Tactile and Bone-conduction Auditory Brain
Computer Interface for Vision and Hearing
Impaired Users - Stimulus Pattern and BCI
Accuracy Improvement
Hiromu Mori∗ , Shoji Makino∗,† and Tomasz M. Rutkowski∗,†,5∗ TARA Life Science Center, University of Tsukuba, Tsukuba, Japan† RIKEN Brain Science Institute, Wako-shi, Japan

E-mail: tomek@bci-lab.info (5 corresponding author)
Website: http://bci-lab.info/

Abstract—This paper aims to improve tactile and bone–
conduction brain computer interface (tbaBCI) classification accuracy based on a new stimulus pattern search in order to trigger
more separable P 300 responses. We propose and investigate three
approaches to stimulus spatial and frequency content modification. As result of the online tbaBCI classification accuracy tests
with six subjects we conclude that frequency modification in
the previously reported single vibrotactile exciter–based patterns
leads to border of significance statistical improvements.

I. I NTRODUCTION
The contemporary brain–computer interfaces (BCIs) are
typically based on mental visual or motor imagery paradigms,
which require extensive user training and good eyesight fromthe users [1]. Recently alternative solutions have been adopted
successfully to make use of spatial auditory [2] or tactile(somatosensory) sensory modalities [3], [4], [5], [6], [7] toimprove BCI usage comfort and to increase the informationtransfer rate (ITR) achieved by users. Two recent reports
published by the authors in [8], [9] have shown combinationof two above–mentioned modalities, which rely on P 300
response evoked by the audio and tactile stimuli delivered
simultaneously via vibrotactile exciters attached to the head
positions, thus benefiting from the bone–conduction effectfor audio. This offers a viable alternative for individuals
lacking somatosensory afferent neural fibers transmission fromperipheral body locations.
This paper reports improvement of our previously reported
tbaBCI paradigm [8] based on suitable stimulus spatial patterns that subject can easily distinguish and as a result evokedstronger in amplitude in EEG the P 300 responses. We propose
to create stimulus patterns using multiple subsets of thevibrotactile exciters using also various stimulation frequencies.
The above steps are meaning to give the user a clear spatial andfrequency pattern based cues leading to simpler discrimination
among the presented tactile and bone–conduction auditory
stimuli.
The rest of the paper is organized as follows. The next
section introduces the materials and methods used in the

978-616-361-823-8 © 2014 APSIPA

study. It also outlines the experiments conducted within thepresented project. The results obtained in EEG online andoffline experiments with six BCI subjects are then discussed.
Finally, conclusions are formulated and directions for future
research are outlined.
II. M ATERIALS AND M ETHODS
Six volunteer male BCI subjects participated in the experiments. The subject’s mean age was 25.83, with a standard
deviation of 8.17. All the experiments were performed at
the Life Science Center of TARA, University of Tsukuba,
Japan. The psychophysical and online (real–time) EEG tbaBCI
paradigm experiments were conducted in accordance with the
WMA Declaration of Helsinki Ethical Principles for Medical Research Involving Human Subjects. The experimental
procedures were reviewed by the Ethical Committee of theFaculty of Engineering, Information and Systems at Universityof Tsukuba, Tsukuba, Japan (experimental permission number
2013R7).
A. Tactile and Bone–conduction Auditory Stimulus
The tactile and bone–conduction auditory stimulus wascreated as a square acoustic frequency wave generated by
the ARDUINO micro–controller board with a custom built
battery–driven and electrically isolated multichannel power
amplifier. An in–house developed software managed the above
device from MAX/MSP visual programming environment. The
stimuli were delivered to the subject’s head locations via
the vibrotactile exciters HiWave HIAX19C01-8 operating in
the acoustic frequencies of 300 ∼ 20, 000 Hz, as depicted
in Figure 1. The subject attended (button–press responded
in case of psychophysical or mentally counted only in caseof EEG experiments) only to the instructed target locations,while ignoring the other stimuli (we assigned labels to theattended stimulus as target and to the other ignored as non–target). The instructions were presented visually by meansof the MAX/MSP program in psychophysical experiment andBCI2000 program in EEG experiment.

APSIPA 2014㻱㼤㼜㼑㼞㼕㼙㼑㼚㼠㻌㻏㻝

Fig. 1. User’s head with EEG cap with embedded electrodes and vibrotactile
exciters (indicated by the white arrows in the picture) attached. The photograph is reproduced with the user permission.

