UZH-Logo

Maintenance Infos

The analysis of the EEG


Gasser, Theo; Molinari, Luciano (1996). The analysis of the EEG. Statistical Methods in Medical Research, 5(1):67-99.

Abstract

The quantitative analysis of the electroencephalogram (EEG) relies heavily on methods of time series analysis. A quantitative approach seems indispensable for research (be it clinical or basic neurophysical research), but it can also be a useful information for purely clinical purposes. Apart from the ongoing spontaneous EEG, evoked potentials (EPs) also play an important role. They can be elicited by simple sensory stimuli or more complex stimuli. Their analysis requires methods which are different from those for the spontaneous EEG. Those methods operate usually in the time domain and offer many challenging problems to statisticians. Methods for analysing the spontaneous EEG usually work in the frequency domain in terms of spectra and coherences. Biomedical engineers who take care of the equipment are usually also trained in time series analysis. Thus, they have contributed much more to methodological progress for analysing EEGs and EPs, compared with statisticians. However, the availability of a sample of subjects, and the associated problems in modelling followed by an inferential analysis could make a larger influence from the statistical side quite profitable. This paper tries to give an overview of a fascinating area. In doing so we treat more extensively problems with some statistical appeal. This leads inevitably to some overlap with our own work.

Abstract

The quantitative analysis of the electroencephalogram (EEG) relies heavily on methods of time series analysis. A quantitative approach seems indispensable for research (be it clinical or basic neurophysical research), but it can also be a useful information for purely clinical purposes. Apart from the ongoing spontaneous EEG, evoked potentials (EPs) also play an important role. They can be elicited by simple sensory stimuli or more complex stimuli. Their analysis requires methods which are different from those for the spontaneous EEG. Those methods operate usually in the time domain and offer many challenging problems to statisticians. Methods for analysing the spontaneous EEG usually work in the frequency domain in terms of spectra and coherences. Biomedical engineers who take care of the equipment are usually also trained in time series analysis. Thus, they have contributed much more to methodological progress for analysing EEGs and EPs, compared with statisticians. However, the availability of a sample of subjects, and the associated problems in modelling followed by an inferential analysis could make a larger influence from the statistical side quite profitable. This paper tries to give an overview of a fascinating area. In doing so we treat more extensively problems with some statistical appeal. This leads inevitably to some overlap with our own work.

Citations

Altmetrics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:March 1996
Deposited On:24 Jun 2016 14:26
Last Modified:25 Jun 2016 08:07
Publisher:Sage Publications Ltd.
ISSN:0962-2802
PubMed ID:8743079

Download

Full text not available from this repository.

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations