Very Slow EEG Fluctuations Predict the Dynamics of Stimulus Detection and Oscillation Amplitudes in Humans
Open accessAbstract
Our ability to perceive weak signals is correlated among consecutive trials and fluctuates slowly over time. Although this “streaking effect” has been known for decades, the underlying neural network phenomena have remained largely unidentified. We examined the dynamics of human behavioral performance and its correlation with infraslow (0.01–0.1 Hz) fluctuations in ongoing brain activity. Full-band electroencephalography revealed prominent infraslow fluctuations during the execution of a somatosensory detection task. Similar fluctuations were predominant also in the dynamics of behavioral performance. The subjects' ability to detect the sensory stimuli was strongly correlated with the phase, but not with the amplitude of the infraslow EEG fluctuations. These data thus reveal a direct electrophysiological correlate for the slow fluctuations in human psychophysical performance. We then examined the correlation between the phase of infraslow EEG fluctuations and the amplitude of 1–40 Hz neuronal oscillations in six frequency bands. Like the behavioral performance, the amplitudes in these frequency bands were robustly correlated with the phase of the infraslow fluctuations. These data hence suggest that the infraslow fluctuations reflect the excitability dynamics of cortical networks. We conclude that ongoing 0.01–0.1 Hz EEG fluctuations are prominent and functionally significant during execution of cognitive tasks.
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There is an abundance of evidence that the EEG is organized across multiple time scales, such as in nested oscillations involving both local and global modulations in time.
“In contrast, there is an abundance of evidence that the EEG is organized across multiple time scales, such as in nested oscillations [Canolty et al., 2006; Monto et al., 2008; Schack et al., 2002; Vanhatalo et al., 2004] involving both local and global modulations in time.”
From Deep learning with convolutional neural networks for EEG decoding and visualization (2017) · cites this paper as Monto et al., 2008
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- Authors
- Monto, S., Palva, S., Voipio, J., & Palva, J. M.
- Affiliations
- University of Helsinki (FI)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2008-08-13
- Volume
- 28
- Issue
- 33
- Pages
- 8268-8272
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6670577
- OpenAlex
- W2103528520
- Cited by
- 453
- References
- 4
- Citations by year
- 2026: 14 · 2025: 23 · 2024: 26 · 2023: 16 · 2022: 28 · 2021: 29 · 2020: 18 · 2019: 20 · 2018: 24 · 2017: 31 · 2016: 32 · 2015: 28 · 2014: 27 · 2013: 35 · 2012: 28
- FWCI
- 12.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Helsingin Yliopisto
- KAUTE-Säätiö
- Academy of Finland
- Ella ja Georg Ehrnroothin Säätiö
- Emil Aaltosen Säätiö
- Jenny ja Antti Wihurin Rahasto
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience and Music Perception
- Keywords
- stimulus detection, neural oscillations, EEG phase, ongoing brain activity, psychophysical performance
- MeSH terms
- Adult, Cerebral Cortex, Electric Stimulation, Electroencephalography, Evoked Potentials, Somatosensory, Humans, Male, Predictive Value of Tests, Psychomotor Performance, Reaction Time
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