Classification Accuracy Improvementof Chromatic and High–Frequency
Code–Modulated Visual Evoked
Potential–Based BCI
Daiki Aminaka, Shoji Makino, and Tomasz M. Rutkowski(B)Life Science Center of TARA at University of Tsukuba,
1-1-1 Tennodai, Tsukuba, Ibaraki, Japan
tomek@bci-lab.info
http://bci-lab.info/

Abstract. We present results of a classiﬁcation improvement approach
for a code–modulated visual evoked potential (cVEP) based brain–computer interface (BCI) paradigm using four high–frequency ﬂashingstimuli. Previously published research reports presented successful BCI
applications of canonical correlation analysis (CCA) to steady–state visualevoked potential (SSVEP) BCIs. Our team already previously proposed
the combined CCA and cVEP techniques’ BCI paradigm. The currently
reported study presents the further enhanced results using a support vector machine (SVM) method in application to the cVEP–based BCI.

Keywords: Brain–computer interfaces
classiﬁcation

1

·

ERP

·

cVEP

·

EEG

Introduction

A brain computer interface (BCI) is a technology that employs human neurophysiological signals for a direct brainwave–based communication of a brain
with an external environment, and without dependence on any muscle or peripheral nervous system actions [9]. Particularly, in the case of patients suﬀeringfrom locked–in–syndrome (LIS) [6], BCI could help them to communicate orcomplete various daily tasks (type letters or control their environments using
Internet of Things technologies, etc). The BCI shall create a feasible option for
amyotrophic lateral sclerosis (ALS) or coma patients to communicate with theirfamilies, friends or caretakers by using their trained, and properly classiﬁed,
brainwaves only [9].
We propose to utilize and classify EEG brainwaves in response to a code–
modulated visual evoked potential (cVEP). The cVEP is a natural response to
the visual stimulus with speciﬁc code–modulated sequences [3,4]. It is generated by the brain when the user gazes at a light source which ﬂashes with a
speciﬁcally designed code–modulated sequence. The cVEP–based BCI belongsc Springer International Publishing Switzerland 2015
Y. Guo et al. (Eds.): BIH 2015, LNAI 9250, pp. 232–241, 2015.
DOI: 10.1007/978-3-319-23344-4 23

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to the stimulus–driven BCIs, which do not require a longer training comparing
to the imagery–driven paradigms [9]. In this paper, we report EEG classiﬁcation
improvement results using a support vector machine (SVM) classiﬁcation technique instead of the previously implemented by our team canonical correlation
analysis (CCA) [1]. The CCA technique has been successfully used for classiﬁcation of steady state visual evoked potentials (SSVEP) and it has resulted withgood outcomes [5]. Next, a cVEP–based BCI has been also successfully implemented using the CCA [4]. We also successfully reproduced and further extended
the above results based on a combination of cVEP stimuli and CCA–based classiﬁcation [1]. A problem that raised in our previous research was related to abiased classiﬁcation accuracy caused by the CCA towards some of the BCI commands. The training dataset for a classiﬁer was created from cVEP responses
when user gazed at the ﬁrst ﬂashing LED pattern (the top location in Figure 1).
The remaining training patterns were created by applying circular shifts of theﬁrst LED cVEP’s response. This method was responsible for the possible accuracy drop due to a limited number of training examples. We propose in thispaper to use the linear SVM–based classiﬁer to improve the cVEP BCI accuracy
and to minimize any biases related to potential overﬁtting problems. In the presented project we also propose to use the RGB light–emitting diodes (LEDs) inorder to evoke four types of cVEPs. We also utilize the higher ﬂashing pattern
carrier frequency of 40 Hz and compare our results with the classical settingof 30 Hz, of which refreshing rates has been chosen previously due to a limited
computer display refresh rate of 60 Hz [2,3]. Moreover, we propose to use thechromatic green–blue stimulus [8] as a further extension in our project and wecompare results with the classical monochromatic (white–black) set up.
From now on the paper is organized as follows. In the following section we
describe materials and methods used in this study. Next, results and discussion
are presented. Conclusions together with future research directions summarize
the paper.

2

Materials and Methods

The experiments reported in this paper were performed in the Life Science Center of TARA, University of Tsukuba, Japan. All the details of the experimental
procedures and the research targets of the cVEP–based BCI paradigm wereexplained in detail to the eight users, who agreed voluntarily to participate
in the study. The electroencephalogram (EEG) cVEP–based BCI experiments
were conducted in accordance with The World Medical Association Declarationof Helsinki - Ethical Principles for Medical Research Involving Human Subjects.
The experimental procedures were approved and designed in agreement withthe ethical committee guidelines of the Faculty of Engineering, Information and
Systems at University of Tsukuba, Tsukuba, Japan (experimental permission
no. 2013R7). The average age of the users was of 26.9 years old (standard deviation of 7.3 years old; seven males and one female).

