a novel single-trial analysis scheme for characterizing the presaccadic brain activity based on a...

A novel single-trial analysis schemefor characterizing

the presaccadic brain activitybased on a SON representation

K. Bozas, S.I. Dimitriadis, N.A. Laskaris, A. Tzelepi

AIIA-Lab, Informatics dept., Aristotle University of ThessalonikiICCS, National Technical University of Athens

OutlineIntroduction

-Every cognitive task is executed in a slightly different way each time, introducing single trial (ST) variability.

Methodology-ST variability is self-organized in patterns.-Brain is a complex system, it’s self-organization can be studied via EEG.-Network analysis examines relations between nodes in a graph.

Results

Conclusions

Saccade is a fast movement of eyes.

Electrooculogram (EOG) is the standard way to record eye movements.

The brain regions involved in saccadic control have not ,yet, been completely identified.

Here, we deal with normal saccades and the related pre-saccadic brain activity as recorded via EEG

Intro Method Results Conclusions

Motivation and problem statement

- Can we exploit the single trial variability (ST) observed in saccades?

Traditional approaches, like characterization via ERD/ERS, do not take into account ST variability.

Manifold learning techniques do.

Combined with network analysis, they can provide a framework to analyze brain’s functional connectivity.

As in many others cognitive tasks the execution of saccades is characterized by considerable single trial (ST) variability.


Outline of our methodology

Single-trial variability is utilized to introduce an implicit experimental control.

An approach to provide a detailed characterization of presaccadic brain activity.

Network analysis is performed for each group individually and the inter-group comparison reveals the essence of saccadic control mechanism

Based on EEG activity and the network of electrodesthe notion of functional connectivity graph (FCG) topology , is utilized to identify different modes of brain’s self organization.

Saccades are organized in groups of different velocity patterns.The associated brain activity is organized accordingly


Data acquisition: Go-No Go experiment

duration = 1000ms

duration = 2000ms (2500±500)

duration =2500±500ms

duration=2000ms

t

9 subjects64 EEG electrodesHorizontal and Vertical EOGTrial duration: 8 seconds7-9 runs, 40 trials per run70-90 trials for each condition

4 Conditions:•Go Right•No-Go Right•Go Left•No-Go Left


A single trial

2000 ms

2500±500 ms

Trigger #1

Trigger #{2,3,4,5}

Trigger #{6,7}

onset

1000 ms

Relax period

2500±500 ms

End of trial

8000ms

or

tLatencies

of interest


Saccadic onset detection in EOG signals

1. Calculate EOG velocity.

( ){ }2212 812

1+−+− −−+

∂= iiiii yyyy

ty

EOG (y) EOG velocity (yK )

2. We look back and forth in time using a linked double window.

,1

)()(

275

7525 thresholdtwm

twmlwmktfif onset >

+−=

we have detected a saccadic onset.

3.

According to D.E. Marple-Horvat et al. (1996) paper.


Introducing experimental controlEach saccade differs in execution speed.We attempt to organize the EOG velocity variations in prototypical patterns.

A self-organized artificial neural network, Neural-Gas, is employed to learn the ST-variability.

We end up with three control groups, corresponding toSLOW, FAST and VERY FAST saccades.


Neural-Gas algorithmNeural-Gas algorithm provides input space representations by constructing data summaries ( via prototypical vectors ).

Its a gradient descent procedure imitating gas dynamics within data space to calculate the prototypes.

)(1 tiki

ktik

tik e pxpp −⋅⋅+= −+ λε

Using the Voronoi-diagram of prototypical vectors, we classify each saccade to the closest prototype.


Applying Neural-GasST Segment to be fed in Neural-Gas (100ms) Neural-Gas for 3

prototypes

Append each saccade to the closest prototype and group the corresponding EEG trials accordingly.


Τhe functional connectivity graph (FCG) describes coordinated brain activity

-How do we identify the important variations in brain activity underlying the different velocity groups?

Considering the brain as a network, where neuronal groups (nodes) exchange information, we can model brain’s self-organization during saccade execution,by measuring information exchange efficiency among nodes.

In order to setup the FCG, we have to establish connectionsbetween the nodes (i.e. the 64 EEG electrodes).

Phase synchronization, is a mode of neural synchronization, that can be easily quantified through EEG signals.


Phase-locking Value (PLV)PLV quantifies the frequency-specific synchronization between two neuroelectric signals (Lachaux et. al. 1999).

∑=

=N

n

ntjt eN

PLV1

),(1 θ

We obtain the phase of each signal using the Hilbert transform.θ(t, n) is the phase difference φ1(t, n) - φ2(t, n) between the signals.

PLV measures the inter-trial variability of this phase difference at t. If the phase difference varies little across the trials, PLV is close to 1; otherwise is close to 0


PLV procedure for a pair of electrodes


Adopted from Lachaux et. al. 1999

0.9

0.6

Building the FCG

The process is repeated for every electrode, creating a complete graph.

Establishing links for a single electrode


Information exchange efficiency over the FCG

The network metric of local efficiency (Latora et. al. 2001) is employedto identity brain regions with high activity, and to model brain’s self-organization prior to a saccade.

∑∑

∈

≠∈

−

−=

Mi ii

ihjhjjh

loc kk

d

ME i

)1(

)(1 ,,

1

G

• ki corresponds to the total number of neighbors of the current node

• M is the set of all nodes in the FCG

• d keeps the shortest absolute path length between every possible pair in the neighborhood of the current node


Method

outline

The topography of the 64 individual efficiency values is potrayedfor different time-intervals before the saccade onset


Go vs. No-Go (S2)Beta band (13-30Hz)

High information exchange rate

Low information exchange rate

Go

No-Go


Go vs. No-Go (S6)Beta band (13-30Hz)

High information exchange rate

Low information exchange rate

Go

No-Go


Differences between velocity groups

Beta band (13-30Hz)

Early peaks and high efficiency in fast saccades.


ConclusionsWe have introduced a ST-analysis framework for modellingbrain’s self-organization during saccadic execution.

Our approach can be used to characterize EEG recorded brain activity,originating from any cognitive task.

Difficulties in the control of a task during an experiment, can be overcomed using ST self-organization.

Our methodology offers novel knowledge about the coding of kinematic parameters related to eye movements.

In the future, it can be used to study the neural activityrelated to the kinematics of arm movements in order to drive neural prostheses.


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