Chromatic and High–frequency cVEP–based BCI Paradigm
Daiki Aminaka1 , Shoji Makino1 , and Tomasz M. Rutkowski1,2,3,∗

Abstract— We present results of an approach to a code–
modulated visual evoked potential (cVEP) based brain–computer interface (BCI) paradigm using four high–frequency
flashing stimuli. To generate higher frequency stimulation compared to the state–of–the–art cVEP–based BCIs, we propose
to use the light–emitting diodes (LEDs) driven from a small
micro–controller board hardware generator designed by our
team. The high–frequency and green–blue chromatic flashingstimuli are used in the study in order to minimize a danger of aphotosensitive epilepsy (PSE). We compare the the green–bluechromatic cVEP–based BCI accuracies with the conventional
white–black flicker based interface. The high–frequency cVEP
responses are identified using a canonical correlation analysis(CCA) method.

I. INTRODUCTION
A brain computer interface (BCI) is a technology thatutilizes human neurophysiological signals for a direct
brainwave–based communication with an external environment, and without depending on any muscle or peripheral
nervous system activities [1]. Particularly, in the case ofpatients suffering from locked–in–syndrome (LIS) [2], suchtechnology could help them to communicate or complete
various daily tasks (type messages on a virtual keyboard
or control their environment using a computer, etc). This
technology shall create a feasible option for amyotrophic
lateral sclerosis (ALS) or coma patients to communicate with
their families, friends or caretakers by using their brainwaves
only [1].
In this paper, we report on the visual BCI paradigm, which
has been inspired by the three seminal papers in this field,
namely a code–modulated visual evoked potential (cVEP)
response–based BCI reported in [3]; a steady–state visualevoked potential (SSVEP) classification with a canonicalcorrelation analysis (CCA) [4]; and a green–blue flashing–based SSVEP BCI [5]. The chromatic green–blue cVEP
stimulus brings a new option of a lower danger of the aphotosensitive epilepsy (PSE) [6], [7] seizures comparing to
the classical SSVEP based BCI paradigms [5], [8], [9]. The
flashing frequencies in a range of 15 ∼ 25 Hz are the most*This research was supported in part by the Strategic Information andCommunications 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.
1 Daiki Aminaka, Shoji Makino and Tomasz M. Rutkowski are withLife Science Center of TARA and Department of Computer Science, University of Tsukuba, 1-1-1 Tennodai Tsukuba Ibaraki, Japan.

tomek@bci-lab.info

http://bci-lab.info/

2 Tomasz M. Rutkowski is also with RIKEN Brain Science Institute,Wako-shi, Japan.
3 Tomasz M. Rutkowski is the corresponding author.
∗ Tomasz M. Rutkowski was supported in part by YAMAHA Corporation.

978-1-4244-9270-1/15/$31.00 ©2015 IEEE

provocative for the PSE (the frequencies of 5 ∼ 65 Hz are
still dangerous) [6].
We also propose to employ the light–emitting diodes(LEDs) for the code modulated visual evoked potential(cVEP) generation with an application of higher flashingfrequencies (40 Hz), comparing to the classical computer
display (with limited refreshing rates allowing for at maximum 30 Hz stimulus generation) approaches [1], [8], [10].
The higher flashing frequencies allow for an increase of
an information transfer rates (ITR), of which low scores
were a limitation of the state–of-the–art cVEP–based BCI
paradigm [3]. The cVEP is a natural response for the visual
stimulus with specific code–modulated sequences [3], [10].
It is generated by the brain when the user gazes at lightsource which flashes the specific code–modulated sequence.
The cVEP–based BCI belongs to the stimulus–driven BCIs,
which do not required longer training comparing to theimagery–driven paradigms [1]. In order to further confirm
results from the recent studies on the chromatic (green–blue) flashing–based paradigms [5], [9], we compare in thispaper the proposed BCI paradigm with a classical white–black cVEP modality.
From now on the paper is organized as follows. In thenext section we describe materials and methods used in thisstudy. Discussion of the obtained results follows and conclusions, together with future research directions, summarize thepaper.
II. MATERIALS AND METHODSThe 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 andthe research targets of the cVEP–based BCI paradigm wereexplained in detail to the eight users, who agreed voluntarily
to participated in the study. The electroencephalogram (EEG)cVEP–based BCI experiments were conducted in accordancewith The World Medical Association Declaration of Helsinki- Ethical Principles for Medical Research Involving Human
Subjects. The experimental procedures were approved anddesigned in agreement with the ethical committee guidelinesof the Faculty of Engineering, Information and Systems
at University of Tsukuba, Tsukuba, Japan (experimental
permission no. 2013R7). The average age of the users wasof 26.9 years old (standard deviation of 7.3 years old; sevenmales and one female).
The visual stimuli were flashed via the RGB LEDs as
square waves generated by the ARDUINO UNO micro–
controller board as shown in Figure 1. The generator program
was written by our team using C–language. In this study

