Kodama T, Makino S, Rutkowski TM. Tactile Brain-Computer Interface Using Classification of P300 Responses Evoked by Full Body Spatial Vibrotactile Stimuli. In: Asia-Pacific Signal and Information Processing Association, 2016 Annual Summit and Conference (APSIPA ASC 2016). APSIPA. Jeju, Korea: IEEE Press; 2016.

1. 1
Tactile Brain-computer Interface
Using Classification of P300
Responses Evoked by Full Body
Spatial Vibrotactile Stimuli
Tactile Brain-computer Interface
Using Classification of P300
Responses Evoked by Full Body
Spatial Vibrotactile Stimuli
@APSIPA ASC 2016@APSIPA ASC 2016
1
Takumi Kodama , Shoji Makino and Tomasz M. Rutkowski1 1 2, 3
Life Science Center of TARA, University of Tsukuba ,
The University of Tokyo , Saitama Institute of Technology
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2 3

2. 1: Introduction - What’s the BCI?
● Brain Computer Interface (BCI)
○ Exploits user intention ONLY using brain waves
2

3. 1: Introduction - ALS Patients
● Amyotrophic lateral sclerosis (ALS) patients
○ Hard to move their muscle due to nerve injuries
○ BCI could be a communicating option for them?
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!!!
…
…

4. 1: Introduction - Research Approach
1, Stimulate touch sensories 2, Classify brain response
A
B
A
B
3, Predict user intention
92.0% 43.3%
A B
Target
Non-Target
P300 brainwave response
4
● Tactile (Touch-based) P300-based BCI paradigm
○ Predict user’s intentions by decoding P300 responses
○ P300 responses were evoked by external (tactile) stimuli

5. ● Previous Tactile P300-based BCI paradigm
○ Tactile point-pressure BCI (for usual hands)
○ Tactile and auditory BCI (for head positions)
1: Introduction - Previous Research
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[1] K. Shimizu, S. Makino, and T. M. Rutkowski, “Inter–stimulus interval study for the tactile point–pressure brain–computer interface,” in 2015 37th
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE Engineering in Medicine and Biology
Society. IEEE Press, August 25–29, 2015, pp. 1910–1913.
[2] H. Mori, Y. Matsumoto, Z. R. Struzik, K. Mori, S. Makino, D. Mandic, and T. M. Rutkowski, “Multi-command tactile and auditory brain
computer interface based on head position stimulation,” in Proceedings of the Fifth International Brain-Computer Interface Meeting 2013. Asilomar
Conference Center, Pacific Grove, CA USA: Graz University of Technology Publishing House, Austria, June 3-7, 2013, p. Article ID: 095.

6. ● Propose new touch-based BCI modality intended
for communicating with ALS patients
● Confirm an effectiveness of the modality in terms
of practical applications, stimulus pattern
discriminations and classification accuracies
1: Introduction - Research Purpose
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7. 2: Method - Our Approach
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● Full-body Tactile P300-based BCI (fbBCI)
○ Applies six vibrotactile stimulus patterns to user’s back
○ User can take experiment with their body lying down

8. 2: Method - Two experiments
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Psychophysical EEG
● Without attaching
EEG electrodes
● Selecting target
stimulus with button
pressing
● To evaluate the fbBCI
stimulus pattern
feasibility
● With ERP calculation
● Selecting target
stimulus with P300
responses
● To reveal the fbBCI
classification
accuracies

9. 2: Method - Signal Acquisition
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● Event related potential (ERP) interval
○ captures 800 ms long after vibrotactile stimulus onsets
○ will be converted to feature vectors with their potentials
Vlength
VCh○○
p[0]
…
p[Vlength - 1]
Ch○○

10. Condition Details
Number of users (mean age) 10 (21.9 years old)
Stimulus frequency of exciters 40 Hz
Vibration stimulus length 100 ms
Inter-stimulus Interval (ISI) 400 ~ 430 ms
Number of trials 1 training + 5 tests
EEG sampling rate 512 Hz
EEG recording system g.USBamp active electrodes EEG
system
Classification algorithm SWLDA with BCI2000
2: Method - Experimental settings
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11. 3: Result - Behavioral accuracies
● Correct rate exceeded 95% for each stimulus pattern
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12. 3: Result - Response times
● Response time differences
for each stimulus pattern
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13. ● P300 responses were confirmed (> 4 μV) in every channel
3: Result - ERP (P300) responses
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Target
Non-Target

14. 3: Result - ERP AUC Scores
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● Times series of the Target vs. Non-Target AUC scores

15. 3: Result - ERP pattern differences
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● P300 peaks were shifted to later latencies from #1 to #6
#1 Left arm
#2 Right arm
#3 Shoulder
#4 Waist
#5 Left leg
#6 Right leg

16. 3: Result - Classification accuracy
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● Grand mean fbBCI classification accuracy: 53.67 %

17. 3: Result - ITR score
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● Information Transfer Rate (ITR)
○ Averaged score: 1.31 bit/minute

18. 4. Conclusions
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● The validity of fbBCI paradigm was confirmed
○ Classification accuracy : 53.67 % by SWLDA
○ Expect to help ALS patients
● However, more analyses would be required
○ Only 10 healthy users has tried yet
○ Applying other machine learning algorithms
○ Higher accuracies would be needed for practical
applications

19. ● The HARA Research Foundation Research Grant
Project
○ for APSIPA ASC 2016 Participation
Special Thanks
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20. 17
Many thanks for your attention!