Transcript Slide 1

A novel single-trial analysis scheme
for characterizing
the presaccadic brain activity
based on a SON representation
K. Bozas, S.I. Dimitriadis, N.A. Laskaris, A. Tzelepi
AIIA-Lab, Informatics dept., Aristotle University of Thessaloniki
ICCS, National Technical University of Athens
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Outline
Introduction
-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
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Intro
Method
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
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Intro
Method
Results
Conclusions
Motivation and problem statement
As in many others cognitive tasks
the execution of saccades is characterized by considerable
single trial (ST) variability.
- 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.
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Intro
Method
Results
Conclusions
Outline of our methodology
An approach to provide a detailed characterization
of presaccadic brain activity.
Single-trial variability is utilized to introduce
an implicit experimental control.
Saccades are organized in groups of different velocity patterns.
The associated brain activity is organized accordingly
Based on EEG activity and the network of electrodes
the notion of functional connectivity graph (FCG) topology ,
is utilized to identify different modes of brain’s self organization.
Network analysis is performed for each group individually
and the inter-group comparison reveals
the essence of saccadic control mechanism
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Intro
Method
Results
Conclusions
Data acquisition: Go-No Go experiment
duration = 2000ms (2500500)
duration = 1000ms
duration
=2500500ms
t
duration=2000ms
4 Conditions:
•Go Right
•No-Go Right
•Go Left
•No-Go Left
9 subjects
64 EEG electrodes
Horizontal and Vertical EOG
Trial duration: 8 seconds
7-9 runs, 40 trials per run
70-90 trials for each condition
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Intro
Method
Results
Conclusions
A single trial
or
Trigger #{6,7}
Relax period
Trigger #{2,3,4,5}
End of trial
1000 ms
2500±500 ms
Trigger #1
2500±500 ms
onset
2000 ms
8000ms
t
Latencies
of interest
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Intro
Method
Results
Conclusions
Saccadic onset detection in EOG signals
According to D.E. Marple-Horvat et al. (1996) paper.
1. Calculate EOG velocity.
EOG (y)
EOG velocity (dy/dt)
2. We look back and forth in time
using a linked double window.
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yi 2  8 yi 1  yi 2   yi  2 
y i 
12 t
3.
if
f onset (t )  k
(lwm25  twm75 )
1  2 twm75
 threshold,
we have detected a saccadic onset.
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Intro
Method
Results
Conclusions
Introducing experimental control
Each 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 to
SLOW, FAST and VERY FAST saccades.
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Intro
Method
Results
Conclusions
Neural-Gas algorithm
Neural-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.
p
t 1
ik
 p  e
t
ik
k

 (x i  p tik )
Using the Voronoi-diagram of prototypical vectors,
we classify each saccade to the closest prototype.
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Intro
Method
Results
Conclusions
Applying Neural-Gas
ST 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.
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Intro
Method
Results
Conclusions
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 connections
between the nodes (i.e. the 64 EEG electrodes).
Phase synchronization, is a mode of neural synchronization,
that can be easily quantified through EEG signals.
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Intro
Method
Results
Conclusions
Phase-locking Value (PLV)
PLV quantifies the frequency-specific synchronization between
two neuroelectric signals (Lachaux et. al. 1999).
1
PLVt 
N
N
j ( t , n )
e

n 1
We obtain the phase of each signal using the Hilbert transform.
(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
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Intro
Method
Results
Conclusions
PLV procedure
for a pair of electrodes
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Intro
Method
Results
Conclusions
Building the FCG
Establishing links
for a single electrode
0.9
0.6
The process is repeated for every electrode,
creating a complete graph.
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Intro
Method
Results
Conclusions
Information exchange efficiency over the FCG
The network metric of local efficiency (Latora et. al. 2001) is employed
to identity brain regions with high activity,
and to model brain’s self-organization prior to a saccade.
1
(
d
)
 jh
1
j , hG i j , h  i
Eloc 

M iM ki (ki  1)
• 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
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Intro
Method
Results
Conclusions
The topography of the 64 individual efficiency values is potrayed
for different time-intervals before the saccade onset
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Intro
Method
Go vs. No-Go (S2)
Results
Conclusions
High information
exchange rate
Beta band (13-30Hz)
Go
No-Go
Low information exchange rate
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Intro
Method
Go vs. No-Go (S6)
Results
Conclusions
High information
exchange rate
Beta band (13-30Hz)
Go
No-Go
Low information exchange rate
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Intro
Method
Results
Conclusions
Differences between velocity groups
Beta band (13-30Hz)
Early peaks and high efficiency in fast saccades.
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Intro
Method
Results
Conclusions
Conclusions
We have introduced a ST-analysis framework for modelling
brain’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 activity
related to the kinematics of arm movements
in order to drive neural prostheses.
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