1906

Fig. 1. A diagram explaining a difference in flashing stimuli between
the conventional monochromatic (white–black) and the proposed chromatic(green–blue) flickering patterns created with square cVEP–modulated waves
generated by the ARDUINO UNO micro–controller board in the presented
study.

we used m − sequence encoded flashing patterns [3] tocreate four commands of the cVEP–based BCI paradigm.
The m − sequence is a binary pseudorandom sequence,
which could be generated using the follwoing equation,x(n) = x(n − p) ⊕ x(n − q),(p > q)(1)thwhere x(n) is the n element of the m−sequence obtainedby the exclusive–or (XOR) operation, denoted by ⊕, using
the two preceding elements indicated by p and q. In thisproject p = 5 and q = 2 were chosen. An initial binary
sequence was chosen, to create the full m − sequence usedin 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) and depicted asa vector s1 in Figure 2.
The interesting m − sequence feature, which is very
useful for the cVEP–based BCI paradigm design, is anunique autocorrelation function. The autocorrelation function
has only a single peak at the period sample value. If them − sequence period is N , the autocorrelation function
will result with values equal to 1 at 0, N, 2N, . . . and 1/Notherwise. It is also possible to introduce a circular shiftof the m − sequence denoted by τ , to create a set ofm − sequences with also shifted autocorrelation functions,
respectively. In this study, the shifted time length has been
defined as τ = 7 bits. Three additional sequences has been
generated using shifting by τ , 2 · τ and 3 · τ , respectively, as
shown also in Figure 2. 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 have been tested toinvestigate whether they would affect the cVEP response?
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 been tested. The two monochromatic (white–black) and chromatic (green–blue) LEDs for the proposed
cVEP–based BCI settings used in the experiments reported inFig. 2. A diagram illustrating a procedure to construct four m − sequences
denoted by si , (i = 1, 2, 3, 4). Each sequence is shifted by τ = 7 bits and it
has a period T . The traces denoted by ȳj , (j = 1, 2, 3, 4) are the averaged
EEG cVEP responses.

this paper are presented in Figure 3. During the cVEP–based
BCI EEG experiments the users were seated on a comfortablechair in front of the LEDs (see Figure 3). The distance
between user’s eyes and LEDs was about 30 ∼ 50 cm(chosen by the users for a comfortable view of all LEDs).
An ambient light was moderate as in a typical office. The
EEG signals were captured with a portable EEG amplifiersystem g.USBamp from g.tec Medical Engineering, Austria.
Eight active wet (gel–based) g.LADYbird EEG electrodes
were connected to the head locations as in an extended
10/10 international system [1]. These positions were decided
due to the visual cortex responses targeting experiment [1].
Details of the EEG experimental set up are summarized inTable I. The sampling frequency was set to 512 Hz and anotch 4th order Butterworth IIR filter at rejection band of48 ∼ 52 Hz was applied to remove power line interferenceof 50 Hz. Moreover, the 8th order Butterworth IIR band–
pass filter at pass band of 5 ∼ 100 Hz was applied to1907

TABLE IEEG 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

EEG electrode locations

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

Reference and ground

Left earlobe and FPz

Notch filter

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

Band–pass filter

Butterworth 8th order with passband of 5 ∼ 100 Hz

LED positions

45.5 × 41.5 cm rectangle edge centersclassification steps were as follows:
1) Capturing the EEG cVEPs yi , (i = 1, 2, 3, 4) obtainedin response to time shifted m − sequences si (seeFigure 2 for stimulus creation details).
2) Averaging the captured j cVEPs as yi,j (t) for eachtarget i separately. The averaged responses ȳi were
defined as the reference data. In this study, the numberof cVEPs was N = 60. The averaging procedure was
as follows,N
1 X
yi,j .
(3)
ȳi =
N j=1
3) For the test purposes, M, (M  N ), cVEPs recorded
in separate sessions were used for the method evaluation. In this study M = 5. The averaged test datasets
were calculated as follows,

