Title :
A Model for Predicting the Reliability of EEG Power Spectral Density Estimates of Subcortical Spiking Frequency
Author_Institution :
Department of Electrical and Computer Engineering, Clemson University
Abstract :
A model for predicting the reliability of power spectral density (PSD) indicators of spike-to-spike intervals taken from EEG time series is proposed. The analysis shows that the PSD is corrupted by irregularities in spike spacing and that conditions are improved if a larger number of spikes are included in the EEG epoch.
Keywords :
Autocorrelation; Bandwidth; Brain modeling; Electroencephalography; Fourier transforms; Frequency domain analysis; Frequency estimation; Inspection; Predictive models; Spectral analysis; Electroencephalography; Humans; Models, Neurological;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.1979.326481
