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At least 1,243 records · Page 69Linked to original sources
PubMed1978

[EEG spectral analysis in normal children between the ages of 6 to 16. Choice and validation of the most significant parameters].

Numerous parameters have been proposed in order to describe the EEG power spectrum. Within the scope of an EEG study on the normal child between 6 and 16 years of age, the following problem had to be solved: given a group of 19 variables measured on 232 subjects in 5 situations, which of these variables best describe the population under examination. The algorithm proposed to answer this question is based on principal component analysis on the correlation matrix. Only 4 parameters are necessary to describe the EEG of a normal child of 6 to 16 years--these are Hjorth's parameters (activity, mobility and first order complexity) and alpha band power. They allow the observation of individual differences and the quantification for a given subject of the reaction to eyes opening, sensitivity to hyperventilation or even reactions that are difficult to perceive visually such as those resulting from doing arithmetic or vocabulary tests.

Adolescent↗
PubMed1982

Spectral analysis of visual potentials evoked by sine wave modulated light in migraine.

Visual evoked potentials to unstructured sine wave-modulated light were measured in 30 common migraine patients and 23 controls. The fundamental and the second harmonic components of the response were analysed separately in the frequency domain by power spectrum estimation. The absolute and relative powers of the fundamental component were significantly larger in the patients at 16-22 Hz stimulation both initially, in the onset phase, and later, in the steady-state phase, whereas those of the 2nd harmonic component were smaller in the later stages of stimulation. Significant difference occurred in the dynamics of the 2nd harmonic response between patients and controls. No significant difference was observed at 10 Hz stimulation.

Adult↗
PubMed1982

Classification of somatic evoked potentials through maximum entropy spectral analysis.

Maximum entropy (ME) spectra are currently considered to be superior to spectra calculated by means of Fast Fourier Transforms (FFT) because the former offer higher resolution. However, in terms of classification of somatic evoked potentials (SEPs), FFT spectra are found to be better than ME spectra. An even better set of parameters for discriminating SEPs is the set of reflection coefficients derived in the process of calculating the ME spectra. They permit separation of SEPs obtained from monkeys with dorsal column lesion from SEPs obtained from normal monkeys, as well as distinguishing between SEPs obtained from large nerve fiber stimulation and SEPs from stimulation of all nerve fibers.

Animals↗
PubMed1983

Spectral analysis of hippocampal unit train in relation to hippocampal EEG.

Sampling and analysis programs are implemented on a microcomputer for the computation of autopower, coherence and phase spectra of unit train and EEG. Application to the hippocampus reveals that both theta and fast activity in the EEG are correlated with different phase relationships with unit activity. The estimate of coherence and phase spectra of unit-EEG relationship has not been reported before and is unique for the present method.

Animals↗
PubMed1984

Sound pressure level measurement and spectral analysis of brief acoustic transients.

The sound pressure level (SPL) of an acoustic transient can be quantified in several ways. The SPL value obtained is dependent on measurement procedure, in addition to signal and transducer characteristics. The acoustic spectrum of a signal shows sound pressure as a function of frequency. The acoustic spectrum can be determined by the use of analog filtering or by Fourier transformation. A constant electrical signal can produce different acoustic spectra due to varying transfer functions across transducers. Signal center frequency, rise/fall time and plateau influence acoustic spectrum. Recording parameters, such as constant bandwidth versus logarithmic bandwidth filtering, or the time domain windowing function used prior to Fourier transformation, also influence the acoustic spectrum.

Acoustic Stimulation↗
PubMed1985

Spectral analysis of hippocampal EEG in the freely moving rat: effects of centrally active drugs and relations to evoked potentials.

Hippocampal EEG signals derived from chronically implanted electrodes in the freely moving rat were recorded before and after administration of centrally acting drugs, and analyzed by power and coherence spectra. Eserine, ether or urethane induced a low frequency (3-6 c/sec) theta power and coherence peak in the immobile rat, which was sensitive to atropine or scopolamine. After phencyclidine, theta that occurred during walking (7-8 c/sec) was virtually abolished by atropine while in the normal rat, absolute theta power was not affected by atropine. The residue spectrum, defined as the EEG spectrum with the theta harmonics removed, was sensitive to centrally acting drugs. Ether, urethane and pentobarbital suppressed fast waves of 50-100 c/sec, and under some conditions, enhanced 15-50 c/sec waves. Eserine enhanced (30-60 c/sec) fast waves during walking while atropine suppressed fast waves and increased irregular slow activity (less than 30 c/sec). The main effects of drugs and behavior on the residue spectra and on the average evoked potentials following stimulation of the Schaffer collaterals could be explained by a previously proposed model (Leung 1982) which suggests a continuum of hippocampal 'activation' (tonic input) under the various conditions.

Anesthetics↗
PubMed1995

Dynamic spectral analysis of event-related EEG data.

A method for analysing the time course of power spectra of event-related EEG data is presented. A sequence of autoregressive models is fitted to segments of the EEG within which the data exhibit local stationarity. For parameter estimation a method involving ensemble averages is introduced. Besides investigating the evolution of power spectra, time courses of peak frequency, bandwidth and power of alpha (mu) and beta rhythms are traced. The method is applied to EEG recorded over the primary motor area during self-paced finger movements.

Electroencephalography↗