Journal of Robotics, Networking and Artificial Life
Vol. 5(4); March (2019), pp. 269–272
DOI: https://doi.org/10.2991/jrnal.k.190220.014; ISSN 2405-9021 print; 2352-6386 online
https://www.atlantis-press.com/journals/jrnal

Research Article

Preliminary Comparative Experiments of Support Vector
Machine and Neural Network for EEG-based BCI
Mobile Robot Control
Yasushi Bandou, Takuya Hayakawa, Jun Kobayashi*
Department of Systems Design and Informatics, Kyushu Institute of Technology, Iizuka 820-8502, Japan

ARTICLE INFO
Article History
Received 16 October 2018
Accepted 19 November 2018
Keywords
Brain computer interface
electroencephalography
support vector machineneural network
mobile robot control

ABSTRACT
Here we present experimental results of Electroencephalogram (EEG)-based Brain Computer Interface (BCI) for mobilerobot control by means of Support Vector Machine (SVM) and Neural Network (NN). The authors had trained NNs using
EEGs collected from subjects and verified the performance as BCI; however, the results were unsatisfactory for practical use.
In this study, we have used SVM with Radial Basis Function (RBF) kernel function for further improvement and compared theperformance with the NNs. Consequently, the SVMs outperformed the NNs in almost all cases.

© 2019 The Authors. Published by Atlantis Press SARL.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
Brain–Computer Interface (BCI) provides you with a means to convey
your intention and volition to a computer on the basis of your brainactivity. A lot of studies on BCI have used Electroencephalogram(EEG) because of its invasiveness. The BCI technology not only
makes up for impairments but also expands and enhances abilities ofhuman beings. Electric wheelchair control and robotic arm control
are typical application examples of EEG-based BCI. Singla et al. [1,2]developed a Steady State Visual Evoked Potential (SSVEP)-based
BCI for controlling a wheelchair using multi-class SVM. Menget al. [3] experimentally investigated a noninvasive BCI for reach andgrasp task of robotic arm. Mobile robot control by EEG-based BCI isalso a challenging and promising technology [4].
The authors have been developing an EEG-based BCI for mobilerobot control by means of machine learning techniques, especially multi-layered neural networks (NNs) [5,6]. We fabricatedan omnidirectional mobile robot and collected brain waves
from subjects picturing a movement of the mobile robot in theirbrains. Using the collected brain waves, we trained multi-layered
NNs to obtain brain wave pattern classifiers for BCI and evaluated them. However, the trained NNs could not achieved practicalclassification performance.
Support Vector Machine (SVM) is also a popular machine learning
method that has been applied to various fields and accomplishedtremendous results. In this study, we use SVM with RBF kernel
Corresponding author. Email: jkoba@ces.kyutech.ac.jp*function as classifiers for EEG patterns and compare their performance with the multi-layered NN we used in the previous studies.

2. EXPERIMENTS FOR EEG DATA­COLLECTION
The authors carried out EEG measurement experiments to collect
EEG signals for training NNs and SVMs. The same experiments
had been conducted for three subjects in the previous study [5].
For this study, we have newly obtained EEG signals from threeother subjects, a 21-year-old and two 22-year-old male college students; EEG signals recorded from totally six subjects (named A–F)
were used in this study. As with the previous EEG measurement
experiments, the EEG headset shown in Figure 1 were used forrecording EEG signals, and the omni-wheel mobile robot shown inFigure 2 that moves following predetermined commands was used.
Commands for move forward, backward, to right, and to left weregiven to the mobile robot in these experiments.
Each subject imagined 30 trials per direction, i.e. 120 trials in total,
at each day for five different days; therefore, we collected EEG dataof 600 trials for each subject. One trial consists of about 5 s rest,
5 s task (imagining), and 5 s rest again. Brain waves of a subject wererecorded throughout the 15 s trials. The subject silently counted 5 sduring the first rest, and then pressed a key of a keypad just before
getting started the task so that a PC for EEG recording could recognize the timing. When a beep sounded after 5 s, the subject stopped
the task and took a rest until the next trial for about 5 s.

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Y. Bandou et al. / Journal of Robotics, Networking and Artificial Life 5(4) 269–272

3. EEG SIGNAL CLASSIFICATION WITH
MULTI-LAYERED NEURAL NETWORKAND SUPPORT VECTOR MACHINE

Figure 1 | EMOTIV EPOC (wireless portable EEG headset).

