chung-Ang University, KOREA, 2011. In this paper, we used compumedics neuroscan synamps2 (64-ch EEG acquisition system), and curry7
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as software. Before experimenting on our paradigms, we used BCI competition IV datasets, general data, about motor imagery for brain-computer interface system. Its dataset 1 is used in this study [14]. Several healthy subjects took part in the experiment, and the 59 electrodes. For each subject, two classes
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of motor imagery were selected from the three classes (left / right hand, and foot). Motor imagery was performed without feedback throughout the whole session. As a visual cue, arrows pointing left, right, and down were displayed on the screen. While the cue was on the screen, the subject was instructed to perform the cued motor imagery task. These periods were interleaved with 2sec
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of a blank screen and 2sec with a fixed cross shown in the center of the screen. The fixed cross was superimposed on the cues, i.e., it was shown for 6sec. These data sets are provided with complete marker information.
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4.2. Experimental Results
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With BPSO, BPSO with a channel impact factor, GA and the HS algorithm, we executed the experiment 20 times for each subject. Among the seven subjects who imagined left or right hand or foot movements, we selected three
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subjects (subjects b, d, and e) who were instructed to imagine either left or right hand movements Table 1. illustrates the comparison of the number of selected
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electrodes and accuracy between BPSO, BPSO with a channel impact factor, GA and HS. CH stands for electrode.
The results of the selected electrode number and accuracy are an average
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over 20 repetitions of the experiment. As we can see, the selected electrodes are different for each subject even though we used the same optimization algorithm. In the case of subject d, on the one hand, BPSO with a channel impact factor had the highest classification accuracy with the lowest number of
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selected electrodes. HS, on the other hand, had the lowest classification accuracy with a large number of electrodes. HS used five more electrodes and had
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20.4% lower accuracy than BPSO with a channel impact factor. In the cases
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of subject b and subject e, BPSO with a channel impact factor also had the highest classification accuracy. From Table 1, we can confirm the assumption
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that we can have higher accuracy with electrode combinations that consist of electrodes C3 and C4 as well as electrodes located near C3 and C4 . Even though we applied the same optimal algorithm to the same subject, we found different
Table 1: Comparison of the number of selected electrodes and accuracy between GA, HS, BPSO and BPSO with channel impact factor.
Subject b
Subject d
Subject e
# of CH
ACC
# of CH
ACC
# of CH
ACC
GA
29
60.55
28.65
63.95
29
65.55
HS
30
58.55
29
60.55
31
64.55
BPSO
29
75.55
29
75.55
29
75.55
BPSO with CIF
23
78.55
24
80.95
23
77.55
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Figure 3: (a) Histogram of selected electrodes for subject d by BPSO with channel
impact factor. (b) Selected electrodes for subject d that were selected more than 10 times. (c) Bounding box for the actually selected electrode. (top view) (d) Bounding box for the actually selected electrode. (rear view)
numbers of selected electrodes. In order to overcome this problem, we executed
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as many experiments as we could and averaged the results. As we executed the experiment 20 times, we estimated how many times each electrode was selected. The maximum frequency was set to 20, and we chose the electrodes that were selected more than or equal to 10 times. Fig. 4(a) illustrates the frequency of the selected electrodes for subject d by BPSO, and the inside of the red square
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Figure 4: Comparison between accuracy of BPSO with selected electrodes for each
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trial and accuracy with selected electrodes that were selected more than 10 times.
box has electrodes that were selected more than or equal to 10 times, and the
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corresponding electrodes are depicted Fig. 4(b). The number of electrodes that were selected more than 10 times is 25. Before using electrodes that were selected more than 10 times, we need to
confirm whether or not better accuracy can be attained with electrodes other
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than those selected using proposed methods such as BPSO and BPSO with a channel impact factor during one experiment. Fig. 4. depicts accuracies with five kinds of selected electrodes by BPSO
with a channel impact factor and accuracy with selected channels depicted in Fig. 3 (b). As we can see from Fig. 4, using the channels that were selected more
210
than 10 times can improve accuracy 6% more than using electrodes selected by one experiment of BPSO with a channel impact factor. Fig. 5(a) illustrates the frequency of the selected electrodes for subject d
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Figure 5: (a) Histogram of selected electrodes for subject d by BPSO with channel
impact factor. (b) Selected electrodes for subject d that were selected more than 10 times. (c) Bounding box for the actually selected electrode. (top view) (d) Bounding box for the actually selected electrode. (rear view)
by HS. electrodes inside the red box were selected more than or equal to 10 times, and the corresponding electrodes are depicted Fig. 5(b). The number of
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electrodes that were selected more than 10 times is 30. Fig. 6. depicts accuracies with five kinds of selected electrodes by HS with a channel impact factor and accuracy with the selected electrodes depicted in Fig. 6(b). As we can see from Fig. 6, using the electrodes that were selected more
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Figure 6: Comparison between accuracy of HS with selected electrodes for each
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trial and accuracy with selected electrodes that were selected more than 10 times.
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than 10 times can improve accuracy 6% more than using cannels selected by BPSO with a channel impact factor. From Fig.4. and 6 we can confirm that the
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selected electrodes that were selected by BPSO with a channel impact factor and were selected more than 10 times can be used in the next experimental session instead of using all electrodes and that these selected electrodes can improve accuracy well.
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5. Conclusion
This paper proposed the use of an optimal electrode instead of using all
electrodes for BCIs. For this purpose, optimal electrode selection methods employing BPSO, BPSO with a channel impact factor, GA, and HS are proposed. By applying these four methods, we selected few electrodes with high accuracy.
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BPSO with a channel impact factor outperformed BPSO, GA, and HS in that it achieved the highest accuracy with the smallest channel number. HS performed
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the worst among four optimization algorithms. We also selected channels that were selected at least 10 times in 20 experiments and found out that these
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channels have better accuracy than the selected channels that were determined
from one trial of the proposed method. In the future, we will adjust the fitness
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function in order to obtain the same optimal channel combination every time the calculation is performed. We can also customize EEG cap with optimized
imagery and steady state visual evoked potentials.
Acknowledgments
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electrodes. The EEG-electrode cap can be used to suit your needs such as motor
This research was supported by the National Research Foundation of KO-
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REA[NRF] grant funded by the KOREA government [MEST] [2012-0008726]
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