Marco Congedo, PhD France Telecom R&D

1. Conjunct COST B27 and SAN Scientific Meeting, Swansea, UK, 16-18 September 2006 Classification of Movement Intention by Spatially Filtered Electromagnetic Inverse Solutions Marco Congedo, PhDFrance Telecom R&D Marco.Congedo@Gmail.com

2. Introduction

3. What is a BCI? A BCI is a system that allows humans to transmit bits of information without making use of any motor activity. This is achieved by detection and classification of discrete brain events.

5. Domains of Applications • Motor Handicap Input (Sensory) World Human Output (Motor)

6. Examples of Current Applications for Motor Handicap "Aware Chair" (Georgia State University) Text Editor (Helsinki University of Technology)

7. Domains of Applications • Motor Handicap • Human-Machine Interface • New Interfaces • Detection of User's Intention • (Video-Games, TeleInteraction)

8. Domains of Applications • Motor Handicap • Human-Machine Interface • New Interfaces • Detection of User's Intention • (Video-Games, TeleInteraction) • Virtual Reality

9. Example of Application of BCI for Virtual Reality Navigation in a Virtual Environment via a Head Mounted Display and a BCI (University of Graz)

10. Domains of Applications • Motor Handicap • Human-Machine Interface • New Interfaces • Video-Games • Detection of User's Intention • Virtual reality • Robotics

11. Implantation of MicroElettrodes

12. Implantation of MicroElettrodes • Advantages: • Bypass the low-pass filter enforced by the cranial bones • Small Neuronal Population Recording (High Spatial Resolution) • 24h Data Availability • Disadvantages: • Invasive

13. Method

14. The Motor Cortex and the detection of Movement Intention

15. The Motor "Homunculus"

16. Cerebral Cortex Section of a Cortical Gyrus pyramidal cell

17. Pyramical Cells of the Motor Cortex

18. Subjects and Procedures • Subject: one non-clinical subject during a self-paced key pressing task. • Task: press with the index and little fingers keys using either the left or right hand, in a self-paced timing and self-chosen order. • Protocol: three sessions of six minutes each, with a few minutes of break between sessions. • EEG Data: BCI Competition 2003, Data-Set IV • (Blankertz et al, 2004) • - EEG was acquired at 28 leads (F3, F1, Fz, F2, F4, FC5, FC3, FC1, FCz, FC2, FC4, FC6, C5, C3, C1, Cz, C2, C4, C6, CP5, CP3, CP1, CPz, CP2, CP4, CP6, O1, O2) with a 1000 Hz sampling rate. - Epochs of 500 ms were extracted ending 130 ms before the key press. - The epochs were divided in a training set and a test set (316 and 100).

19. Examples of EEG trial related to Movement Intention (from -630 ms. to -130ms. before movement onset) Left Finger Right Finger Frontal Sites Occipital Sites -130 ms -630 ms Periodogram AutoCorrelation

20. Data Processing (Schematic Representation) Band-Pass Filtering Projection on the Beamspace (Spatial Filtering) Source Power Estimation In the Regions of Interest (sLORETA) Classification

21. Band-Pass Filtering T-tests of Left vs. Right Finger Movement Intention (N= 159 Left Fingers trials + 157 Right Fingers trials.

22. Band-Pass Filtering maxima Threshold of significance minima (maxima – minima)/2 Maximal and minimal absolute t-statistic across the volume for each frequency bin and their relation with the threshold of significance

23. Spatial Filtering(Common Spatial Pattern) Problem: First and last d vectors of the Joint Diagonalizer of Solution: and satisfying: where I is the identity matrix, VΣ= VL+VR, W=diag(W1≥W2≥…≥WN-1) and I-W=diag(1-W1≤1-W2≤…≤1-WN-1).

24. sLORETA Source Power of the Filter Spatial Patterns … 1 23 2 24 3 25 4 26 5 27 … The 28 scalp coefficients are given as the 27 columns of

25. Actual Filter Employed Filter for Left Motor Cortex Filter for Left Motor Cortex is the unith norm dthcolumn vector of F. where

26. sLORETA Source Power Estimation Unfiltered sLORETA Filtered sLORETA LEFT Motor Cortex RIGHT Motor Cortex

27. Filtered Source power of Left and Right finger movement intention grand average training trials Left trials Right trials Legend: R=Right; L=Left; A=Anterior; P=Posterior; S=Superior; I=Inferior.

28. Classification

29. Results Training Set (N=316) Test Set (N=100) Right Finger Movement Intention Trials Our Method Left Finger Movement Intention Trials Unfiltered sLORETA

30. Discussion

31. Advantages of the Method • The Classifier is Untrained

32. Advantages of the Method • The Classifier is Untrained • Adapt to Invividual Characteristics

33. Advantages of the Method • The Classifier is Untrained • Adapt to Invividual Characteristics • Processing Speed

34. Advantages of the Method • The Classifier is Untrained • Adapt to Invividual Characteristics • Processing Speed • Non Invasiveness

35. End… Reference Congedo M., Lotte, F, Lécuyer, A. (2006), Classification of Movement Intention by Spatially Filtered Electromagnetic Inverse Solutions, Physics in Medicine and Biology, 51, 1971-1989. Contacts Marco.Congedo@Gmail.com Acknowledgments This Research has been partially funded by the French National Research Agency within the project Open-ViBE (Open Platform for Virtual Brain Environments), and by Nova Tech EEG, Inc., Knoxville, TN.