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COST B27 ENOC Joint WGs Meeting Swansea UK, 16-18 September 2006. Fuzzy Inference Systems for Brain-Computer Interfaces: a preliminary study. Fabien Lotte IRISA, Rennes, France. Introduction. Identification of “brain activity patterns” achieved using various classifiers

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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 l.jpg

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

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

Fabien Lotte

IRISA, Rennes, France

COST B27 meeting, Swansea, 2006


Introduction l.jpg

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 l.jpg

Outline

  • FIS algorithm

  • Motor Imagery classification using FIS

  • Evaluation of FIS

  • Conclusion

COST B27 meeting, Swansea, 2006


Fis algorithm l.jpg

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


Learning 1 l.jpg

O

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


Learning 2a l.jpg

O

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


Learning 2b l.jpg

O

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


Classification l.jpg

O

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 imagery using fuzzy inference system l.jpg

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 l.jpg

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 l.jpg

FIS for Motor Imagery

  • Fuzzy Rules learnt for subject 1

  • Contralateral Event Related Desynchronisation (ERD) observed

COST B27 meeting, Swansea, 2006


Evaluation l.jpg

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 l.jpg

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


Questions l.jpg

Questions ?

Fabien [email protected]

COST B27 meeting, Swansea, 2006


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