In this study, we conducted three experiments to test the
proposed new stimulus patterns’ effectiveness. In each of the
conducted experiments six different stimulus patterns wereused. In the experiment #1 the stimuli were delivered tothe user’s head by single vibrotactile exciters as depicted
in Figure 2. This setting was the same as in our previous
studies [8], [9] in order to compare brainwave responses
with the current setup of the tactile and bone–conduction
auditory stimulus patterns introduced in this paper. In theexperiment #2 the stimuli were composed of multiple vibrotactile exciters combined together. Each stimulus patternin the experiment #2 has been also depicted in the middle
column of Figure 2. Based on our previous studies [8], [9]and user reports we decided to stimulate larger head areas
to avoid mistakes caused often by close spatial proximity ofthe vibrotactile exciters embedded within the EEG cap. In theboth experiments #1 and #2 the same stimulus frequency of
350 Hz was used. Only in the experiment #3 we used various
frequencies in order to add more discriminative features for theusers (see also Figure 2 for details).
B. Psychophysical Experiment Protocol
The psychophysical experiment was conducted to investigate the stimulus pattern possible influences on the subject’s
response time and accuracy. The behavioral responses werecollected using a computer keyboard and the MAX/MSP program. Each subject was instructed which stimulus to attend by
a red color circle shown on a computer display and generated
by the instruction program as shown in Figure 3. The subject
was instructed to press the response button (any key on a
keyboard) with the dominant hand when the target stimulus
was presented in a series of random distractors (as in aclassical oddball paradigm [1]).

㻱㼤㼜㼑㼞㼕㼙㼑㼚㼠㻌㻏㻞㻱㼤㼜㼑㼞㼕㼙㼑㼚㼠㻌㻏㻟㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻝㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻠㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻝㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻠㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻝㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻠㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻞㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻡㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻞㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻡㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻞㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻡㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻟㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻢㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻟㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻢㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻟㻿㼠㼕㼙㼡㼘㼡㼟㻌㻏㻢

Fig. 2. The spatial setting for the tactile and bone–conduction auditory
stimulus patterns used in the experiments reported in this paper. The circles
drawn on face icons represent the vibratoctile exciter locations. The color
wavy lines depict the stimulus spatial configurations of the exciters used ineach pattern. The line colors represent the stimulus frequencies (red 350 Hz;
brown 300 Hz; purple 500 Hz; and green 700 Hz respectively).

Fig. 3. The visual instruction screen presented to the subject during thepsychophysical experiment created in the MAX/MSP environment. The circles
represent vibrotactile exciters and the red color indicates the target.

Each trial was composed of 100 ms long tactile stimuli delivered in a randomized order to each head location separately
with an inter–stimulus–interval (ISI) of 900 ms. Every random
sequence thus contained a single target and five non–targets.
A single run included 10 sequences (resulting in 60 targets and
300 non–targets). Details of the psychophysical experimental
protocol are summarized in Table I.
C. EEG Experiment Protocol
EEG signals were captured with an EEG amplifier system vAmp by Brain Products GmbH, Germany, using
g.GAMMAbox with connected active electrodes g.LADYbird
by g.tec Medical Instruments GmbH, Austria. The electrodes
were attached to the head locations: Cz, Pz, P3, P4, C3,

TABLE I
T HE PSYCHOPHYSICAL EXPERIMENT CONDITIONS

Vibrotactile frequencies
Inter–stimulus–interval (ISI)
Tactile stimulus device
Subject response input device
Number of sequences
Number of runs

Detail6
100 msexperiment #1 350 Hz
experiment #2 350 Hz
experiment #3 {300,500,700} Hz
900 msHiWave HIAX19C01-8
Computer keyboard
10
1