Fig. 3. The visual stimulation device used in the presented study. The top,bottom, right and left LEDs’ flickering patterns correspond to si , (i =1, 2, 3, 4) m − sequences, respectively (see the m − sequence shifted
patterns Figure 2). The LEDs were arranged at edge centers of the 45.5 ×
41.5 cm rectangular frame as shown in the above photograph.

remove eye blinks and high frequency muscle–originating
noise. The OpenViBE [11] bio–signal data acquisition andprocessing environment, together with in–house programmedin Python extensions, were applied to realize the online
cVEP–based BCI paradigm. To avoid user’s eye blinks, eachtrial to gaze at a single LED was separated with pauses
during the experimental sessions (sessions length details
in Table I). All the participating users were instructed not
to move their neck when changing their eye–gaze among
the four LEDs to reduce electromyographic noise. No eye–movement interference affected the proposed cVEP BCI.
In the data acquisition phase, first the users gazed at top
flashing LED in order to collect classifier training dataset, as
instructed verbally by the experimenter conducting the study.
Twenty m − sequence cycles were repeated in a single EEG
capturing session. In short, sixty cVEPs to m − sequencebased flashing were collected for each direction in a single
experimental trial. The triggers indicating the onsets of them − sequences were sent to g.USBamp directly from the
ARDUINO UNO micro–controlled to mark the beginning ofeach cVEP response. Finally, four experiment types wereconducted for each user: the conventional low frequency; theproposed high frequency; and in each above setting in thetwo color modes using white–black and green–blue flashingLEDs.
A canonical correlation analysis (CCA) [4] method wasused in this study to classify the cVEP responses based on
the user’s intentional choices (gazes). In the training session asingle dataset containing the cVEP responses to top flashing
LED was used. The remaining three cVEP responses wereconstructed by shifting the top LED response by τ , 2 · τ
and 3 · τ (see Figure 2 for stimulus design explanation). We
applied an analysis of multichannel correlation coefficientsamong the reference m−sequences and captured cVEPs [3].
We used the CCA method to identify the gazed by theuser flickering patterns. The cVEP response processing andz̄k,l =

1
M

l+M
X−1

zk,m ,(4)m=lwhere k = 1, 2, 3, 4 represented the stimulus number
and l = 1, 2, . . . , N − M + 1 was the test dataset
number.
4) In order to identify (classify) the attended stimulus the
CCA was calculated for test z̄k,l versus the referenceȳi datasets as,aT Rz̄k,l ȳi b,
ρi = qpaT Rz̄k,l z̄k,l a bT Rȳi ȳi b(5)where the Rz̄k,l ȳi , Rz̄k,l z̄kl , Rȳi ȳi were the respective covariance matrices; a and b were the canonicalcorrelation vectors. The maximum value of correlation
coefficient identified by an index c was used to classify
the attended stimuli, which pointed the correlated
reference as,ρc = max{ρ1 , ρ2 , ρ3 , ρ4 }.

(6)

The results of the above procedure applied to data recordedin the cVEP–based BCI experiments with eight subjects are
discussed in the following section.
III. RESULTS
Results of the conducted cVEP–based BCI paradigm experiments are summarized in form of accuracies as bar plots
with standard errors is in Figure 4. The accuracies were calculated in experiments using the proposed green–blue highfrequency, the green–blue conventional low frequency, thewhite–black high frequency, and the low frequency white–black flashing settings, respectively. The theoretical chancelevel of all experiments was of 25%. To investigate flickering
frequency effect on cVEP classification (discriminability) accuracies, we applied pairwise Wilcoxon–test for a statisticalanalysis of median difference significances, because all theaccuracy results were not normally distributed. The test wasapplied for pairs of green–blue flashing in high (40 Hz)
versus low (30 Hz) frequencies and similarly for white–black
setting accuracies, respectively. The results of the statistical1908

Fig. 4. The results of CCA–based classification from the eight users participating in the study presented in form of bar plots with standard errors. 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); and white–black lower frequency cases (yellow), respectively. The theoretical chance level of the experiments was of 25%.