Stacked Autoencoder (SAE) with a softmax layer at the output wasadopted as multi-layered NN for EEG pattern classification. In thetraining process of the NNs, the hyperparameters were optimized
by Bayesian optimization [7]. Table 1 describes the optimized
hyperparameters and their options. The SAEs and Bayesian optimization were implemented using Chainer (ver. 1.18.0, PreferredNetworks, inc.) [8] and GPyOpt (ver. 1.2.0, the Sheffield machinelearning group and collaborators) [9].
As another EEG pattern classifier, nonlinear SVM with RBF kernel
function was used. In the training process of the SVMs, input feature vectors were scaled, and parameters of RBF function and regularization were optimized using grid search. LIBSVM (ver. 3.22,Chih-Chung Chang and Chih-Jen Lin) [10] was used for implementing the SVMs.

Figure 2 | Omni-wheel mobile robot.

The classification performance of the NNs and SVMs trained inthis study was evaluated using fivefold cross validation. Table 2shows the computation environment for development of the SAEs
and SVMs.

Figure 3 | Arrows indicating directions (up, down, right, and left).

The subjects were given two different imagining tasks, called
CLOSED-EYES and OPEN-EYES.
•• CLOSED-EYES: close your eyes and imagine a specified arrow.
•• OPEN-EYES: watching the mobile robot moving to a certain
direction, imagine the robot’s motion.
The CLOSED-EYES experiment was carried out first, then theOPEN-EYES one. Figure 3 shows arrows, one of which the subjects imagined in a trial of the CLOSE-EYES task in the EEG measurement experiment. Up, down, right, and left arrows correspondto moving forward, backward, to right, and to left for the mobile
robot, respectively.
Figure 4 shows the procedure of the trials in the experiments for
OPEN-EYES task. At the beginning of the procedure, a subject
counts 5 s silently while gazing the mobile robot being at rest.
During the task period, the subject keeps gazing at the mobile
robot moving to a certain direction and imagine the robot’s motion
for 5 s. As the task finish, the subject closes their eyes for rest andthe mobile robot automatically moves back to the initial position.
The subject can notice when they should end the rest even with
CLOSED-EYES because the mobile robot emits a sound during thereturn movement.
The EEG signals recorded with 14 electrodes of the EPOC for
5 s were preprocessed according to the following methods. First,
EEG signal components in the frequency range from 8 to 30 Hz
were extracted by a band-pass filter. Second, power spectrum of
the filtered EEG signals was calculated, and then moving averagewas applied to them. Finally, the power spectrum was normalized
between 0 and 1, and resampled with 1 Hz. This preprocessing
produces a 322-dimensional feature vector from 5 s EEG signals,
which was used as an input into the EEG pattern classifiers.

Figure 4 | Procedure of trials in experiments for OPEN-EYES.
Table 1 | Hyperparameters and options
Hyperparameters

Options

Number of hidden layers
Number of nodes in 1st hidden layer
Number of nodes in 2nd hidden layer
Number of nodes in 3rd hidden layer
Iteration of pretraining each AE (epoch)Iteration of finetuning (epoch)
Dropout 50% in pretraining AEs
Dropout 50% in finetuning

1–3250 or 300
150 or 200
50 or 100
1000 or 2000
5000 or 10,000
OFF or ON
OFF or ON

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4. RESULTS AND DISCUSSION
Tables 3 and 4 shows results of the comparative experimentsof the NNs and SVMs trained with the EEG signals recorded inCLOSED_EYES task. The values in the rightmost columns areaveraged classification rate percentages of the five experiment days.
The better averaged classification rate between the NNs and SVMs
are shown in red. Comparative results in the case of OPEN_EYES
are shown in Tables 5 and 6.
These results clearly show that the SVMs outperformed the NNs
regardless of the task types, CLOSED_EYES and OPEN_EYES. The
averaged classification rate percentages of all subjects were 55.03%
for the NNs and 63.49% for the SVM in CLOSED_EYES; 50.61%
for the NNs and 61.17% for the SVM in OPEN_EYES.
The input feature vectors to the classifiers were extracted from theEEG signals obtained in the experiments as mentioned in Section 2.
The EEG signals were recorded when the subjects were imagining one of the four motions of the mobile robot. That means theextracted feature vectors belongs to one of the classes representing mobile robot motions, such as move forward, backward, to left,and to right. In the experiments, the stacked autoencoder NNs andnonlinear SVMs with RBF kernel function were trained to recognize the class of the input feature vectors.
The NNs scaled the input feature vectors’ dimension down
through the hidden layers and finally classified them at the softmax output layer. On the other hand, the nonlinear SVMs transforms the input feature vectors into a higher dimensional feature
space with RBF kernel function so that a decision boundary canTable 2 | Specifications of computation environment for development ofSAEs and SVMs
CPUMemoryStorage
GPUOSIntel® Core™ i7-6800K CPU @ 3.40 GHz
16 GB (DDR4-2133 4 GBx4)
SSD 240 GB + HDD 1 TB
NVIDIA GeForce GTX1060 6 GB GDDR5Ubuntu 16.04 LTS

