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 individualized 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 center 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 data sets. 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, Python), and thus can easily be integrated within the most popular M/EEG toolsets (EEGLAB, FieldTrip, MNE-Python). As such, it affords a convenient tool for improving the reliability and replicability of future IAF-related research.
Highlights
Alpha is the dominant rhythm in the human EEG, and its importance for cognitive processing has been recognised since Hans Berger’s seminal work in the early 20th century
Individual alpha frequency (IAF) tends to decrease with age from young adulthood onwards (Chiang, Rennie, Robinson, Albada, & Kerr, 2011; Köpruner, Pfurtscheller, & Auer, 1984), lifelong changes in IAF accompany the decline of many cognitive abilities in older adulthood (e.g. Hedden & Gabrieli, 2004; Salthouse, 2011)
MinP, the minimum power value that a local maximum must exceed to qualify as a peak candidate;
Summary
Alpha is the dominant rhythm in the human EEG, and its importance for cognitive processing has been recognised since Hans Berger’s seminal work in the early 20th century (cf. Adrian & Matthews, 1934; Berger, 1929). More recent research has revealed that IAF predicts performance on a variety of perceptual (e.g., Cecere, Rees, & Romei, 2015; Samaha & Postle, 2015) and cognitive (e.g., Bornkessel, Fiebach, Friederici, & Schlesewsky, 2004; Klimesch, Doppelmayr, & Hanslmayr, 2006) tasks. IAF is a trait-like characteristic of the human EEG (Grandy et al, 2013b), which shows high heritability (Lykken, Tellegen, & Thorkelson, 1974; Malone et al, 2014; Smit, Wright, Hansell, Geffen, & Martin, 2006) and test-retest reliability (Gasser, Bächer, & Steinberg, 1985; Kondacs & Szabo, 1999; Näpflin, Wildi, & Sarnthein, 2007). IAF tends to decrease with age from young adulthood onwards (Chiang, Rennie, Robinson, Albada, & Kerr, 2011; Köpruner, Pfurtscheller, & Auer, 1984), lifelong changes in IAF accompany the decline of many cognitive abilities in older adulthood (e.g. Hedden & Gabrieli, 2004; Salthouse, 2011)
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