100
6 1.7%

0.0%

0.0%

0.0%

0.0%

98.3%

0.0%

5 0.0%

0.0%

0.0%

0.0%

100.0% 0.0%

0.0%

90
80
70

instructed target

Experimental condition
Number of subjects
Stimulus length

Confusion matrix of the psychophysical experiment #1 accuracies

4 0.0%

0.0%

0.0%

100.0% 0.0%

0.0%

0.0%

60
50

3 0.0%

0.0%

98.3%

1.7%

0.0%

0.0%

0.0%

2 0.0%

100.0% 0.0%

0.0%

0.0%

0.0%

0.0%

40
30
20

C4, CP5, and CP6 as in the 10/10 extended international
system [10]. The ground electrode was attached to FPz position and reference to the left earlobe respectively. No electromagnetic interference was observed from the vibrotactile
exciters operating in higher frequencies comparing to the EEG
frequency spectrum. Details of the EEG experimental protocol
are summarized in Table II.
The recorded EEG signals were processed by the in–houseextended BCI2000 [11], [12], [13] using a stepwise linear
discriminant analysis (SWLDA) classifier [14] with features
drawn from the 0 ∼ 800 ms ERP latency intervals. The
sampling rate was set at 500 Hz, the high pass filter at 0.1
Hz, and the low pass filter at 40 Hz. The ISI was set to
400 ms, and each stimulus duration was 100 ms. The subjects
were instructed which stimulus to attend by visual instruction
presented on a computer screen using the same interface asin the psychophysical experiment presented in Figure 3. Eachrun included ten sequences (randomized 60 targets and 300
non–targets each), and the averages of ten ERPs were later
used for the classification. Each subject performed three runs
(resulting in 180 targets and 900 non–targets), which werelater averaged as discussed in the next section.
III. R ESULTS
The results comparison the three setups tested in psychophysical and online BCI EEG experiments are summarized
in the following sections.
TABLE II
T HE EEG EXPERIMENT CONDITIONS
Experimental condition
Number of subjects
Stimulation length
Vibrotactile frequency
Inter–stimulus–interval (ISI)
EEG recording system
Number of electrodes
EEG electrodes positions
Reference and ground electrodes
Tactile stimulus device
Number of sequences
Number of runs

Detail6
100 msexperiment #1 350 Hz
experiment #2 350 Hz
experiment #3 {300, 500, 700} Hz
400 msvAamp and g.GAMMAbox withactive EEG electrodes
8
Cz, CPz, P3, P4, C3, C4, CP5, CP6
earlobe and FPz
HiWave HIAX19C01-8
103

1 98.3%
1

0.0%

0.0%

2

3

0.0%

0.0%

1.7%

0.0%

4
5
subject response

6

N

10
0

Fig. 4. Confusion matrix resulting from the psychophysical experiment #1
of all the six subjects in form of a grand mean average responses. The
diagonal of the above matrix depicts the correct response rates, while the off–
diagonals the mistakes. The last (seventh and marked with a letter N ) column
illustrates the lack of misses (no responses) in our experiment. The color–
coding, summarized with the color–bar on the right of the plot, visualizes the
response rates.

A. Psychophysical Experiment Results
The psychophysical experiment accuracy results are depicted in form of a confusion matrices in Figures 4, 5, and6. The confusion matrices with majority of accurate responses
depicted on diagonals confirmed experimental designs validity
and low difficulties for the tested participants. The boxplot
psychophysical response time distributions are presented in
Figures 7, 8, and 9. Each response time distribution results
further confirmed the stimulus related cognitive load similarity since the behavioral responses for all the patterns werebasically the same (as resulted with non–significant mediandifferences from pairwise Wilcoxon–tests). This finding validated the design of the following tbaBCI EEG experiments.
B. EEG Experiment Results
The grand mean averaged results of the conducted online
BCI EEG experiments with the six participating subjects arepresented in Figures 10, 11, and 12 in form of matrices
depicting ERP latencies with P 300 responses together withthe area under the curve (AUC) feature separability analyses.
We report also the averaged head topographic plots of theevoked responses at the highest and lowest ERP separability
latencies in the target versus non–target averaging scenario as
elucidated by AUC maxima and minima. The highest averaged
differences were found at 398 ms, 444 ms and 392 ms, which
perfectly aligned the P 300 response peaks in grand mean
average ERP plots.
Figure 13 depicts time series of all electrodes with target
and non–target responses from the three EEG experiments

Confusion matrix of the psychophysical experiment #2 accuracies

Psychophysical experiment response times

100
6 3.1%

0.0%

1.6%

3.1%

0.0%

92.2%

0.0%

550
7

90
500

80
5 0.0%

0.0%

0.0%

0.0%

92.2%

7.8%

6

0.0%
450

4 0.0%

0.0%

0.0%

88.1%

0.0%

11.9%

0.0%

400

50
3 0.0%

0.0%

96.7%

0.0%

0.0%

3.3%

0.0%

2 0.0%

100.0% 0.0%

0.0%

0.0%

0.0%

0.0%

5

60

40

time [ms]instructed target

70

4
350

30

3
300

20
1 100.0% 0.0%
1

2

0.0%
3

0.0%

0.0%

0.0%

0.0%

4
5
subject response

6

N

10
0

Confusion matrix of the psychophysical experiment #3 accuracies
100
0.0%

1.6%

0.0%

0.0%

95.2%

0.0%

0.0%

0.0%

0.0%

100.0% 0.0%

0.0%

instructed target

70
4 0.0%

0.0%

0.0%

95.2%

0.0%

4.8%

0.0%

60
50

3 0.0%

0.0%

98.4%

1.6%

0.0%

0.0%

0.0%

2 0.0%

100.0% 0.0%

0.0%

0.0%

0.0%

0.0%

40
30
20

1 98.4%
1

0.0%

0.0%

2

3

0.0%

0.0%

1.6%

0.0%

4
5
subject response

6

N

150

1

2

3
4
response numbers

5

6

0

Fig. 7. Distribution boxplots depicting all the participating subjects’ psychophysical experiment #1 response times. The red lines in each plot depict
the 25th , 50th , and 75th percentiles. No significant differences were observed
among the medians as tested with the pairwise Wilcoxon rank sum tests. The
color–coding, summarized with a color–bar on the right of the plot, visualizes
the response numbers.

