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Journal Article

Calibrating rhythmic stimulation parameters to individual electroencephalography markers: The consistency of individual alpha frequency in practical lab settings

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Ten Oever,  Sanne
Language and Computation in Neural Systems, MPI for Psycholinguistics, Max Planck Society;
Maastricht University;
FC Donders Centre for Cognitive Neuroimaging , External Organizations;

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data and analysis code
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Supplementary Material (public)
Citation

Janssens, S. E. W., Sack, A. T., Ten Oever, S., & Graaf, T. A. (2022). Calibrating rhythmic stimulation parameters to individual electroencephalography markers: The consistency of individual alpha frequency in practical lab settings. European Journal of Neuroscience, 55(11/12), 3418-3437. doi:10.1111/ejn.15418.


Cite as: http://hdl.handle.net/21.11116/0000-0009-262C-C
Abstract
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 electroencephalography (EEG) measurements at rest or during an attention task (cognitive state), using single parieto-occipital electrodes in 24 participants on 4 days (between-sessions), with multiple measurements over an hour on 1 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 10 Hz for all participants. For the ‘maximum’ method, rest-EEG calibration was best, followed by task-EEG, and then 10 Hz. For the ‘Gaussian fit’ method, rest-EEG and task-EEG-based calibration were similarly accurate, and better than 10 Hz. These results lead to concrete recommendations about valid, and automated, estimation of individual oscillation markers in experimental and clinical settings.