Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
531280 | Pattern Recognition | 2010 | 15 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Alfonso Alba, José L. Marroquín, Edgar Arce-Santana, Thalía Harmony,