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Takumi Kodama, Shoji Makino and Tomasz M. Rutkowski*Life Science Center of TARA, University of Tsukuba
1
*The University of Tokyo, Tokyo, Japan
Toward a QoL improvement of ALS patients: Development of the Full-Body P300-based Tactile
Brain-Computer Interface
Toward a QoL improvement of ALS patients: Development of the Full-Body P300-based Tactile
Brain-Computer Interface @2016 AEARU Young Researchers International Conference@2016 AEARU Young Researchers International Conference
1: Introduction - What’s BCI?
● Brain Computer Interface (BCI)○ Neurotechnology ○ Exploits user intention ONLY using brain waves
2
1: Introduction - ALS Patients
● Amyotrophic lateral sclerosis (ALS) patients○ Have difficulty to move their muscle by themselves○ BCI could be a communicating tool for ALS patients
3
http://www.businessinsider.com/an-eye-tracking-interface-helps-als-patients-use-computers-2015-9
Dr. Hawkins
● Tactile (Touch) P300-based BCI paradigm○ Predict user’s intentions with finding P300 responses○ P300 responses are evoked by touch stimuli
1: Introduction - Research Approach
41, Stimulate touch sensories 2, Classify brain response
AB
A
B
3, Predict user intention
92.0% 43.3%
A B
TargetNon-Target
P300 brainwave response
● Development of a new tactile BCI paradigm● Propose new communication option toward ALS
patients for improving their Quality of Life
1: Introduction - Research Purpose
5
● Full-body Tactile P300-based BCI (fbBCI) [1]○ Applied six vibrotactile stimulus patterns to user’s back○ User can use fbBCI with their body laying down
2: Method - Our Approach
6[1] Kodama T, Shimizu K, Rutkowski TM. Full Body Spatial Tactile BCI for Direct Brain-robot Control. In: Proceedings of the Sixth International Brain-Computer Interface Meeting. Asilomar Conference Center, Pacific Grove, CA USA: Verlag der Technischen Universitaet Graz; 2016. p. 68.
● Full-body Tactile P300-based BCI (fbBCI)
2: Method - Demonstration
7https://www.youtube.com/watch?v=sn6OEBBKsPQ
2: Method - fbBCI specification
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Subject
Robot
Brain waves
Commands
2: Method - Signal Acquisition
9
● Event related potential (ERP) interval○ captures 800 ms long after vibrotactile stimulus onsets○ will be converted to feature vectors with their potentials
In fbBCI (default settings): fs = 512 [Hz] ERPinterval = 800 [ms] = 0.8 [sec] Vlength = ceil(512・0.8) = 410
Vlength
VCh○○
p[0]
…
p[Vlength - 1]
Vlength = ceil( fs・ERPinterval)where fs [Hz] , ERPinterval [sec]
Ch○○
2: Method - Signal Processing
10
● Bandpass Filtering & DownsamplingCh○○
Before:
After:
fs = 512 [Hz]
fs’ = 128 [Hz]
● Concatenating feature vectors
2: Method - Feature Extraction
In fbBCI: fs’ = 128 [Hz] Vlength = ceil(128・0.8) = 103
11
…
Vlength
VCz …
Vlength
VPz …
Vlength
VCP6
…
…
… … ……Vex.) VlengthALL = Vlength・8 = 103・8 = 824
VlengthALL
…
Ch1 Ch2 Ch8
● Non-linear SVM (Gaussian Kernel)
2: Method - Classification (1)
12
where γ = 1/VlengthALL (c = 1)
hyperplane
● Training the classifier
2: Method - Classification (2)
13
VT1
VT2
VlengthALL VlengthALL
VN1
VN2
Classifier (2cls)
VTmax
・
・
・
・
・
・
VNmax
VTmax = 60 / ne VNmax = 60 / ne
Random chooseas many as Tmax
}
Non-Target Target
● Evaluation with trained classifier○ P300 responses exist or not?
2: Method - Classification (3)
14
VT1
VlengthALL
・
・
VTmax = 10
Target? orNon-Target? Classifier (2cls)
Test data
2: Method - Experimental settings
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Condition Details
Number of users (mean age) 10 (21.9 years old)
Number of trials 1 training + 5 tests
Stimulus frequency of exciters 40 Hz
EEG recording system g.USBamp active electrodes EEG system
EEG sampling rate 512 Hz
Vibration stimulus length 100 ms
Inter-stimulus Interval (ISI) 400 ~ 430 ms
● P300 responses were confirmed (> 4 μV) in every channel
3: Result - ERP (P300) responses
16
TargetNon-Target
3: Result - Classification accuracy
17
● Mean classification average: 59.83 %
4: Discussion and conclusions
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● The effectiveness of fbBCI modality was confirmed○ Classification accuracy : 59.83 % by GSVM○ Expect to improve a QoL for ALS patients
● However, more analyses would be required○ Only 10 healthy users has tried yet○ Need higher accuracies for practical
applications
● [1] Kodama T, Shimizu K, Rutkowski TM. Full Body Spatial Tactile BCI for Direct Brain-robot Control. In: Proceedings of the Sixth International Brain-Computer Interface Meeting: BCI Past, Present, and Future. Asilomar Conference Center, Pacific Grove, CA USA: Verlag der Technischen Universitaet Graz; 2016. p. 68.
