10th AEARU Workshop on Computer Science and Web Technology
February 25-27, 2015, University of Tsukuba, Japan
PosterMulti-command Tactile Brain-computer Interface
Using the Touch-sense Glove
1,1

1

1,2,▽

Hiroki Yajima *, Shoji Makino , and Tomasz M. Rutkowski

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 The project reported in this abstract aims
to confirm whether a tactile stimulator ”touch–sense glove” is effective for a novel brain-computerinterface (BCI) and whether it would improve BCI
classification accuracies. We also report that tactile
stimuli delivered to fingers could be utilized toevoke event related potential (ERP) responses withan attention modulation. The designed new
stimulator device is presented in detail together
with psychophysical and EEG BCI experiment
protocols. Results supporting the proposed ”touch–sense glove” device are presented in form of online
BCI classification accuracies.
Keywords Brain-computer interface (BCI); tactile
BCI; event related potential (ERP); EEG; P300.
I.

INTRODUCTION

Brain computer interface (BCI) technology shallallow disable people, e.g. the amyotrophic lateralsclerosis (ALS) users, to operate devices without any
muscle activity involved. The most popular BCI
modality is the visual one, however, it prevents users
from paying attention to the surrounding environment
making often operation an application impossible. SuchBCIs are also not available for users suffering from lostor bad vision [1,2,3].
Our research project proposes to use a tactile BCI(tBCI) modality. This modality shall derive P300
responses, which are usually obtained by attending to aspecific target. P300 response is a part of an event
related potential (ERPs) [4].
Recently BCI, which utilizes ERP, has been
actively researched [1,2,3,4]. So far, the most populartBCIs use fingertip stimulation to evoke P300
responses [3]. However, this modality accuracy is still
low, thus, we propose to expand conventional
stimulation to the whole finger surfaces. Our researchaims to improve tBCI classification accuracy and todevelop the novel and practical stimulation device.

II.

METHODS

We propose to utilize the P300 responses in thetBCI paradigm and a classification method based on aclassical oddball paradigm [4]. The P300-based BCI
discriminates the attended stimulus (the target) andignored stimulus (non-target) from the differences in
ERP potentials. We propose a new stimulator as type ofa glove named ”touch-sense glove”. The 12 vibrotactile
exciters are attached to user fingers. Each finger has
one to three attached vibrotactile exciters. Signle
exciter is attached on the thumb; two on the littlefinger; three on the forefinger, the middle and the ringfingers. In psychophysical and EEG experiments, there
are five stimulus patterns. The reason why thevibrotactile excites are attached to a glove is to enhancea convenience of an experimental setup avoiding
manual attachment of the 12 devices separately eachtime.
III.

EEG EXPERIMENT CONDITIONS AND RESULTS

In the psychophysical experiments, user button press
responses were recorded. Based on the recorded
responses, we analyzed the correct answer rates andthe response delay times. We confirmed the users
could identify stimulus patterns. In order to evaluate
the P300 response occurrences and the further possible
online BCI good classification accuracies, weconducted a series of EEG experiments with the sameusers as in the previous psychophysical experiments.
The EEG experiments did not require the users to
respond behaviorally by pressing a button, but only
mental responses were instructed. The user’s
brainwaves were captured using wet active EEG
electrodes. Then, the captured and filtered brain
signals were segmented and classified after a trainingof the step-wise linear discriminant analysis (SWLDA)

classifier [5]. The EEG recording detailed conditions
have been summarized in Table I. The brainwaves
have been depicted in form of grand mean averaged(all users and sessions) ERPs in Figure 1. Online tBCI
experiments results have been also summarized inform user achieved accuracies (a chance level was of20%) and information transfer rates (ITR). The
accuracy and ITR results are presented in the Table II.
Table I. The EEG experiment conditions
Condition
Number of users
Inter-stimulus-interval
Stimulus duration
Number of stimuli
EEG electrode positions
High-pass; low-pass; notch

Detail5500 ms
100 ms5 (thumb; forefinger;
middle finger; ringfinger: little finger
Cz, CPz, P3, P4, C3, C4,CP5, and CP6
0.1 ; 60 ; 48 ∼ 52 (Hz)

Table II. BCI accuracies and ITR scores
User number

The best BCI accuracy

The best ITR

12
3
4
5

100%
100%
100%
100%
60%

4.46
4.46
4.46
4.46
1.10Figure 1. Grand mean averaged brainwave ERPs resultsof the two channels with blue lines depicting ignored
non-targets and purple the intended targets with P300s.

IV.

CONCLUSIONSIn the series of psychophysical and EEG experiments
we confirmed that the users could distinguish five
vibrotactile stimulus patterns delivered to all thefingers by the touch–sense glove. We could also
observe clear and possible to discriminate brainwave
P300 responses. The obtained results have shown thatthe averaged classification accuracies resulted above
the chance level scores of 20%. The EEG experiments
resulted with the easily discriminable P300 responses
leading to the classification accuracies and ITR scores
above the chance levels, or even with perfect scoreswithin the limitations of the experiential settings. We
consider continuing our research in the near future toconduct experiments with shorter inter-stimulusintervals and with single trial-based classification
sequences in order to bring closer the proposed
paradigm to the real users in need.
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