Robust, automatic real-time monitoring of the time course of the individual alpha frequency in the time and frequency domain

We analyzed three different approaches to automatic real-time monitoring of the time course of individual alpha frequencies (IAFs) of the human electro-encephalograms. Fast Fourier transform and wavelet transform were compared to classical automated cycle counting in the time domain. With fast Fourier and wavelet transform, test results with healthy adult subjects, demented and psychiatric patients revealed typical short-term variations of the instantaneous IAFs of about ± 2 Hz. When cycles were counted in the time domain, however, variations of only ± 1 Hz were recorded. Thus, IAF measurement in the time domain appears to be particularly suitable. We also observed long-term IAF trends that typically amounted to about ± 0.5 to ± 1.0 Hz. Therefore, our hypothesis is that the IAF does not constitute an intra-individual constant but varies with time and cognitive state. Our fully automatic real-time signal-processing procedure includes pre-processing for artifact detection and for localization of segments with synchronized alpha oscillations where the IAF should preferably be measured.