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Fuzzy Inference Systems for Brain-Computer Interfaces: a preliminary study

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### Fuzzy Inference Systems for Brain-Computer Interfaces: a preliminary study

COST B27 ENOC Joint WGs Meeting Swansea UK, 16-18 September 2006

Fabien Lotte

IRISA, Rennes, France

COST B27 meeting, Swansea, 2006

Introduction

- Identification of “brain activity patterns” achieved using various classifiers
- Neural Networks
- SVM…
- Fuzzy logic based classifiers scarcely used
- Fuzzy Inference Systems (FIS) not used for BCI despite they are
- Universal approximators [Wang92]
- Readable and extensible [Chiu97]
- Suitable for biomedical signals classification [Chan00][Bay03]

► Study of FIS for EEG-based BCI

COST B27 meeting, Swansea, 2006

Outline

- FIS algorithm
- Motor Imagery classification using FIS
- Evaluation of FIS
- Conclusion

COST B27 meeting, Swansea, 2006

FIS algorithm

- A FIS is
- a set of fuzzy “if-then” rules
- FIS algorithm considered: Chiu’s algorithm [Chiu97]
- Robust to noise
- Generally more accurate than Neural Networks
- Principle
- Learning the rules
- Classification using the fuzzy “if-then” rules

COST B27 meeting, Swansea, 2006

O

O

O

O

O

Learning (1)- Clustering of the data of each class separately

(substractive clustering)

3

X1

1

X

X

X

X

X

X

X

X

X

X

X

2

X

X

X

X

X2

COST B27 meeting, Swansea, 2006

O

O

O

O

O

Learning (2a)Gaussian fuzzy

membership function

- fuzzy rules generation

3

X1

1

X

X

A31

X

X

X

X

X

X

X

X

X

2

X

X

X

X

X2

A32

COST B27 meeting, Swansea, 2006

O

O

O

O

O

Learning (2b)- fuzzy rules generation

3

X1

1

X

X

A31

X

X

X

X

X

If X1 is A31and X2 is A32

Then

Class is O

X

X

X

X

2

X

X

X

X

X2

A32

COST B27 meeting, Swansea, 2006

O

O

O

O

O

Classification- Classification of an unseen vector B

3

If X1 is A11 and X2 is A12Then Class is X

A11(X1) * A12(X2) = 0.8 * 0.2 = 0 .16

If X1 is A21 and X2 is A22Then Class is X

A21(X1) * A22(X2) =0.01 * 0.9 = 0.009

If X1 is A31 and X2 is A32Then Class is O

A31(X1) * A32(X2) = 0.1 * 0.01 = 0.001

X1

1

X

X

A31

X

X

A11

X

X

X

B

X

X

X

X

A21

2

X

X

X

X

X2

A12

A22

A32

B belongs toX

COST B27 meeting, Swansea, 2006

Classification of motor imageryusing Fuzzy Inference System

- EEG Data Used [Vidaurre04][Leeb04]
- Source
- Data set IIIb of the BCI competition III (Graz)
- Protocol
- Imagination of left and right hand movements (2 classes)
- Recordings
- 2 electrodes: C3 and C4
- Signals band pass filtered between 0.5 and 30 Hz
- 3 subjects

COST B27 meeting, Swansea, 2006

Feature Extraction

- Band Power (BP) features employed
- Advantages
- Efficient, low dimensional, understandable
- Drawback
- The most reactive frequency bands need to be identified
- Reactive frequency bands identification
- Statistical paired t-test for each 2 Hz frequency band
- Reactive frequencies obtained: α and β (18-28 Hz) bands
- Feature vector obtained

[C3α, C3β, C4α, C4β]

COST B27 meeting, Swansea, 2006

FIS for Motor Imagery

- Fuzzy Rules learnt for subject 1
- Contralateral Event Related Desynchronisation (ERD) observed

COST B27 meeting, Swansea, 2006

Evaluation

- Comparison with classifiers widely used for BCI
- A MultiLayer Perceptron (MLP)
- A Gaussian Support Vector Machine (SVM)
- A Linear Classifier (LC)

COST B27 meeting, Swansea, 2006

Conclusion

- Exploration of Fuzzy Inference Systems for BCI systems
- Motor imagery data
- FIS were shown to be
- More accurate than a Linear Classifier
- As accurate as Neural Networks or SVM
- Readable
- FIS are suitable and useful for BCI design
- Integration of this work in the Open-ViBE project (France télécom, INRIA, INSERM, AFM)
- www.irisa.fr/siames/OpenViBE

COST B27 meeting, Swansea, 2006

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