Maximum a posteriori estimation of change points in the EEG.
A new approach for EEG segmentation is introduced. This is based on a methodology for optimal segmentation of non-stationary signals derived from the maximum a posteriori estimation principle. It is a model-based, not sequential approach that allows for segmentation at different resolution levels. The features of the methodology are illustrated by its application to EEG recordings containing several types of spectral changes due to normal and pathological variations of spontaneous brain rhythmic activities, as well as physiological artifacts.
Biscay
R
R
Cuban Neuroscience Center, Havana.
Lavielle
M
M
González
A
A
Clark
I
I
Valdés
P
P
D000465:Algorithms; D000513:Alpha Rhythm / Q000706:statistics & numerical data; D001143:Arousal / Q000502:physiology; D016477:Artifacts; D001499:Bayes Theorem; D001611:Beta Rhythm / Q000706:statistics & numerical data; D001767:Blinking / Q000502:physiology; D001921:Brain / Q000502:physiology; D004569:Electroencephalography / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis