Classification and interactive segmentation of EEG synchrony patterns

This paper presents a novel methodology for the exploratory analysis of power and synchronization patterns in EEG data from psychophysiological experiments. The methodology is based on the segmentation of the time–frequency plane in regions with relatively homogeneous synchronization patterns, which...

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Bibliographic Details
Published inPattern recognition Vol. 43; no. 2; pp. 530 - 544
Main Authors Alba, Alfonso, Marroquín, José L., Arce-Santana, Edgar, Harmony, Thalía
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.02.2010
Subjects
Bayesian regularization
EEG synchrony
Electroencephalograpy
Interactive segmentation
Psychophysiological experiment
Seeded region growing
Seeded region growing
Electroencephalograpy
Psychophysiological experiment
Interactive segmentation
EEG synchrony
Bayesian regularization
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Summary:This paper presents a novel methodology for the exploratory analysis of power and synchronization patterns in EEG data from psychophysiological experiments. The methodology is based on the segmentation of the time–frequency plane in regions with relatively homogeneous synchronization patterns, which is performed by means of a seeded region-growing algorithm, and a Bayesian regularization procedure. We have implemented these methods in an interactive application for the study of cognitive experiments, although some of the techniques discussed in this work can also be applied to other multidimensional data sets. To demonstrate our methodology, results corresponding to a figure and word categorization EEG experiment are presented.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2009.03.005