● [2] Kodama T, Shimizu K, Makino S, Rutkowski TM. Tactile Brain–computer Interface Based on Classification of P300 Responses Evoked by Spatial Vibrotactile Stimuli Delivered to the User’s Full Body. In: Asia-Pacific Signal and Information Processing Association, 2016 Annual Summit and Conference (APSIPA ASC 2016). APSIPA. Jeju, Korea: IEEE Press; 2016. p. (accepted, in press).
● [3] Kodama T, Shimizu K, Makino S, Rutkowski TM. Full–body Tactile P300–based Brain–computer Interface Accuracy Refinement. In: Proceedings of the International Conference on Bio-engineering for Smart Technologies (BioSMART 2016). Dubai, UAE: IEEE Press; 2016. p. (accepted, in press).
Publications
19
20
Thank you for listening!
● Training the classifier
2: Method - Classification (2)
21
VT1
VT2
VlengthALL VlengthALL
VN1
VN2
VTmax
・
・
・
・
・
・
VNmax
・
・
・
VTmax = 60 / ne VNmax = 300 / ne
Classifier (2cls)
Non-Target Target
● How to train the P300-based BCI classifier?○ Each stimulus pattern were given 10 times randomly○ Altogether 360 (60×6) times for a training classifier
2: Method - Training phase
22
ω1 : Target
Classifier (2cls)
Target 1
1
2
345
6
1
6
5
4
3
2
ω2 : Non-Target
× 10
× 10
× 10
× 10
× 10
× 10Session: 1/6
● How to train the P300-based BCI classifier?○ Each stimulus pattern were given 10 times randomly○ Altogether 360 (60×6) times for a training classifier
2: Method - Training phase
23
ω1 : Target
Classifier (2cls)
Target 2
1
2
345
6
1 × 102 × 10
Session: 2/6
6
5
4
3
2
ω2 : Non-Target× 20
× 20
× 20
× 20
× 10
1 × 10
● How to train the P300-based BCI classifier?○ Each stimulus pattern were given 10 times randomly○ Altogether 360 (60×6) times for a training classifier
2: Method - Training phase
24
ω1 : Target
Classifier (2cls)
Target 3
1
2
345
6 ω2 : Non-Target
Session: 3/6
1 × 102 × 10
6
5
4
3
2
× 30
× 30
× 30
× 20
× 20
1 × 20
3 × 10
● How to train the P300-based BCI classifier?○ Each stimulus pattern were given 10 times randomly○ Altogether 360 (60×6) times for a training classifier
2: Method - Training phase
25
ω1 : Target
Classifier (2cls)
Target 4
1
2
345
6 ω2 : Non-Target
Session: 4/6
1 × 102 × 10
6
5
4
3
2
× 40
× 40
× 30
× 30
× 30
1 × 30
3 × 104 × 10
● How to train the P300-based BCI classifier?○ Each stimulus pattern were given 10 times randomly○ Altogether 360 (60×6) times for a training classifier
2: Method - Training phase
26
ω1 : Target
Classifier (2cls)
Target 5
1
2
345
6 ω2 : Non-Target
Session: 5/6
1 × 102 × 10
6
5
4
3
2
× 50
× 40
× 40
× 40
× 40
1 × 40
3 × 104 × 105 × 10
● How to train the P300-based BCI classifier?○ Each stimulus pattern were given 10 times randomly○ Altogether 360 (60×6) times for a training classifier
2: Method - Training phase
27
ω1 : Target
Classifier (2cls)
Target 6
1
2
345
6 ω2 : Non-Target
Session: 6/6
1 × 102 × 10
6
5
4
3
2
× 50
× 50
× 50
× 50
× 50
1 × 50
3 × 104 × 105 × 106 × 10
60 300
2: Method - Evaluation phase
● How to predict user’s intention with trained classifier?○ Correct example
28
ω1 : Target
Classifier (2cls)
1 × 10
72.6 %
Target 1
Session: 1/6
ω1 : Target
Classifier (2cls)
2 × 10
24.4 %ω1 : Target
Classifier (2cls)
3 × 10
56.3 %ω1 : Target
Classifier (2cls)
4 × 10
44.1 %ω1 : Target
Classifier (2cls)
5 × 10
62.9 %ω1 : Target
Classifier (2cls)
6 × 10
39.8 %
1
2
345
6
2: Method - Evaluation phase
29
ω1 : Target
Classifier (2cls)
1 × 10
35.1 %
Target 6
Session: 6/6
ω1 : Target
Classifier (2cls)
2 × 10
48.1 %ω1 : Target
Classifier (2cls)
3 × 10
69.2 %ω1 : Target
Classifier (2cls)
4 × 10
54.3 %ω1 : Target
Classifier (2cls)
5 × 10
50.9 %ω1 : Target
Classifier (2cls)
6 × 10
64.3 %
1
2
345
6
● How to predict user’s intention with trained classifier?○ Wrong example
2: Method - Evaluation phase
Target 11/6
5
Target 2
Target 3
3
5
● Calculate stimulus pattern classification accuracy○ How many sessions could the user classify targets?
Target 4
Target 5
Target 6
2
4
Result
1
Session
2/6
3/6
4/6
5/6
6/6
1 Trial
Classification accuracy rate:
4/6 = 0.667 ⇒ 66.7 [%]
Correct
Correct
Wrong
Correct
Correct
Wrong
Target Status
Recommended