1. EEG synchrony pattern segmentation for the exploratory analysis of cognitive experiments
Alfonso Alba1, José Luis Marroquín2, Edgar Arce1 1 Facultad de Ciencias, UASLP 2 Centro de Investigación en Matemáticas

2. 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.
These interactions are reflected as synchronization between EEG signals.
Varela et al., 2001

3. EEG synchrony data
Synchrony is measured at specific frequency bands for a given pair of electrode signals.
Typical procedure:
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.

4. Time-frequency (TF) map for a given electrode pair (T4-O2)
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.

5. Seeded region growing
TF regions with homogeneous SP’s can be segmented using a simple region growing algorithm, which basically:
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.

6. Seeded region growing

7. 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.

8. 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

9. Results (Figure categorization experiment)
Automatic segmentation
Regularized segmentation

10. Results

11. Future work Merge regions with similar RSP’s.
Apply methodology to segment amplitude maps.
Use segmented maps for the study of a psychophysiological experiment.

12. Thank you!