Marco Congedo, PhD France Telecom R&D

Classification of Movement Classification of Movement Intention Intention by Spatially Filtered Electromagnetic by Spatially Filtered Electromagnetic Inverse SolutionsInverse Solutions

Marco.Congedo@Gmail.comMarco.Congedo@Gmail.com

Conjunct COST B27 and SAN Scientific Meeting,Swansea, UK, 16-18 September 2006

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Introduction

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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.

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Peer-Rewiewed Articles on "Brain-Computer Interface" (Source: PUBMED)

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1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Year

Nu

mb

er o

f A

rtic

les

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Domains of Applications• Motor Handicap

World Human

Input (Sensory)

Output (Motor)

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"Aware Chair"(Georgia State University)

Text Editor(Helsinki University of Technology)

Examples of Current Applications for Motor Handicap

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Domains of Applications• Motor Handicap

• Human-Machine Interface• New Interfaces• Detection of User's Intention (Video-Games, TeleInteraction)

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Domains of Applications• Motor Handicap

• Human-Machine Interface

• Virtual Reality

• New Interfaces• Detection of User's Intention (Video-Games, TeleInteraction)

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Navigation in a Virtual Environment via a Head Mounted Display and a BCI

(University of Graz)

Example of Application of BCI for Virtual Reality

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Domains of Applications• Motor Handicap

• Human-Machine Interface

• Virtual reality• Robotics

• New Interfaces• Video-Games• Detection of User's Intention

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Implantation of MicroElettrodes

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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

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Method

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The Motor Cortex and the detection of Movement Intention

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The Motor "Homunculus"

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pyramidal cell

Section of aCortical Gyrus

Cerebral Cortex

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Pyramical Cells of the Motor Cortex

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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.

- 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).

• 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.

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Examples of EEG trial related to Movement Intention(from -630 ms. to -130ms. before movement onset)

Left Finger Right Finger

-630 ms -130 ms Periodogram AutoCorrelation

Fron

tal

Site

sO

ccipita

l S

ites

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Data Processing (Schematic Representation)

Band-Pass Filtering

Projection on the Beamspace(Spatial Filtering)

Source Power EstimationIn the Regions of Interest

(sLORETA)

Classification

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Band-Pass Filtering

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

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Band-Pass Filtering

Threshold ofsignificance

minima

maxima

(maxima – minima)/2

Maximal and minimal absolute t-statistic across the volume for each frequency bin and their relation with the threshold of significance

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Spatial Filtering (Common Spatial Pattern)

max ; maxT T

L RT T

R L

a b

a V a b V ba V a b Vb

Problem:

Solution:First and last d vectors of the Joint Diagonalizer ofLV and

RV

T

TL

TR

F V F I

F V F W

F V F I W

where I is the identity matrix, VΣ = VL+VR,W=diag(W1≥W2≥…≥WN-1) andI-W=diag(1-W1≤1-W2≤…≤1-WN-1).

satisfying:

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sLORETA Source Power of the Filter Spatial Patterns

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3

4

5

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25

26

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The 28 scalp coefficients are given as the 27 columns of T G F

…

…

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Actual Filter Employed

27 26R F f f

where df is the unith norm dth column vector of F.

1 2L F f f Filter for Left Motor Cortex

Filter for Left Motor Cortex

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sLORETA Source Power Estimation

TL L Ltr H VH T T T

L L L L L L Ltr H F F VF F H

T T TR R R R R R Rtr H F F VF F H

Unfiltered sLORETA Filtered sLORETA

LEFT

Moto

r C

orte

x

RIG

HT

Moto

r C

orte

x

TR R Rtr H VH

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Filtered Source power of Left and Right finger movement intention grand average training trials

Legend: R=Right; L=Left; A=Anterior; P=Posterior; S=Superior; I=Inferior.

Left

trials

Rig

ht

trials

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Classification

classify trial as finger movement intention if

classify trial as finger movement intention if

L R

L R

left

right

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Right Finger Movement Intention Trials

Left Finger Movement Intention Trials

Training Set (N=316) Test Set (N=100)

Results

Ou

r M

eth

od

Un

filte

red

sLO

RE

TA

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Discussion

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• The Classifier is Untrained

Advantages of the Method

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• The Classifier is Untrained

Advantages of the Method

• Adapt to Invividual Characteristics

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• The Classifier is Untrained

Advantages of the Method

• Adapt to Invividual Characteristics• Processing Speed

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• The Classifier is Untrained

Advantages of the Method

• Adapt to Invividual Characteristics• Processing Speed

• Non Invasiveness

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End…

AcknowledgmentsThis 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.

Marco.Congedo@Gmail.com

ReferenceCongedo M., Lotte, F, Lécuyer, A. (2006),

Classification of Movement Intention by Spatially Filtered Electromagnetic Inverse Solutions, Physics in Medicine and Biology, 51, 1971-1989.

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