90
80

5 0.0%

1200

Fig. 5. Confusion matrix resulting from the psychophysical experiment #2of all the six subjects in form of a grand mean average responses. The
diagonal of the above matrix depicts the correct response rates, while the off–
diagonals the mistakes. The last (seventh and marked with a letter N ) column
illustrates the lack of misses (no responses) in our experiment. The color–
coding, summarized with the color–bar on the right of the plot, visualizes the
response rates.

6 3.2%

2

250

10
0

Fig. 6. Confusion matrix resulting from the psychophysical experiment #3of all the six subjects in form of a grand mean average responses. The
diagonal of the above matrix depicts the correct response rates, while the off–
diagonals the mistakes. The last (seventh and marked with a letter N ) column
illustrates the lack of misses (no responses) in our experiment. The color–
coding, summarized with the color–bar on the right of the plot, visualizes the
response rates.

collected together. The black lines in the above figure indicate
non–target responses which resulted the same ERP shapes.
The color lines represent P 300 responses which remained enhanced even after 500 ms mark when the next stimulus started.

The results comparison of the three online BCI EEG experiments classification accuracies are presented in Figure 14.
The majority of the averaged classification accuracies resulted
above chance levels of 16.66%. The red bars in Figure 14
show results of the experiment #1, while the blue bars depict
results of the experiment #2, and the green bars represent
results of the experiment #3. The classification accuracies of
the new proposed stimulus patterns (experiments #2 and #3)
were not significantly improved comparing with the previously
proposed approach in the experiment #1, but we could show
improvements between patterns #2 and #3. We conductedpairwise t–test analyses using classification accuracies. As
results, we obtained the border of significance results withp < 0.06 between the patterns #2 and #3, which supported
the initial research hypothesis of the new patterns search.
IV. C ONCLUSIONSThis case study demonstrated results obtained with a comparison of three six–commands tactile and bone–conduction
auditory approaches to BCI paradigms improvements. The
experiment results obtained in this study confirmed the initial research hypothesis and stimulation patterns optimization
could lead to the final BCI accuracy improvements.
The EEG experiment with the paradigm has confirmed thattactile and bone–conduction BCI paradigm can be used easily
improved for online case using SWLDA classifier.
The results presented offer a step forward in the development of novel neurotechnology application. The current

Psychophysical experiment response times
550
6

time [ms]

500

450

5

400

4

paradigm obviously needs still improvements and further
optimizations to implement online successfully. These needs
determine the major lines of study for future research.
We plan to continue this line of the tactile and bone–
conduction auditory BCI research in order to further optimize
stimulus patterns design.
ACKNOWLEDGMENTS

350
3
300
2
250
1

200

We would like to express our gratitude to Yoshihiro Matsumoto, the BCI-lab-group and the Multimedia Laboratory
alumnus, who supported the project in the early stages with
programming of BCI2000 updates.
This research was supported in part by the Strategic Information and Communications R&D Promotion Programme
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.
R EFERENCES

150

1

2

3
4
response numbers

5

6

0

Fig. 8. Distribution boxplots depicting all the participating subjects’ psychophysical experiment #2 response times. The red lines in each plot depict
the 25th , 50th , and 75th percentiles. No significant differences were observed
among the medians as tested with the pairwise Wilcoxon rank sum tests. The
color–coding, summarized with a color–bar on the right of the plot, visualizes
the response numbers.

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Psychophysical experiment response times
550

8

500

7
max(AUC) at 398 ms

6

Averaged targets
0.6
0.58

time [ms]

400

5

[µV]

450

0.56
0.54

350

4

Cz
CPz
P3
P4
C3
C4
CP5
CP6

4
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600

900

Averaged non−targets
0.5

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[µV]

3

250

2

min(AUC) at 250 ms
0.6

Cz
CPz
P3
P4
C3
C4
CP5
CP6

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1

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4
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AUC

0.54

150

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900

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CPz
P3
P4
C3
C4
CP5
CP6

0.6
0.58
0.56
0.54
0.52
0.5
0

Fig. 9. Distribution boxplots depicting all the participating subjects’ psychophysical experiment #3 response times. The red lines in each plot depict
the 25th , 50th , and 75th percentiles. No significant differences were observed
among the medians as tested with the pairwise Wilcoxon rank sum tests. The
color–coding, summarized with a color–bar on the right of the plot, visualizes
the response numbers.

