EEG synchrony pattern segmentation
for the exploratory analysis of
cognitive experiments
Alfonso Alba1, José Luis Marroquín2, Edgar Arce1
Facultad de Ciencias, UASLP
2 Centro de Investigación en Matemáticas
1
Introduction
Electroencephalography (EEG)
consists of voltage measurements
recorded by electrodes placed on the
scalp surface or within the cortex.
Electrode cap
• During cognitive tasks, several areas
of the brain interact together.
Varela et al., 2001
• These interactions are reflected as
synchronization between EEG
signals.
EEG synchrony data
Synchrony is measured at specific frequency
bands for a given pair of electrode signals.
Typical procedure:
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Band-pass filter electrode signals Ve1(t) and
Ve2(t) around frequency f.
Compute a correlation/synchrony measure
mf,t,e1,e2 between the filtered signals
Test the synchrony measure for statistical
significance
In particular, we obtain a class field cf,t,e1,e2
which indicates if synchrony was significantly
higher (c=1), lower (c=-1) or equal (c=0)
than the average during a neutral condition.
Visualization
The field cf,t,e1,e2 can be partially visualized in various ways:
Multitoposcopic display of the
synchronization pattern (SP) at a
given time and frequency
Time-frequency (TF) map for a
given electrode pair (T4-O2)
Time-frequency-topography (TFT)
histogram of synchrony increases at
each electrode
• The TFT histogram shows regions with homogeneous
synchronization patterns. These may be related to specific
neural processes.
Seeded region growing
TF regions with homogeneous SP’s can be
segmented using a simple region growing algorithm,
which basically:
1.
2.
3.
Computes a representative synchrony pattern (RSP) for
each region (initially the SP corresponding to the seed).
Takes a pixel from some region’s border and compares its
neighbors against the region’s RSP. If they are similar
enough, the neighbors are included in the region and the
RSP is recomputed.
Repeats the process until neither region can be expanded
any further.
Seeded region growing
Automatic seed selection
An unlabeled pixel is a good candidate for a
seed if it is similar to its neighbors, and all of
its neighbors are also unlabeled.
To obtain an automatic segmentation, choose
the seed which best fits the criteria above,
grow the corresponding region, and repeat
the procedure.
Bayesian regularization
The regions obtained by region-growing show
very rough edges and require regularization.
We apply Bayesian regularization by
minimizing the following energy function:
lt,f is the label field
Lt,f is a pseudo-likelihood function
Ns is the number of electrode pairs
V is the Ising potentia function
lt and lf are regularization parameters
Results
(Figure categorization experiment)
Automatic segmentation
Regularized segmentation
Results
Future work
Merge regions with similar RSP’s.
Apply methodology to segment
amplitude maps.
Use segmented maps for the study of a
psychophysiological experiment.
Thank you!