A Contribution by Multidimensional Methods to Choosing Descriptive Data for EEG by Spectral Analysis

One of the main difficulties in automatic analysis of EEG tracings is the reduction of data; spectral analysis allows only a partial reduction. Principal component analysis is used to determine amongst a group of quantitative variables those which best describe a given sample of power spectra. The tracings come from normal children (10 to 12 years of age) recorded on four leads (fronto-central right and left, centro-occipital right and left) in five experimental situations (resting with eyes closed, during five minutes of hyperventilation, resting with eyes open, and during simple arithmetic and vocabulary tests, eyes closed). It is shown that four variables are enough to describe the spectra.

Eine der Hauptschwierigkeiten in der automatischen Analyse des EEG ist die Datenreduktion. Die spektrale Analyse bietet nur eine partielle Lösung. Die Hauptkomponentenanalyse wird benützt, um unter einer Gesamtheit von Variablen diejenigen zu bestimmen, die die beste Beschreibung eines Musters von Powerspektren ermöglichen.

EEGs von 10—12 Jahre alten normalen Kindern wurden auf vier Ableitungen in den fünf folgenden experimentalen Situationen aufgenommen : Ausruhen mit geschlossenen Augen, nach 5 Minuten langer Hyperventilation, Ausruhen mit geöffneten Augen und bei einfachen Rechen- und Wortschatztests. Es wird gezeigt, daß vier Variable zur Beschreibung der Spektren ausreichen.

Keywords

EEG - Power Spectra - Multivariate Analysis

Schlüsselwörter

EEG - Power-Spektrum - multivariate Analyse

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