300

Targets vs. non−targets AUC scores

300

600

900

time [ms]Fig. 10. All the six subjects grand mean averaged results of the EEG
experiment#1. The left two panels present the head topographic plot of thetarget versus non–target area under the curve (AUC), a measure commonlyused in machine learning intra–class discriminative analysis. (AUC > 0.5 isusually assumed to be confirmation of feature separability).The right threepanels present the averaged ERPs (six subjects; three experimental runs eachwith six targets repeated ten times) for target in the top panel; non–target in
the middle; and target versus non–target AUC at the bottom.

max(AUC) at 444 ms

Averaged targets

0.58
0.56

[µV]

0.6

0.54

Cz
CPz
P3
P4
C3
C4
CP5
CP6

4
2
0

0

0.52

300

0.5

[µV]

0.48

min(AUC) at 0 ms
0.6

4
2
0

0

AUC

0.54

0.48

300

600

900

Targets vs. non−targets AUC scores

0.56

0.5

900

Cz
CPz
P3
P4
C3
C4
CP5
CP6

0.58

0.52

600

Averaged non−targets

Cz
CPz
P3
P4
C3
C4
CP5
CP6

0.6

0.55

0.5
0

300

600

900

time [ms]Fig. 11. All the six subjects grand mean averaged results of the EEG
experiment#2. The left two panels present the head topographic plot of thetarget versus non–target area under the curve (AUC), a measure commonlyused in machine learning intra–class discriminative analysis. (AUC > 0.5 isusually assumed to be confirmation of feature separability).The right threepanels present the averaged ERPs (six subjects; three experimental runs eachwith six targets repeated ten times) for target in the top panel; non–target in
the middle; and target versus non–target AUC at the bottom.

max(AUC) at 392 ms

Averaged targets
0.62

0.56
0.54

Cz
CPz
P3
P4
C3
C4
CP5
CP6

4
2
0

[µV]

0.48

min(AUC) at 246 ms
0.62

90%

900

80%
4
2
0
ï2

0

0.58

300

600

900

Targets vs. nonïtargets AUC scores

0.56

AUC

0.54

0.48

600

Cz
CPz
P3
P4
C3
C4
CP5
CP6

0.6

0.5

300

Averaged nonïtargets

0.5

0.52

100%

ï2
0

0.52

[3] G. Muller-Putz, R. Scherer, C. Neuper, and G. Pfurtscheller, “Steadystate somatosensory evoked potentials: suitable brain signals for braincomputer interfaces?” Neural Systems and Rehabilitation Engineering,IEEE Transactions on, vol. 14, no. 1, pp. 30–37, March 2006.

Cz
CPz
P3
P4
C3
C4
CP5
CP6
0

300

600

70%

Accuracy

0.58

[µV]

0.6

Fig. 13. ERP to real and virtual sound image stimuli for all EEG electrodes
used in the experiments conducted. The red lines represent the grand mean
averaged target responses to the EEG experiment#1, while the green lines
indicate target responses to the EEG experiment#2, and the blue lines show
target response to the EEG experiment#3, remaining black lines mean non–target response to the all of EEG experiment, respectively. Error bars depicting
standard errors are also drawn around each of the averaged responses.

60%

30%

0.55

20%

0.5

10%

time [ms]Experime

40%

0.6

900

Experime

50%

Experime

0%#1

Fig. 12. All the six subjects grand mean averaged results of the EEG
experiment#3. The left two panels present the head topographic plot of thetarget versus non–target area under the curve (AUC), a measure commonlyused in machine learning intra–class discriminative analysis. (AUC > 0.5 isusually assumed to be confirmation of feature separability).The right threepanels present the averaged ERPs (six subjects; three experimental runs eachwith six targets repeated ten times) for target in the top panel; non–target in
the middle; and target versus non–target AUC at the bottom.

#2

#3

#4

#5

#6

Average

Subject
Fig. 14. The accuracy results comparing of the three experiments with various
stimulus patterns. Blue, red and green bars stand for experiments #1, #2 and#3 respectively. The horizontal axis represents the subject numbers and thevertical one the accuracy rates in percents. Error bars depict standard errors.

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