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2.1

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Experimental Settings

The visual stimuli were presented to the subjects as ﬂashing light sources delivered via the RGB LEDs. The LEDs were driven by square waves generated fromARDUINO UNO micro–controller board. The generator program was written
by our team using C–language.
In this study we used m − sequence encoded ﬂashing patterns [4] to create
four commands of the cVEP–based BCI paradigm. The m−sequence is a binary
pseudorandom string, which could be generated using the following equation,x(n) = x(n − p) ⊕ x(n − q),(p > q),(1)

where x(n) is the nth element of the m − sequence obtained by the exclusive–or(XOR) operation, denoted by ⊕ in the equation (1), using the two preceding
elements indicated by their positions (n − p) and (n − q) in the string. In thisproject p = 5 and q = 2 were chosen. An initial binary sequence was decided, tocreate the ﬁnal m − sequence, used in the equation (1), as follows,
xinitial = [0, 1, 0, 0, 1].

(2)

Finally, the 31 bits long sequence was generated based on the above initial
sequence as in equation (2). The interesting m − sequence feature, which is very
useful for the cVEP–based BCI paradigm design, is an unique autocorrelation
function. The autocorrelation function has only a single peak at the period sample
value. If the m−sequence period is N , the autocorrelation function will result withvalues equal to 1 at 0, N, 2N, . . . and 1/N otherwise. It is also possible to introduce
a circular shift of the m − sequence denoted by τ , to create a set of m − sequenceswith shifted autocorrelation functions, respectively. In this study, the shifted time
length has been deﬁned as τ = 7 bits. Three additional sequences have been generated using shifting by τ , 2·τ and 3·τ , respectively. During the online cVEP–based
BCI experiments the four LEDs continued to ﬂash simultaneously using the time–
shifted m − sequences as explained above. Two m − sequence period lengths havebeen tested to investigate whether they would aﬀect the cVEP responses. The conventional full m − sequence period of T = 516.7 ms (based on the conventional
computer screen refresh rate of 60 Hz and referred as “a low ﬂashing frequency”)
and the proposed T = 387.5 ms (referred as “a high ﬂashing frequency”) have beentested. The experimental setting with four LEDs arranged on a square frame infront of a user is depicted in Figure 1.
2.2

EEG Signal Acquisition and Processing

During the cVEP–based BCI EEG experiments the users were seated on a comfortable chair in front of the LEDs (see Figure 1). The distance between user’seyes and LEDs was about 30 ∼ 50 cm (chosen by the users for a comfortable viewof the all LEDs). An ambient light was moderate as in a typical oﬃce. The EEG
signals were captured with a portable EEG ampliﬁer system g.USBamp fromg.tec Medical Engineering, Austria. Eight active wet (gel–based) g.LADYbird

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235

EEG electrodes were connected to the head locations of O1, O2, PO3, PO4, P1,
P2, Oz, and POz as in an extended 10/10 international system [9]. These positions were decided due to the visual cortex responses targeting experiment [9].
The ground electrode was attached to head location FPz and the reference to aleft earlobe, respectively. Details of the EEG experimental set up are summarized in Table 1. An EEG sampling frequency was set to 512 Hz and a notch
4th –order Butterworth IIR ﬁlter at rejection band of 48 ∼ 52 Hz was applied toremove power line interference of 50 Hz. Moreover, the 8th –order Butterworth
IIR band–pass ﬁlter at a pass band of 5 ∼ 100 Hz was applied to remove eyeblinks and a high frequency noise.
The OpenViBE [7] bio–signal data acquisition and processing environment,
together with in–house programmed in Python extensions, were applied to realize
the online cVEP–based BCI paradigm. To avoid user’s eye blinks, each trial togaze at a single LED was separated with pauses during the experimental sessions(see details in Table 1).
In the data acquisition phase, ﬁrst the users gazed at top ﬂashing LED inorder to collect classiﬁer training dataset, as instructed verbally by the experimenter conducting the study. Twenty m − sequence cycles were repeated ina single EEG capturing session. In short, sixty cVEPs to m − sequence based
ﬂashing were collected for each direction in a single experimental trial. The triggers indicating the onsets of the m − sequences were sent to g.USBamp directlyfrom the ARDUINO UNO micro–controller to mark the beginning of each cVEP
response. Finally, four experiment types were conducted for each user:– the conventional low frequency;– the proposed high frequency;– and for the each above setting two color modes using white–black and green–
blue ﬂashing LEDs were applied.
Table 1. EEG experiment condition details
Number of users

8

Single session length

8 and 11 secondsm − sequence lengths T

516.7 and 387.5 ms

Shifts τ

116.7 and 87.5 ms

EEG recording systemg.USBamp by g.tec with active wet(gel–based) g.LADYbird electrodes

Number of EEG channels 8
Electrode locations

O1, O2, Po3, Po4, P1, P2, Oz and Poz

Reference electrode

Left earlobe

Ground electrode

FPz

Notch ﬁlter

Butterworth 4th order with a rejection band of 48 ∼ 52 Hz

Band–pass ﬁlter

Butterworth 8th order with a passband of 5 ∼ 100 Hz

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Fig. 1. The user is seating in front of a frame with four visual stimulation RGB LEDs
used in this study. The top, bottom, right and left LEDs’ ﬂickering patterns correspondto the four diﬀerent m − sequences, respectively. On the right side of the photographthere is the g.USBamp together with g.TRIGbox from g.tec Medical Engineering, Austria, used in the study. A laptop computer runs OpenViBE EEG data acquisition andprocessing environment.

2.3

The cVEP Responses Classification

A linear SVM classiﬁer was used in this study to compare accuracies with apreviously successfully implemented CCA method [1]. In the training session asingle dataset containing the cVEP responses to top ﬂashing LED was used.
The remaining three cVEP responses were constructed by shifting the top LED
response by τ , 2 · τ and 3 · τ , where τ ∈ {87.5 ms, 116.7 ms}. We used the linear
SVM classiﬁer to identify the intended by the user ﬂickering patterns. The cVEP
response processing and classiﬁcation steps were as follows:
1. For the classiﬁer training purposes, capturing the EEG cVEP y1 obtained in
response to the ﬁrst m − sequence. A procedure to construct the remaining
training patterns yi , (i = 2, 3, 4), based on the original recorded y1 sequence
was as follows:(3)yi (t) = y1 (t − (i − 1)τ ),where τ was the circular shift and t indicated a position in the sequence.
2. Averaging the captured j cVEPs as yi,j (t) for each target i separately. The
averaged responses ȳi were used for the linear SVM classiﬁer training. In
this study, there were N = 60 training datasets and the number of averaged

Classiﬁcation Accuracy Improvement of cVEP-Based BCI

237

Fig. 2. The results of CCA and linear SVM–based classiﬁcation from the eight users
participating in the study presented in form of bar plots. There are four results depicted
for each user, namely from the green–blue high frequency (green); green–blue conventional low frequency (light green); white–black higher frequency (orange); white–black
lower frequency cases (yellow) bar color, respectively. The solid–colored bars resulted
from the linear SVM application. On the other hand, the transparent bars, which overlap the solid counterparts, resulted from the CCA–based classiﬁcation. The pairwise
Wilcoxon–test was applied for each classiﬁcation pair and the signiﬁcant diﬀerences(p < 0.05) have been denoted with “∗.” The theoretical chance level of the experiments was of 25%.

responses was M = 5. The averaging procedure was as follows,ȳi,l =

1
M

l+M
−1

yi,j ,(4)j=lwhere l = 1, 2, . . . , N − M + 1 was the dataset number.
3. For the test classiﬁcation purposes, cVEPs from remaining BCI sessions, notused for the classiﬁer training, to four target m − sequences were applied.
The results of the above procedure applied to data recorded in the cVEP–based
BCI experiments with eight users are discussed in the following section.

3

Results

Results of the conducted cVEP–based BCI paradigm experiments are summarized in form of accuracies as bar plots in Figure 2 and confusion matrices in
Figures 3 and 4, respectively. The accuracies were calculated in experiments
using the proposed green–blue high frequency, the green–blue conventional low
frequency, the white–black high frequency, and the low frequency white–black

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Fig. 3. The results of the linear CCA–based classiﬁcation from the eight users depicted
as confusion matrices from all the settings tested within the project.

ﬂashing settings, respectively. The theoretical chance level of all experiments wasof 25%.
To investigate command accuracy eﬀects of cVEPs (discriminability) of CCA
versus the proposed linear SVM, we applied pairwise Wilcoxon–test for a statistical analysis of median diﬀerence signiﬁcances, because all the accuracy results
were not normally distributed. The results of the classiﬁers’ comparisons arepresented in Table 2.
Next, a test was applied for a pairwise comparison of CCA versus linear SVM
classiﬁcation BCI accuracies for each frequency and color setting separately. The
results presented in Figure 2 show individual bar plots, of which several haveresulted with statistically signiﬁcant diﬀerences (denoted with “∗”). Moreover,
the averaged accuracies of all subjects resulted all with statistically signiﬁcantClassiﬁcation Accuracy Improvement of cVEP-Based BCI

239

Fig. 4. The results of the linear SVM–based classiﬁcation accuracy from the eight
BCI experiments depicted as confusion matrices from all the settings tested within the
project.

diﬀerences and the proposed linear SVM–based classiﬁcation scored higher comparing to the CCA.
The accuracies for each command separately have been calculated anddepicted in form of confusion matrices visualized in Figures 3 and 4, for CCA
and linear SVM classiﬁers respectively. The results presented in the above mentioned ﬁgures have shown, that the biased classiﬁcation result problem previously
observed in the CCA case (command one scored relatively higher in the previous study) [1] was solved by the linear SVM application. Additionally, pairwise
Wilcoxon–tests have been conducted for all users separately to evaluate diﬀerences of CCA versus linear SVM classiﬁer accuracies in the four experimental
settings. The resulting p−values are arranged in Table 3. There were signiﬁcant

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Table 2. The cVEP classiﬁcation accuracy comparison of CCA versus linear SVM
with pairwise Wilcoxon–test. The results of the BCI accuracy comparison with “∗”symbols denote statistically signiﬁcant median diﬀerences at a level of p < 0.05
Command green–blue white–black green–blue white–black
number
high
high
low
low
#1
#2
#3
#4

0.664
0.327
0.067
0.015∗

0.122
0.005∗
0.009∗
0.385

0.940
0.326
0.242
0.036∗

0.870
0.115
0.032∗
0.040∗

Table 3. The cVEP classiﬁcation pairwise Wilcoxon–test’s p–value results of the BCI
accuracy comparisons between linear SVM and CCA classiﬁers with “∗” symbols denoting medians’ diﬀerence statistical signiﬁcances at the level of p < 0.05
User green–blue white–black green–blue white–black
number
high
high
low
low
#1
#2
#3
#4
#5
#6
#7
#8

0.028∗
0.122
0.245
0.000∗
0.154
0.105
0.082
0.743

0.730
0.167
0.194
0.001∗
0.019∗
0.006∗
0.060
0.702

0.093
0.001∗
0.522
0.001∗
0.014∗
0.037∗
0.036∗
0.374

0.200
0.983
0.034∗
0.026∗
0.066
0.006∗
0.169
0.853

pairwise diﬀerences observed in which the proposed linear SVM–based cVEP
BCI classiﬁcation accuracies scored also higher comparing to the CCA.

4

Conclusions

The proposed LED ﬂashing and cVEP response–based BCI paradigm with thechromatic green–blue stimuli has been discussed in this paper. We tested linear SVM classiﬁcation–based method in comparison to the classical already
CCA case. The majority of the obtained linear SVM accuracy results scoredhigher comparing to CCA outcomes. We also resolved biased accuracies problem observed previously by our group in case of the CCA. The linear SVM
classiﬁcation cVEP BCI accuracies scored very good for all commands comparing to the CCA. The conducted experiments to verify the feasibility of the
proposed method conﬁrmed successfully our research hypothesis based on the
results obtained from the eight healthy users. All of the cVEP–based BCI accuracies resulted well above the theoretical chance levels and there were also 100%outcomes observed.

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241

For the future research, we plan to investigate upper limits of the stimulus
frequency and an optimization of the m − sequences in order to create the evenbetter code–modulated visual BCI.
Acknowledgments. We would like to thank Dr. Andrzej Cichocki from RIKEN Brain
Science Institute, Japan, and Dr. Koichi Mori from Research Institute of National Rehabilitation Center for Persons with Disabilities, Japan, for very stimulating discussions
about chromatic SSVEP–based BCI which stimulated this project. This research was
supported in part by the Strategic Information and Communications R&D Promotion
Program (SCOPE) no. 121803027 of The Ministry of Internal Aﬀairs and Communication in Japan, and by KAKENHI, the Japan Society for the Promotion of Science,
grant no. 24243062.

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