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

1. 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

2. 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

3. Outline • FIS algorithm • Motor Imagery classification using FIS • Evaluation of FIS • Conclusion COST B27 meeting, Swansea, 2006

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. FIS for Motor Imagery • Fuzzy Rules learnt for subject 1 • Contralateral Event Related Desynchronisation (ERD) observed COST B27 meeting, Swansea, 2006

12. 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

13. 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

14. Questions ? Fabien Lottefabien.lotte@irisa.fr COST B27 meeting, Swansea, 2006