Towards a reliable, automated method of individual alpha frequency (IAF) quantification

Individual alpha frequency (IAF) is a promising electrophysiological marker of interindividual differences in cognitive function. IAF has been linked with trait-like differences in information processing and general intelligence, and provides an empirical basis for the definition of individualised frequency bands. Despite its widespread application, however, there is little consensus on the optimal method for estimating IAF, and many common approaches are prone to bias and inconsistency. Here, we describe an automated strategy for deriving two of the most prevalent IAF estimators in the literature: peak alpha frequency (PAF) and centre of gravity (CoG). These indices are calculated from resting-state power spectra that have been smoothed using a Savitzky-Golay filter (SGF). We evaluate the performance characteristics of this analysis procedure in both empirical and simulated EEG datasets. Applying the SGF technique to resting-state data from n = 63 healthy adults furnished 61 PAF, and 62 CoG estimates. The statistical properties of these estimates were consistent with previous reports. Simulation analyses revealed that the SGF routine was able to reliably extract target alpha components, even under relatively noisy spectral conditions. The routine consistently outperformed a simpler method of automated peak detection that did not involve spectral smoothing. The SGF technique is fast, open-source, and available in two popular programming languages (MATLAB and Python), and thus can easily be integrated within the most popular M/EEG toolsets (EEGLAB, FieldTrip and MNE-Python). As such, it affords a convenient tool for improving the reliability and replicability of future IAF-related research.