10th AEARU Workshop on Computer Science and Web Technology
February 25-27, 2015, University of Tsukuba, Japan
PosterSSVEP Brain-computer Interface using Green and Blue Lights
Daiki Aminaka 1,*, Shoji Makino1, and Tomasz M. Rutkowski1,2,▽
1
Department of Computer Science and Life Science Center of TARA, University of Tsukuba, Japane-mail: tomek@bci-lab.info (▽corresponding author)
2
RIKEN Brain Science Institute, Japan
Abstract A novel approach to steady-state visualevoked potentials (SSVEP) based brain-computerinterface (BCI) is reported in this abstract. To minimize possible side effects of the monochromaticlight SSVEP we propose to utilize color information
in form of the green and blue interlaced flicker.
The feasibility of the proposed method is evaluatedin a comparison of the classical monochromatic
versus the proposed colorful SSVEP responses.
Keywords Brain-computer interface; SSVEP.
I.

INTRODUCTION

The brain-computer interface (BCI) is a technologythat utilizes human neurophysiological signals for a
direct communication of a brain with external devices
without any muscle activities [1]. Particularly, in thecase of users suffering from locked-in-syndrome(LIS) [2], such technology could allow them to communicate. In this abstract, we propose a novel BCI
paradigm using the colorful SSVEP. We compare ourproposal with the classical monochromatic method [1].
SSVEP is a natural response for visual stimulations
with specific frequencies. This response’s feature canbe used for detecting at which stimulator the user gazes. While SSVEPs have been known for many years
already [1] the SSVEP-BCIs have been not popular
due to several limitations.
The major reason why SSVEP-BCI has not beenused is a danger of generation of an epileptic seizure
called the photosensitive epilepsy. Moreover, the light
flashing with lower frequencies might cause user’s
exhaustion when used for longer time periods.
Based on the above limitations of the conventional
SSVEP devices, we propose a safer option for BCI
using the green and blue colorful light combinationsdelivered by light-emitting diodes (LEDs).

II.

MATERIALS AND METHODSThe experiments reported in this abstract were performed in the Life Science Center of TARA, University of Tsukuba, Japan. The experimental procedures
were approved and designed in agreement with theethical committee guidelines of the Faculty of Engineering, Information and Systems at University ofTsukuba, Tsukuba, Japan (experimental permission no.
2013R7). The visual stimuli were flashed using small
white or RGB LEDs as square waves generated byARDUINO UNO microcontroller board. In this study
we used 7, 18, 25, 39 Hz flashing frequency patterns tocreate four commands SSVEP-BCI paradigm. Duringexperiments the LEDs continued to flash simultaneously with the above frequencies. In the training phase,to let the user to gaze at specific LED, the spoken experiment instructions were delivered from OpenViBEsoftware environment. The experiment settings havebeen summarized in Table I. The EEG electrodes wereplaced on the user’s head close to the visual cortex [1].
Spoken instructions were given to let the user to execute saccades and to focus at the target LEDs. The
spoken instructions were delivered as a recorded human voice (for Japanese speakers) or a synthesized
MacOS X system speech (Serena’s voice for Englishspeakers). In a single BCI EEG experimental run there
were 40 trials to gaze at each LED (ten trials for eachtarget frequency).
For SSVEP responses classification a linear softmargin support vector machine (C-SVM) [3] was chosen. The features were calculated as power spectral
densities based on Welch’s method [4]. The featurevector used for linear C-SVM training was constructedby 10✕96 matrix for each target frequency. A test data
used for classification was constructed as a concatenated of all EG channels 1✕96 feature vector in eachsingle trial.

Table 1. EEG experiment details
Number of users
Recording length
EEG recording system
Electrode locations
Reference and groundelectrode
Notch filter
High-pass filterNumber of runs

III.

35000 ms for each trial
g.USBamp with active wet EEGelectrodes system
O1, O2, Po3, Po4, P1, P2, Oz andPoz
Left earlobe and FPz
Butterworth 4th order with rejection band of 48 ~ 52 Hz
Butterworth 8th order at 5 Hz
4 for each LED display colors

RESULTSThe results obtained from offline BCI EEG classification accuracy improvement with the soft margin
support vector machine [4] together with questionnaire
given to the users’ analysis are presented in this section. Figure 1 summarizes the SSVEP-BCI experimentwith results calculated by a multi-class linear C-SVM
classifier. The mean classification accuracies based on
various time windows are depicted in a form of boxplots with pairwise t-test statistical significances(p < 0.05) marked with “*” symbols. The results haveshown that the longer the recorded time the higher accuracy except for the user #2. The accuracies of users#1 and #3 formed monotonic increase for each stimulus’ color type. Moreover the proposed method scoredwith higher accuracies comparing to the monochromatic SSVEP. Results of user #1 showed also statistically significant differences (conventional vs. proposed) in the time window ranges of 2 to 5 seconds.

On the other hand, results of the user #3 did not contain significant differences except for the result of one
second long interval. Results of questionnaires given
have been summarized in Table II. There were twoquestions as follows: (1) Which stimulus did you prefer monochromatic versus colorful (response range -5~ +5)? (2) Please evaluate experiment difficulty fromtiring to relaxing (response range -5 ~ +5). When thepreference response was positive, the user preferredcolorful flashing comparing to the monochromatic.
Similarly, the difficulty positive scores reflected theusers’ lower tiredness with the colorful LEDs. Except
for preference report of the user #3, all the others reported that the proposed method was better from thepoint of view of preference and difficulty.
Table 2. Questionnaire results
User number
Preference
Tiredness
#1
3
1
#2
5
3
#3
-1
4
Average
2.33
2.67
IV.

CONCLUSIONSThis paper reported a successful implementation of thefour command-based colorful SSVEP BCI in offline
signal analysis scenario. We conducted experiments toverify the feasibility and user experience of the proposed method. According to the results obtained fromthree users, classification accuracies were as good oreven better as of the conventional monochromaticstimuli. In addition to these results, the majority of theusers preferred the proposed green-blue LED flashing.
For future research, we aim to validate the prototype
colorful SSVEP-based BCI with more users to further
proof the results.
REFERENCES

Figure 1. SSVEP EEG classification results of every user invarious signal analysis time windows. The horizontal axis
represents each time window. The cyan doted horizontal
line depicts the chance level of 25%.

[1] J. Walpaw and E. W. Wolpaw, Eds, BrainComputer 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] C. Cortes and V. Vapink, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273297, Sep. 1995
[4] P. D. Welch, “The use of fast Fourier transform
for the estimation of power spectra: a method
based on time averaging over short, modified periodograms,” IEEE Transactions on Audio andElectroacoustics, vol. 15, no. 2, pp. 70-73, 1967.