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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

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
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
Outline
  • FIS algorithm
  • Motor Imagery classification using FIS
  • Evaluation of FIS
  • Conclusion

COST B27 meeting, Swansea, 2006

fis algorithm
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

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

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

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

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
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
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
FIS for Motor Imagery
  • Fuzzy Rules learnt for subject 1
  • Contralateral Event Related Desynchronisation (ERD) observed

COST B27 meeting, Swansea, 2006

evaluation
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
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
Questions ?

Fabien [email protected]

COST B27 meeting, Swansea, 2006

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