Calibrating rhythmic stimulation parameters to individual EEG markers: the consistency of individual alpha frequency in practical lab settings.

Dataset

Description

Rhythmic stimulation can be applied to modulate neuronal oscillations. Such ‘entrainment’ is optimized when stimulation frequency is individually-calibrated based on magneto/encephalography markers. It remains unknown how consistent such individual markers are across days/sessions, within a session, or across cognitive states, hemispheres, and estimation methods, especially in a realistic, practical, lab setting. We here estimated individual alpha frequency (IAF) repeatedly from short EEG measurements at rest or during an attention task (cognitive state), using single parieto-occipital electrodes in 24 participants on four days (between-sessions), with multiple measurements over an hour on one day (within-session). First, we introduce an algorithm to automatically reject power spectra without a sufficiently clear peak to ensure unbiased IAF estimations. Then we estimated IAF via the traditional ‘maximum’ method and a ‘Gaussian fit’ method. IAF was reliable within- and between-sessions for both cognitive states and hemispheres, though task-IAF estimates tended to be more variable. Overall, the ‘Gaussian fit’ method was more reliable than the ‘maximum’ method. Furthermore, we evaluated how far from an approximated ‘true’ task-related IAF the selected ‘stimulation frequency’ was, when calibrating this frequency based on a short rest-EEG, a short task-EEG, or simply selecting 10Hertz for all participants. For the ‘maximum’ method, rest-EEG calibration was best, followed by task-EEG, and then 10 Hertz. For the ‘Gaussian fit’ method, rest-EEG and task-EEG-based calibration were similarly accurate, and better than 10 Hertz. These results lead to concrete recommendations about valid, and automated, estimation of individual oscillation markers in experimental and clinical settings.

Terms of reuse

CC0 Public Domain Dedication
Date made available1 Sep 2021
PublisherDataverseNL

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