Table 3 | Classification rate percentages of NNs trained with Bayesian
optimization using 120 samples recorded in CLOSED_EYES
Subject

1st day

2nd day

3rd day

4th day

5th day

Average

AB
C
DEF

55.00
50.00
44.17
58.34
45.83
76.67

57.50
70.00
49.17
70.00
45.00
42.50

66.67
53.33
51.77
54.17
56.67
40.00

79.17
66.67
76.67
50.83
44.17
39.17

92.50
54.17
33.33
40.83
29.17
57.50

70.17
58.83
51.02
54.83
44.17
51.17

Table 4 | Classification rate percentages of SVMs trained using
120 samples recorded in CLOSED_EYES
Subject

1st day

2nd day

3rd day

4th day

5th day

Average

AB
C
DE
F

57.97
64.70
93.63
52.44
64.64
65.18

49.04
63.04
86.73
49.94
71.43
68.16

64.40
71.79
55.36
55.95
65.00
65.18

58.80
72.50
59.70
61.25
72.32
63.40

62.74
56.61
62.02
55.71
58.33
56.79

58.59
65.73
71.49
55.06
66.34
63.74

271

Table 5 | Classification rate percentages of NNs trained with Bayesian
optimization using 120 samples recorded in OPEN_EYES
Subject

1st day

2nd day

3rd day

4th day

5th day

Average

AB
CD
E
F

95.83
66.67
60.00
52.50
49.17
51.67

48.33
57.50
40.00
79.17
33.34
62.50

45.83
60.83
49.17
37.50
45.84
55.84

41.67
33.33
76.67
38.34
19.17
48.34

75.00
43.33
38.33
35.00
31.67
45.8361.33
52.33
52.83
48.50
35.84
52.84

Table 6 | Classification rate percentages of SVMs trained using
120 samples recorded in OPEN_EYES
Subject

1st day

2nd day

3rd day

4th day

5th day

Average

AB
C
DEF

63.04
68.75
65.00
69.52
62.62
54.64

56.61
73.57
58.57
49.88
67.50
56.67

62.68
66.90
67.20
55.06
63.39
45.24

66.31
63.75
60.24
52.86
54.94
43.21

65.83
77.02
69.40
60.00
72.20
42.62

62.89
70.00
64.08
57.46
64.13
48.48

linearly separate the feature vectors into classes more easily.
The transformation to a higher dimensional space might have
led to the results that the SVMs achieved the better classification
performance.

5. CONCLUSION
This paper presented the comparative experimental results of twomachine learning methods, SVM with RBF kernel function andstacked autoencoder NN with a softmax output layer, which weretrained as EEG-based BCI for mobile robot control. The experimental results showed that the SVMs with RBF kernel function
outperformed the NNs trained with hyperparameter optimizationin almost all cases. The authors have presumed that the featurevector transformation into a higher dimensional feature space wasa key factor for the improvement by SVMs. Therefore, from thatpoint of view, the authors will design an EEG pattern classification
method including feature extraction to achieve practical EEGbased BCI mobile robot control.

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Authors Introduction
Dr. Jun Kobayashi

Mr. Yasushi Bandou
Mr. Yasushi Bandou is a master student at Graduate School of Computer
Science and Systems Engineering, Kyushu
Institute of Technology. His main interestis brain computer interface using support
vector machine.

Mr. Takuya Hayakawa
Mr. Takuya Hayakawa is a master student at Graduate School of Computer
Science and Systems Engineering, Kyushu
Institute of Technology. His main interestis brain computer interface using neuralnetwork.

Dr. Jun Kobayashi is an Associate
Professor at Department of Systems
Design and Informatics, Kyushu Instituteof Technology. He obtained his PhD
degree at Kyushu Institute of Technology
in 1999. Currently, his main interests are
Robotics, Physiological Computing, andCybernetic Training.