Segmentación de mapas de amplitud y sincronía para el estudio de tareas cognitivas Alfonso Alba 1, José Luis Marroquín 2, Edgar Arce 1 1 Facultad de Ciencias, UASLP 2 Centro de Investigación en Matemáticas

Introduction Electroencephalography (EEG) consists of voltage measurements recorded by electrodes placed on the scalp surface or within the cortex. Electrode cap Varela et al., 2001 During cognitive tasks, several areas of the brain are activated simultaneously and may even interact together.

EEG synchrony data Synchrony is measured at specific frequency bands for a given pair of electrode signals. Typical procedure: Band-pass filter electrode signals V e1 (t) and V e2 (t) around frequency f. Compute a correlation/synchrony measure  f,t,e1,e2 between the filtered signals Test the synchrony measure for statistical significance In particular, we obtain a class field c f,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 (Figure categorization experiment) The field c f,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. Computes a representative synchrony pattern (RSP) for each region (initially the SP corresponding to the seed). 2. 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. 3. Repeats the process until neither region can be expanded any further.

Region growing (Figures experiment)

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: l t,f is the label field L t,f is a pseudo-likelihood function N s is the number of electrode pairs V is the Ising potential function t and f are regularization parameters

Results (Figure categorization experiment) Automatic segmentationRegularized segmentation

Results (Figure categorization experiment)

Results with induced amplitude

Region optimization Merge regions with similar RSP’s Two regions i and j are merged if Delete small regions After merging, regions whose area is smaller than some  d are deleted.

Region optimization example

Conclusions We have developed a visualization system for EEG dynamics which Produces detailed representations of synchrony and amplitude patterns that may be relevant to the task. Helps neurophysiologists determine TF regions of possible interest. Can be fully automated and allows for human interaction.

Future work Validation Use of segmented maps for the study of a psychophysiological experiment. Segmentation using combined amplitude+synchrony data?

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