Abstract
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. In this paper, we describe an automated method for deriving the two most common estimators of IAF: peak alpha frequency (PAF) and centre of gravity (CoG). These indices are calculated from resting-state power spectra that have been smoothed by a Savitzky-Golay filter (SGF). We evaluated the performance characteristics of this SGF analysis routine in both empirical and simulated EEG datasets. Application of the SGF technique to resting-state data from n = 63 healthy adults resulted in 61 PAF, and 62 CoG estimates. The statistical properties of these estimates were consistent with previous studies. Analysis of simulated electrophysiological signals revealed that the automated SGF routine reliably extracts target alpha components, even under relatively noisy spectral conditions. The routine consistently outperformed a simpler method of automated peak localisation 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 opportunity for improving the reliability and replicability of future IAF-related research.
Conflicts of interest: None.
Role of funding source: This work was partially supported by funding from the University of South Australia Ehrenberg-Bass Institute for Marketing Science. This funding supported AC while he collected and analysed the empirical EEG dataset reported in this manuscript. The Institute had no influence on the design, analysis, or interpretation of the reported study. AC is also supported by an Australian Government Research Training Program (RTP) Scholarship. IBS is supported by an Australian Research Council Future Fellowship (FT160100437).