Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30152
Appears in Collections:Psychology Journal Articles
Peer Review Status: Refereed
Title: Frequency and power of human alpha oscillations drift systematically with time-on-task
Author(s): Benwell, Christopher S Y
London, Raquel E
Tagliabue, Chiara F
Veniero, Domenica
Gross, Joachim
Keitel, Christian
Thut, Gregor
Keywords: Oscillations
EEG
Alpha
Frequency
Non-stationarity
Power
Issue Date: 15-May-2019
Citation: Benwell CSY, London RE, Tagliabue CF, Veniero D, Gross J, Keitel C & Thut G (2019) Frequency and power of human alpha oscillations drift systematically with time-on-task. NeuroImage, 192, pp. 101-114. https://doi.org/10.1016/j.neuroimage.2019.02.067
Abstract: Oscillatory neural activity is a fundamental characteristic of the mammalian brain spanning multiple levels of spatial and temporal scale. Current theories of neural oscillations and analysis techniques employed to investigate their functional significance are based on an often implicit assumption: In the absence of experimental manipulation, the spectral content of any given EEG- or MEG-recorded neural oscillator remains approximately stationary over the course of a typical experimental session (∼1 h), spontaneously fluctuating only around its dominant frequency. Here, we examined this assumption for ongoing neural oscillations in the alpha-band (8–13 Hz). We found that alpha peak frequency systematically decreased over time, while alpha-power increased. Intriguingly, these systematic changes showed partial independence of each other: Statistical source separation (independent component analysis) revealed that while some alpha components displayed concomitant power increases and peak frequency decreases, other components showed either unique power increases or frequency decreases. Interestingly, we also found these components to differ in frequency. Components that showed mixed frequency/power changes oscillated primarily in the lower alpha-band (∼8–10 Hz), while components with unique changes oscillated primarily in the higher alpha-band (∼9–13 Hz). Our findings provide novel clues on the time-varying intrinsic properties of large-scale neural networks as measured by M/EEG, with implications for the analysis and interpretation of studies that aim at identifying functionally relevant oscillatory networks or at driving them through external stimulation.
DOI Link: 10.1016/j.neuroimage.2019.02.067
Rights: © 2019 The Authors. Published by Elsevier Inc. This article is available under the terms of the Creative Commons Attribution License (CC BY - https://creativecommons.org/licenses/by/4.0/). You may copy and distribute the article, create extracts, abstracts and new works from the article, alter and revise the article, text or data mine the article and otherwise reuse the article commercially (including reuse and/or resale of the article) without permission from Elsevier. You must give appropriate credit to the original work, together with a link to the formal publication through the relevant DOI and a link to the Creative Commons user license above. You must indicate if any changes are made but not in any way that suggests the licensor endorses you or your use of the work.
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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