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WIN Wednesday Methods Series

Fluctuations in the activity of populations of neurons are observable as oscillations in electrophysiological recordings of brain activity. As a result, many oscillatory signatures have been proposed as markers for identifying, or tracking, disorders of brain function. For these markers to be viable, we need to know how individually variable simple oscillatory signals are in large, normative populations. Similarly, we need to know which demographic, physiological and acquisition factors need to be controlled for.

These investigations are becoming possible with increased accessibility of large, open access Electroencephalography (EEG) and Magnetoencephalography (MEG) datasets. I will introduce a new method for spectrum analysis that expands on Welch’s periodogram to allow covariate and confound regression at the level of individual datasets and large group analyses. I illustrate this analysis on a contrast between eyes open and eyes closed resting state in EEG, and on the effect of age across a large multi-site MEG dataset.