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1
Takumi Kodama , Kensuke Shimizu ,
Shoji Makino and Tomasz M. Rutkowski
Full–body Tactile
P300–based
Brain–computer Inter...
1: Introduction - What’s BCI?
● Brain Computer Interface (BCI)
○ Neurotechnology
○ Exploits user intention ONLY using brai...
1: Introduction - ALS Patients
● Amyotrophic lateral sclerosis (ALS) patients
○ Have difficulty to move their muscle by th...
● Tactile (Touch-based) P300-based BCI paradigm
○ P300 responses were evoked by external (tactile) stimuli
○ Predict user’...
● Full-body Tactile P300-based BCI (fbBCI) [1]
○ Applies six vibrotactile stimulus patterns to user’s back
○ User can use ...
1: Introduction - Demonstration
6
https://www.youtube.com/watch?v=sn6OEBBKsPQ
● P300 responses were confirmed (> 4 μV) in each channel
1: Introduction - fbBCI results (1)
7
Target
Non-Target
● Problem: Low online classification accuracies
○ SWLDA : 53.67 % (10 users average)
1: Introduction - fbBCI results (2)
8
● To improve the fbBCI classification accuracy
● To reconfirm the validity of fbBCI modality
1: Introduction - Research Pu...
● Test several signal preprocessing combinations ①
○ Downsampling
○ Epoch averaging
● Classify with three different machin...
2: Method - Signal Acquisition
11
● Event related potential (ERP)
○ captures 800 ms after an onset of vibrotactile stimulu...
2: Method - Signal Preprocessing(1)
12
● Downsampling (nd)
○ ERPs were decimated by
4 (128 Hz), 16 (32 Hz) or kept
intact ...
13
● Epoch averaging (ne)
○ ERPs were averaged using 5, 10
ERPs or no averaging
○ To cancel background noise
ne = 1 ne = 1...
● Concatenating all feature vectors
2: Method - Feature Extraction
ex.)
fs = 128 [Hz] (nd = 4)
Vlength = ceil(128・0.8) = 1...
● Machine learning methods
○ SWLDA
○ Linear SVM
… K(u,v’) = u v’
○ Non-linear SVM (Gaussian)
… K(u,v’) = exp(-γ||u-v|| )
γ...
● Training the classifier
2: Method - Classification (2)
16
VT1
VT2
VlengthALL VlengthALL
VN1
VN2
VTmax
・
・
・
・
・
・
VNmax
...
● Training the classifier
2: Method - Classification (2)
17
VT1
VT2
VlengthALL VlengthALL
VN1
VN2
Classifier (2cls)
VTmax
...
● Evaluation with the trained classifier
○ Same nd and ne were applied
2: Method - Classification (3)
18
VT1
VlengthALL
・
...
● SWLDA classification accuracies
○ BEST: 57.48 % (nd = 4, ne = 1)
3: Results - SWLDA
19
Signal decimation (nd)
● Linear SVM classification accuracies
○ BEST: 58.5 % (nd = 16, ne = 10)
3: Results - Linear SVM
20
Signal decimation (nd)
● Non-linear SVM classification accuracies
○ BEST: 59.83 % (nd = 4, ne = 1)
3: Results - Non-linear SVM
21
Signal decimati...
4: Discussion and conclusions
22
● fbBCI classification accuracy has been improved
○ Both nd and ne combinations were test...
23
Many thanks for your attention!
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Full–body Tactile P300–based Brain–computer Interface Accuracy Refinement

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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. 20-23.

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Full–body Tactile P300–based Brain–computer Interface Accuracy Refinement

  1. 1. 1 Takumi Kodama , Kensuke Shimizu , Shoji Makino and Tomasz M. Rutkowski Full–body Tactile P300–based Brain–computer Interface Accuracy Refinement @bioSMART conference 2016 1 1 1 2, 3, 4 1 2 3 4 Life Science Center of TARA, University of Tsukuba, The University of Tokyo, Saitama Institute of Technology, RIKEN Brain Science Institute
  2. 2. 1: Introduction - What’s BCI? ● Brain Computer Interface (BCI) ○ Neurotechnology ○ Exploits user intention ONLY using brainwaves 2
  3. 3. 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 3 ! … …
  4. 4. ● 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 A B A B 3. Predict user intention 92.0% 43.3% A B Target Non-Target P300 brainwave response
  5. 5. ● 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.
  6. 6. 1: Introduction - Demonstration 6 https://www.youtube.com/watch?v=sn6OEBBKsPQ
  7. 7. ● P300 responses were confirmed (> 4 μV) in each channel 1: Introduction - fbBCI results (1) 7 Target Non-Target
  8. 8. ● Problem: Low online classification accuracies ○ SWLDA : 53.67 % (10 users average) 1: Introduction - fbBCI results (2) 8
  9. 9. ● To improve the fbBCI classification accuracy ● To reconfirm the validity of fbBCI modality 1: Introduction - Research Purpose 9
  10. 10. ● 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 10 CommandBrainwave ① ②
  11. 11. 2: Method - Signal Acquisition 11 ● 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○○
  12. 12. 2: Method - Signal Preprocessing(1) 12 ● 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○○
  13. 13. 13 ● 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)
  14. 14. ● Concatenating all feature vectors 2: Method - Feature Extraction ex.) fs = 128 [Hz] (nd = 4) Vlength = ceil(128・0.8) = 103 14 … Vlength VCz … Vlength VPz … Vlength VCP6 … … … … ……V ex.) VlengthALL = Vlength・8 = 103・8 = 824 VlengthALL … Ch1 Ch2 Ch8
  15. 15. ● 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) 15 T 2
  16. 16. ● Training the classifier 2: Method - Classification (2) 16 VT1 VT2 VlengthALL VlengthALL VN1 VN2 VTmax ・ ・ ・ ・ ・ ・ VNmax ・ ・ ・ VTmax = 60 / ne VNmax = 300 / ne Classifier (2cls) Non-TargetTarget
  17. 17. ● Training the classifier 2: Method - Classification (2) 17 VT1 VT2 VlengthALL VlengthALL VN1 VN2 Classifier (2cls) VTmax ・ ・ ・ ・ ・ ・ VNmax VTmax = 60 / ne VNmax = 60 / ne Random choose as many as Tmax } Non-TargetTarget
  18. 18. ● Evaluation with the trained classifier ○ Same nd and ne were applied 2: Method - Classification (3) 18 VT1 VlengthALL ・ ・ VTmax = 10 / ne Target? or Non-Target? Classifier (2cls) Test data
  19. 19. ● SWLDA classification accuracies ○ BEST: 57.48 % (nd = 4, ne = 1) 3: Results - SWLDA 19 Signal decimation (nd)
  20. 20. ● Linear SVM classification accuracies ○ BEST: 58.5 % (nd = 16, ne = 10) 3: Results - Linear SVM 20 Signal decimation (nd)
  21. 21. ● Non-linear SVM classification accuracies ○ BEST: 59.83 % (nd = 4, ne = 1) 3: Results - Non-linear SVM 21 Signal decimation (nd)
  22. 22. 4: Discussion and conclusions 22 ● 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
  23. 23. 23 Many thanks for your attention!

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