This study has compared four PAC algorithms (Canolty et al., 2006; Cohen, 2008; Tort et al., 2010b; Özkurt and Schnitzler, 2011), which are among the most commonly used approaches in sensory EEG/MEG research (Mathewson et al., 2011; Khan et al., 2013; Bonnefond and Jensen, 2015; Cho et al., 2015). However, these only comprise a small subset of the available algorithms designed to quantify PAC (Canolty and Knight, 2010; Hyafil et al., 2015). There have also been advances in measuring transient changes in PAC (Dvorak and Fenton, 2014), directed PAC (Jiang et al., 2015) and algorithms designed for spontaneous neural activity (Florin and Baillet, 2015; Weaver et al., 2016). A more comprehensive evaluation of algorithms and their application to real-world electrophysiological data is beyond the scope of this article, but would nevertheless benefit the field of cross-frequency coupling. Secondly, in order to detect alpha-gamma PAC within visual area V1, we used a broad filter bandwidth, defined as ±0.4 times the amplitude center-frequency. Consequently, the alpha-gamma comodulograms will be unable to differentiate between adjacent gamma sub-bands, which have been proposed to fulfill