analyses resulted with significant pgb < 0.029 for green–
blue setting, which supported the project initial hypothesisof the chromatic cVEP modality feasibility. The white–black
setting resulted with non–significant differences at a level of
pwb < 0.143. A comparison for significance of frequencies
across to color settings (chromatic versus monochromatic)
resulted with non–significant differences for high frequencies
with phigh > 0.05. The low frequencies with significantly
different at a level of plow < 0.034.
IV. CONCLUSIONSThe proposed LED flashing and cVEP response–based
BCI paradigm with the chromatic green–blue stimuli has
been discussed in this paper. We tested positively, for the
online BCI feasibility, the higher frequency cVEP and CCAclassification–based method in comparison to the classicallow frequency cVEP stimuli. Also the the chromatic green–
blue light effect resulted with encouraging results.
The conducted experiments to verify the feasibility ofthe proposed method confirmed successfully our research
hypothesis based on the results obtained from healthy eightusers. All of the cVEP–based BCI accuracies scored above
the theoretical chance levels and there were also 100%
accuracies reported.
For the future research, we plan to investigate upper limitsof the stimulus frequency and an optimization of the m −sequences in order to create the even better visual BCI.
ACKNOWLEDGMENTS
We would like to thank Dr. Andrzej Cichocki from RIKEN
Brain Science Institute, Japan, and Dr. Koich Mori from
Research Institute of National Rehabilitation Center for Persons with Disabilities, Japan, for very stimulating discussions

about chromatic SSVEP–based BCI which stimulated thisproject.
R EFERENCES[1] J. Wolpaw and E. W. Wolpaw, Eds., Brain-Computer Interfaces:
Principles and Practice. Oxford University Press, 2012.
[2] F. Plum and J. B. Posner, The Diagnosis of Stupor and Coma.
Philadelphia, PA, USA: FA Davis, 1966.
[3] G. Bin, X. Gao, Y. Wang, Y. Li, B. Hong, and S. Gao, “A high-speed
bci based on code modulation vep,” Journal of Neural Engineering,
vol. 8, no. 2, p. 025015, 2011.
[4] G. Bin, X. Gao, Z. Yan, B. Hong, and S. Gao, “An online multi-channel
SSVEP-based brain–computer interface using a canonical correlation
analysis method,” Journal of Neural Engineering, vol. 6, no. 4, p.
046002, 2009.
[5] T. Sakurada, T. Kawase, T. Komatsu, and K. Kansaku, “Use of highfrequency visual stimuli above the critical flicker frequency in a
SSVEP-based BMI,” Clinical Neurophysiology, p. (online first), 2014.
[6] R. S. Fisher, G. Harding, G. Erba, G. L. Barkley, and A. Wilkins,“Photic-and pattern-induced seizures: A review for the epilepsy foundation of america working group,” Epilepsia, vol. 46, no. 9, pp. 1426–
1441, 2005.
[7] J. Parra, F. H. Lopes da Silva, H. Stroink, and S. Kalitzin, “Is colour
modulation an independent factor in human visual photosensitivity?”
Brain, vol. 130, no. 6, pp. 1679–1689, 2007.
[8] H. Bakardjian, T. Tanaka, and A. Cichocki, “Optimization of SSVEP
brain responses with application to eight-command brain–computerinterface,” Neuroscience Letters, vol. 469, no. 1, pp. 34 – 38, 2010.
[9] D. Aminaka, S. Makino, and T. M. Rutkowski, “Chromatic ssvep bci
paradigm targeting the higher frequency eeg responses,” in Asia-PacificSignal and Information Processing Association, 2014 Annual Summit
and Conference (APSIPA), Dec 2014, pp. 1–7.
[10] G. Bin, X. Gao, Y. Wang, B. Hong, and S. Gao, “VEP-based braincomputer interfaces: time, frequency, and code modulations [research
frontier],” Computational Intelligence Magazine, IEEE, vol. 4, no. 4,
pp. 22–26, 2009.
[11] Y. Renard, F. Lotte, G. Gibert, M. Congedo, E. Maby, V. Delannoy,
O. Bertrand, and A. Lécuyer, “Openvibe: an open-source software
platform to design, test, and use brain-computer interfaces in realand virtual environments,” Presence: teleoperators and virtual environments, vol. 19, no. 1, pp. 35–53, 2010.

1909