A new segmentation method of electroencephalograms by use of Akaike's information criterion

Non-stationary EEGs, whose statistical properties change with time, were segmented into stationary segments to closely track the behavior of EEG characteristics. We have developed a new segmentation method of optimizing segmentation parameters by using AIC (Akaike's information criterion) as an...

Full description

Saved in:
Bibliographic Details
Published inBrain research. Cognitive brain research Vol. 3; no. 1; pp. 33 - 40
Main Authors Inouye, Tsuyoshi, Toi, Seigo, Matsumoto, Yuko
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.12.1995
Elsevier Science
Subjects
Adolescent
Adult
Akaike's information criterion
Behavioral psychophysiology
Biological and medical sciences
Brain - physiology
Electroencephalogram
Electroencephalography - methods
Female
Fundamental and applied biological sciences. Psychology
Humans
Male
Methods
Models, Biological
Non-stationary EEG analysis
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Segmentation
Wavelet analysis
Wavelet analysis
Non-stationary EEG analysis
Akaike's information criterion
Electroencephalogram
Segmentation
Separation method
Methodology
Investigation method
Electrophysiology
Cognition
Electroencephalography
Mental activity
Spectral analysis
Online AccessGet full text

Cover

More Information
Summary:Non-stationary EEGs, whose statistical properties change with time, were segmented into stationary segments to closely track the behavior of EEG characteristics. We have developed a new segmentation method of optimizing segmentation parameters by using AIC (Akaike's information criterion) as an objective criterion. We applied the segmentation method to EEGs. The instantaneous power spectra of EEGs estimated with wavelet transform were compared with the segmented EEGs. EEGs were recorded from F3, F4, C3, C4, P3, P4, 01 and 02 in 13 normal subjects. Artifact-free 15-s epochs were taken at each electrode location. Each epoch was divided into stationary segments, consisting of several fixed intervals, by optimizing 2 segmentation parameters (interval length and starting point) so that the sum of AICs for several sequences of segments could be the smallest. The EEG segmentation could represent differences in the power spectra between segments. The average length of segments during relaxed wakefulness was 6.0 ± 3.8 s. The EEG segmentation during mental arithmetic could detect the start of mental arithmetic.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0926-6410
DOI:10.1016/0926-6410(95)00016-X