full–body tactile p300–based brain–computer interface accuracy refinement
TRANSCRIPT
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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
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● 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!