Detection and quantification of planar traveling waves in the EEG using spherical phase fitting

BACKGROUND: Recent years have seen an increasing interest in traveling waves, i.e., oscillatory neural activity propagating over cortical space. The most common waves in the EEG are planar waves, i.e., synchronized wavefronts with a consistent direction, which have been linked to diverse perceptual and cognitive measures. However, their quantification has faced challenges due to the high dimensionality of the signal. Existing methods employ restrictive windows of analysis (e.g., lines of electrodes) or rely on wave motifs extracted from the data. NEW METHOD: We present a comprehensive analysis pipeline that specifically targets planar waves, comprising three steps: first, genuine oscillatory activity is extracted from the EEG as clusters. The spatial phase gradient is then fit using a spherical wave model. Finally, stable waves are extracted from the time series of best fits. RESULTS: We validate our method using simulations over a physiological range of parameters. Using a forward model, we test EEG wave detection for propagation along different pathways at the source level. Lastly, we apply our analysis to real EEG recordings, targeting alpha oscillations during visual stimulation and at rest. COMPARISON WITH EXISTING METHODS: Our method improves upon previous model-based approaches by offering greater resolution in time and propagation direction, and including spatial localization of the wave on the scalp. CONCLUSIONS: In summary, our method provides a reliable algorithm for detecting planar waves in the EEG. Given the emerging functional roles of traveling waves in perception and cognition, this could potentially be utilized in a wide range of future studies.