a tactile p300-based brain-computer interface accuracy improvement

A Tactile P300-based Brain-computer Interface Accuracy Improvement

201420642 Takumi KodamaMultimedia Laboratory, Department of Computer ScienceSupervisors: Shoji Makino and Tomasz M. Rutkowski*

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*The university of Tokyo, Tokyo, Japan

@ Midterm Presentation for Master’s Degree on July, 2016

1: Introduction - What’s BCI?

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

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1: Introduction - ALS Patiens

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

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http://www.businessinsider.com/an-eye-tracking-interface-helps-als-patients-use-computers-2015-9

Dr. Hawkins

● Tactile (Touch) P300-based BCI paradigm○ P300 responses were evoked by external (touch) stimuli○ Predict user’s intentions with finding 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]○ Applied six vibrotactile stimulus patterns to user’s back○ User can use fbBCI with their body laying down

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.

● Full-body Tactile P300-based BCI (fbBCI)

1: Introduction - Demonstration

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

http://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: Not high Online classification accuracies○ SWLDA : 53.67 % (10 users average)

● not enough online results to assert fbBCI validity ...

1: Introduction - fbBCI results (2)

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● Improve the fbBCI classification accuracies● Affirm the potential validity of proposed 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

● 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

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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



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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



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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



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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



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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



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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

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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


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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

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● How to predict user’s intention with trained classifier?○ Wrong example


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

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

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(2)

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● Downsampling○ ERPs were decimated by

2 (256 Hz), 4 (128 Hz), 8 (64 Hz), 16 (32 Hz) or kept intact (512 Hz)

○ To reduce vector length Vlength

nd = 2 (256 Hz) nd = 16 (32 Hz)

Ch○○ Ch○○

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● Epoch averaging○ ERPs were averaged using 2, 5

or 10 ERPs, or no averaging○ To reject background noise

ne = 1 ne = 10

Ch○○ Ch○○


● Concatenating feature vectors

2: Method - Feature Extraction

ex.) fs = 256 [Hz] (nd = 2) Vlength = ceil(256・0.8) = 205

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…

Vlength

VCz …

Vlength

VPz …

Vlength

VCP6

…

…

… … ……Vex.) VlengthALL = Vlength・8 = 205・8 = 1640

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


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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


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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 trained classifier○ Same nd and ne were applied


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VT1

VlengthALL

・

・

VTmax = 10 / ne

Target? orNon-Target? Classifier (2cls)

Test data

● SWLDA classification accuracies○ BEST: 56.33 % (nd = 4, ne = 1)

3: Results - SWLDA

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Number of epoch averaging (ne)

Signal decimation (nd)

● Linear SVM classification accuracies○ BEST: 57.33 % (nd = 16, ne = 10)

3: Results - Linear SVM

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

3: Results - Non-linear SVM

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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)○ 57.33 % by linear SVM and 56.33 % 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 for a practical application

● [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. (submitted).

● [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. (submitted).

● [4] Kodama T, Makino S, Rutkowski TM. Toward a QoL improvement of ALS patients: Development of the Full-body P300-based Tactile Brain--Computer Interface. In: Proceedings of The AEARU Young Researchers International Conference (YRIC-2016). University of Tsukuba; 2016. p. (submitted).

Publications

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● [5] Shimizu K, Kodama T, Jurica P, Cichocki A, Rutkowski TM. Tactile BCI Paradigms for Robots’ Control. In: 6th Conference on Systems Neuroscience and Rehabilitation 2015 (SNR 2015). Tokorozawa, Japan; 2015. p. 28.

● [6] Rutkowski TM, Shimizu K, Kodama T, Jurica P, Cichocki A, Shinoda H. Controlling a Robot with Tactile Brain-computer Interfaces. In: Abstracts of the 38th Annual Meeting of the Japan Neuroscience Society - Neuroscience 2015. BMI/BCI. Kobe, Japan: Japan Neuroscience Society; 2015. p. 2P332.

● [7] Shimizu K, Aminaka D, Kodama T, Nakaizumi C, Jurica P, Cichocki A, et al. Brain-robot Interfaces Using Spatial Tactile and Visual BCI Paradigms - Brains Connecting to the Internet of Things Approach. In: The International Conference on Brain Informatics & Health - Type II Paper: Proceedings 2015. London, UK: Imperial College London; 2015. p. 9-10.

● [8] Rutkowski TM, Shimizu K, Kodama T, Jurica P, Cichocki A. Brain--robot Interfaces Using Spatial Tactile BCI Paradigms - Symbiotic Brain-robot Applications. In: Symbiotic Interaction. vol. 9359 of Lecture Notes in Computer Science. Switzerland: Springer International Publishing; 2015. p. 132-137.

Publications (co-author)

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Thanks for the listening!

● Bandpass filtering○ Set at 0.1 ~ 30 Hz (fixed)○ To limit noise of exciters (40 Hz)


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