Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/35634
Appears in Collections:Psychology Journal Articles
Peer Review Status: Refereed
Title: Developmental changes in individual alpha frequency: Recording EEG data during public engagement events
Author(s): Turner, Christopher
Baylan, Satu
Bracco, Martina
Cruz, Gabriela
Hanzal, Simon
Keime, Marine
Kuye, Isaac
McNeill, Deborah
Ng, Zika
van der Plas, Mircea
Ruzzoli, Manuela
Thut, Gregor
Trajkovic, Jelena
Veniero, Domenica
Wale, Sarah P.
Whear, Sarah
Learmonth, Gemma
Contact Email: gemma.learmonth@stir.ac.uk
Issue Date: 10-Aug-2023
Date Deposited: 29-Nov-2023
Citation: Turner C, Baylan S, Bracco M, Cruz G, Hanzal S, Keime M, Kuye I, McNeill D, Ng Z, van der Plas M, Ruzzoli M, Thut G, Trajkovic J, Veniero D, Wale SP, Whear S & Learmonth G (2023) Developmental changes in individual alpha frequency: Recording EEG data during public engagement events. <i>Imaging Neuroscience</i>, 1, pp. 1-14. https://doi.org/10.1162/imag_a_00001
Abstract: Statistical power in cognitive neuroimaging experiments is often very low. Low sample size can reduce the likelihood of detecting real effects (false negatives) and increase the risk of detecting non-existing effects by chance (false positives). Here, we document our experience of leveraging a relatively unexplored method of collecting a large sample size for simple electroencephalography (EEG) studies: by recording EEG in the community during public engagement and outreach events. We collected data from 346 participants (189 females, age range 6-76 years) over 6 days, totalling 29 hours, at local science festivals. Alpha activity (6-15 Hz) was filtered from 30 seconds of signal, recorded from a single electrode placed between the occipital midline (Oz) and inion (Iz) while the participants rested with their eyes closed. A total of 289 good-quality datasets were obtained. Using this community-based approach, we were able to replicate controlled, lab-based findings: individual alpha frequency (IAF) increased during childhood, reaching a peak frequency of 10.28 Hz at 28.1 years old, and slowed again in middle and older age. Total alpha power decreased linearly, but the aperiodic-adjusted alpha power did not change over the lifespan. Aperiodic slopes and intercepts were highest in the youngest participants. There were no associations between these EEG indexes and self-reported fatigue, measured by the Multidimensional Fatigue Inventory. Finally, we present a set of important considerations for researchers who wish to collect EEG data within public engagement and outreach environments.
DOI Link: 10.1162/imag_a_00001
Rights: © 2023 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

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