ArticleNature human behaviour2022
The brain time toolbox, a software library to retune electrophysiology data to brain dynamics.
Article in Nature human behaviour, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Dynamic modeling of EEG responses to natural speech reveals earlier processing of predictable words.PLoS computational biology · 2025Article
- The Problem with Time: Application of Partial Least Squares Analysis on Time-Frequency Plots to Account for Varying Time Intervals with Applied EEG Data.Brain sciences · 2025Article
- Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture.Communications biology · 2024Article
- The genetically programmed rhythmic alteration of diurnal gene expression in the agedFrontiers in plant science · 2024Article
- No evidence that visual impulses enhance the readout of retrieved long-term memory contents from EEG activity.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Enhancing precision in human neuroscience.eLife · 2023Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human thought is highly flexible, achieved by evolving patterns of brain activity across groups of cells. Neuroscience aims to understand cognition in the brain by analysing these intricate patterns. We argue that this goal is impeded by the time format of our data-clock time. The brain is a system with its own dynamics and regime of time, with no intrinsic concern for the human-invented second. Here, we present the Brain Time Toolbox, a software library that retunes electrophysiology data in line with oscillations that orchestrate neural patterns of cognition. These oscillations continually slow down, speed up and undergo abrupt changes, introducing a disharmony between the brain's internal regime and clock time. The toolbox overcomes this disharmony by warping the data to the dynamics of coordinating oscillations, setting oscillatory cycles as the data's new time axis. This enables the study of neural patterns as they unfold in the brain, aiding neuroscientific enquiry into dynamic cognition. In support of this, we demonstrate that the toolbox can reveal results that are absent in a default clock time format.
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Registered trials
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