10th AEARU Workshop on Computer Science and Web Technology
February 25-27, 2015, University of Tsukuba, Japan
PosterTactile Pin-pressure Brain-computer Interface
Kensuke Shimizu1,*, Hiromu Mori2, Shoji Makino1,2, and Tomasz M. Rutkowski1,2,3
1College of Information Science, University of Tsukuba, Tsukuba, Japan
2
Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Japan
3
RIKEN Brain Science Institute, Wako-shi, Japane-mail: {kensuke, tomek}@bci-lab.info

Abstract The abstract presents a tactile pinpressure stimulus-based brain-computer interface(BCI) paradigm. The 3x3 pressure pin matrix
stimulus patterns are presented to the users in an
oddball paradigm allowing for brainwave “aharesponses” generation to attended targets. Our
research hypothesis is confirmed with the resultswith five users performing online BCI experiments.

obtained with five healthy users tested in online BCI
experiments.
II.

MATERIALS AND METHODS

Tactile stimulus was generated via the tactile pinpressure generator composed of nine solenoids
arranged in the 3 × 3 matrix as depicted in Figure 1.

Keywords Brain-computer interaction; BCI; P300;
sEEG; brain signal processing.
I.

INTRODUCTION

A brain-computer interface (BCI) is a technologythat uses neurophysiological signals (brainwaves) ofusers to allow communication with others or a controlof external devices without any muscle movements [1].
The majority of BCI applications are based on a visualmodality. However, disabled people with impaired
vision need other types of the BCI for communication.
The tactile BCI seems to offer the better
communication options in comparison with visual andother modalities in case of locked–in–syndrome (LIS)
patients [2]. The paradigm proposed in this abstract isa BCI using tactile pin-pressure stimulus generated bysolenoids, which we refer to in brief as tactile pinpressure BCI (tpBCI). The presented approach allows
for faster and more precise delivery of tactile pinpressure stimuli comparing to the previously proposed
vibrotactile stimulator-based approaches, and it is not
limited to finger tips only [3]. The goal of this study isto evaluate the performance of the novel tpBCI
paradigm proposed and developed by our team. We
present the concept of the novel tpBCI and results

Figure 1. The tactile pin-pressure generator put on theuser’s dominant hand covering index, middle and ringfingers.

There were six linear patterns of tactile pinpressure stimuli delivered in random order to the userfingers. Three of them were horizontal lines orderedfrom the top to bottom of user’s fingers respectively.
The remaining patterns were the vertical lines in left toright position order. The solenoids generated pinpressures 100 ms long each time. The psychophysicalexperiments were conducted to investigate theinfluence of tactile pin-pressure stimulus on the user
behavioral response time and accuracy. The behavioral
responses were collected using a trigger button on thekeyboard and a MAX 6 [4] program.

III.

RESULTS

Table 1. Online BCI experiment SWLDA accuracy
results and ITR of three experimental sessions withaverages (avg)
Accuracy [%]ITR [bit/min]

#1
50
0.84

#2
38.9
0.40

#3
55.6
1.13

#4
5.6
0.16

1006 0.0%

2.0%

2.0%

0.0%

2.0%

94.1%

0.0%

5 0.0%

0.0%

0.0%

2.0%

98.0%

0.0%

0.0%

9080
70

4 2.0%

0.0%

0.0%

96.0%

2.0%

0.0%

0.0%

3 2.1%

2.1%

93.8%

2.1%

0.0%

0.0%

0.0%

2 0.0%

100.0% 0.0%

0.0%

0.0%

0.0%

0.0%

6050403020

1 96.2%

0.0%

0.0%

2310.0%

1.9%

1.9%

0.0%

45
subject response

6N

100Figure 2. Tactile pin-pressure psychophysical experimentresults in form of the confusion matrix of the grand mean
averaged user accuracy results.
IV.

CONCLUSIONS

According to the results obtained from the online
EEG BCI experiments, a single user could score oncewith perfect accuracy and above 90% on average.
Encouraging results support the initial research
hypothesis of tactile pin-pressure–based stimulus
validity for the BCI paradigms.
REFERENCES

The psychophysical experiment accuracy results
are depicted in form of a confusion matrix in Figure 2.
This result confirmed the stimulus similarity since thebehavioral responses for all the patterns were basicallythe same. This finding validated the design of thefollowing tpBCI EEG experiment.
The online tpBCI accuracies (see Table 1), the fourout of five users scored well above the chance level of16%, which is a good outcome of the proposed
prototype. Based on the obtained accuracies wecalculated, to allow simply comparison of theproposed tpBCI paradigm with other publishedapproaches, the ITR scores that were in the range from
0.16 bit/min to 3.83 bit/min.

User

Confusion matrix of the psychophysical responses

instructed target

A single trial was composed of six tactile pinpressure patterns delivered to the user fingers in
randomized order with an inter-stimulus-interval (ISI)of 900 ms. One session was composed of ten trials foreach tactile pin-pressure target. We conducted asession with each user.
In the BCI experiments EEG signals were captured
with a portable EEG amplifier system g.USBamp byg.tec Medical Instruments, Austria. Eight active wet
EEG electrodes were used to capture brainwaves.
Those were attached to the head locations Cz, Cpz, P3,P4, C3, C4, CP5, and CP6. A reference electrode wasattached to a left earlobe and a ground electrode on theforehead at FPz position respectively. The users put onpolyethylene gloves to limit any electric interference.
The users were also requested to limit their eye-blinksand body movements to avoid electromagnetic andelectromyography interference. The EEG signals wererecorded and preprocessed by an in-house enhanced
BCI2000-based application [5], using a stepwise linear
discriminant analysis (SWLDA) classifier [6] withfeatures drawn from ERP intervals of 0∼800 ms. The
sampling rate was set to 256 Hz, the high pass filter at
0.1 Hz, and the low pass filter at 40 Hz. The ISI was
400 ms and each stimulus’ duration was of 100 ms.

#590.6
3.83

avg
48.1
0.76

[1] J. Wolpaw and E. W. Wolpaw, Eds., BrainComputer Interfaces: Principles and Practice.
Oxford University Press, 2012.
[2] T. Kaufmann, E. M. Holz, and A. Kuebler,“Comparison of tactile, auditory and visualmodality for brain-computer interface use: A casestudy with a patient in the locked-in state,”
Frontiers in Neuroscience, vol. 7, no. 129, 2013.
[3] M. van der Waal, M. Severens, J. Geuze, andP. Desain, “Introducing the tactile speller: an ERPbased
brain–computerinterfaceforcommunication,” Journal of Neural Engineering,
vol. 9, no. 4, p. 045002, 2012.
[4] http://cycling74.com/, “Max 6,” 2012.
[5] G. Schalk and J. Mellinger, A Practical Guide toBrain–Computer Interfacing with BCI2000.
Springer-Verlag London Limited, 2010.
[6] D.J. Krusienski, E. W. Sellers, F. Cabestaing, S.
Bayoudh, D. J. McFarland, T. M. Vaughan, and J. R.
Wolpaw, “A comparison of classification
techniques for the P300 speller,” Journal of NeuralEngineering, vol. 3, no. 4, p. 299, 2006.