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Takumi Kodama , Kensuke Shimizu , Shoji Makino and Tomasz M. Rutkowski

Full–body Tactile P300–based

Brain–computer Interface Accuracy Refinement

@bioSMART conference 2016

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Life Science Center of TARA, University of Tsukuba, The University of Tokyo, Saitama Institute of Technology, RIKEN Brain Science Institute

1: Introduction - What’s BCI?

● Brain Computer Interface (BCI)○ Neurotechnology ○ Exploits user intention ONLY using brainwaves

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1: Introduction - ALS Patients

● Amyotrophic lateral sclerosis (ALS) patients○ Have difficulty to move their muscle by themselves○ BCI could be a communicating tool for them

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ʳ!…

…

● Tactile (Touch-based) P300-based BCI paradigm○ P300 responses were evoked by external (tactile) stimuli○ Predict user’s intentions by decoding P300 responses

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

● Full-body Tactile P300-based BCI (fbBCI) [1]○ Applies six vibrotactile stimulus patterns to user’s back○ User can use fbBCI while lying down and interacting

using a whole body surface

1: Introduction - Our Method

5[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.

1: Introduction - Demonstration

6https://www.youtube.com/watch?v=sn6OEBBKsPQ

● P300 responses were confirmed (> 4 μV) in each channel

1: Introduction - fbBCI results (1)

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TargetNon-Target

● Problem: Low online classification accuracies○ SWLDA : 53.67 % (10 users average)

1: Introduction - fbBCI results (2)

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● To improve the fbBCI classification accuracy● To reconfirm the validity of fbBCI modality

1: Introduction - Research Purpose

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● Test several signal preprocessing combinations ①○ Downsampling○ Epoch averaging

● Classify with three different machine learning methods ②○ SWLDA○ Linear SVM○ Non-linear SVM (Gaussian kernel)

2: Method - Conditions

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CommandBrainwave① ②

2: Method - Signal Acquisition

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● Event related potential (ERP) ○ captures 800 ms after an onset of vibrotactile stimulus ○ next converted to a feature vector using EEG potential

ex.) 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 Preprocessing(1)

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● Downsampling (nd)○ ERPs were decimated by

4 (128 Hz), 16 (32 Hz) or kept intact (512 Hz)

○ To reduce vector length Vlength

nd = 4 (128 Hz) nd = 16 (32 Hz)

Ch○○ Ch○○

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● Epoch averaging (ne)○ ERPs were averaged using 5, 10

ERPs or no averaging○ To cancel background noise

ne = 1 ne = 10

Ch○○ Ch○○

2: Method - Signal Preprocessing(2)

● Concatenating all feature vectors

2: Method - Feature Extraction

ex.) fs = 128 [Hz] (nd = 4) Vlength = ceil(128・0.8) = 103

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…

Vlength

VCz …

Vlength

VPz …

Vlength

VCP6

…

…

… … ……Vex.) VlengthALL = Vlength・8 = 103・8 = 824

VlengthALL

…

Ch1 Ch2 Ch8

● Machine learning methods○ SWLDA○ Linear SVM

… K(u,v’) = u v’○ Non-linear SVM (Gaussian)

… K(u,v’) = exp(-γ||u-v|| ) γ = 1/VlengthALL , c = 1

2: Method - Classification (1)

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T

2

● Training the classifier

2: Method - Classification (2)

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VT1

VT2

VlengthALL VlengthALL

VN1

VN2

VTmax

・

・

・

・

・

・

VNmax

・

・

・

VTmax = 60 / ne VNmax = 300 / ne

Classifier (2cls)

Non-Target Target

● Training the classifier

2: Method - Classification (2)

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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 the trained classifier○ Same nd and ne were applied

2: Method - Classification (3)

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VT1

VlengthALL

・

・

VTmax = 10 / ne

Target? orNon-Target? Classifier (2cls)

Test data

● SWLDA classification accuracies○ BEST: 57.48 % (nd = 4, ne = 1)

3: Results - SWLDA

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Signal decimation (nd)

● Linear SVM classification accuracies○ BEST: 58.5 % (nd = 16, ne = 10)

3: Results - Linear SVM

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Signal decimation (nd)

● Non-linear SVM classification accuracies○ BEST: 59.83 % (nd = 4, ne = 1)

3: Results - Non-linear SVM

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Signal decimation (nd)

4: Discussion and conclusions

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● fbBCI classification accuracy has been improved○ Both nd and ne combinations were tested○ 53.67 % in previous reported results

⇒ 59.83 % by non-linear SVM (nd = 4, ne = 1)○ 58.5 % by linear SVM and 57.48 % by SWLDA

● The potential validity of fbBCI modality was reconfirmed○ Expect to improve a QoL for ALS patients

● However, more analyses would be required○ Only 10 healthy users of fbBCI paradigm○ Need higher accuracies in case of a practical application

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Many thanks for your attention!