ArticleDevelopmental cognitive neuroscience2022
Time-frequency analysis methods and their application in developmental EEG data.
Article in Developmental cognitive neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 4 of them syntheses that pooled it.
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Who cites it
71 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Functional and effective EEG connectivity patterns in Alzheimer's disease and mild cognitive impairment: a systematic review.Frontiers in aging neuroscience · 2025Pooled it
- A mapping-knowledge-domain analysis of ERP research on language processing.Frontiers in human neuroscience · 2024Pooled it
- Electrocortical activity during resistance exercises in healthy young adults-a systematic review.Frontiers in sports and active living · 2024Pooled it
- Rhythmic visual stimulation as a window into early brain development: A systematic review.Developmental cognitive neuroscience · 2023Pooled it
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- Altered Processing of Auditory Distractions Under Competing Inputs in Children With Attention-Deficit/Hyperactivity Disorder.Journal of the American Academy of Child and Adolescent Psychiatry · 2026Article
- Neural Sensitivity to Conversational Inter-Speaker Gaps in the Broad Autism Phenotype.Psychophysiology · 2026Article
- Comparative Analysis of Tri-Polar Concentric Ring and Conventional Electrodes for Overt and Covert Speech.Sensors (Basel, Switzerland) · 2026Article
- App-based mindfulness meditation training enhances cognitive flexibility and modulates ACC and medial frontal gyrus activation during task switching in adolescent OCD.Neuroimage. Reports · 2026Article
- A Systematic Review of Functional Brain Imaging Studies in Neurofibromatosis 1.Neuropsychology review · 2026Review
- Direct Evidence for the Feedforward Neurovascular Coupling Mechanism in Humans During Task Onset: An EEG-fNIRS-TCD Multimodal Imaging Study.Sensors (Basel, Switzerland) · 2026Article
- Decoding multi-class motor attempt from the affected unilateral limbs in chronic stroke patients.Journal of neuroengineering and rehabilitation · 2026Article
- Neural investigation of default effects on decision-making under uncertainty.Scientific reports · 2026Article
- Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs.Scientific reports · 2026Article
- Multivariate synchrosqueezing transform and time-frequency attention for mental workload classification from EEG signals.Scientific reports · 2026Article
- Speech Perception and Preparation Are Supported by Distinct Neural Dynamics Across Development.Neurobiology of language (Cambridge, Mass.) · 2026Article
- Article
- Post-stroke dysphagia is associated with abnormal gamma power and phase synchronisation during swallowing.Frontiers in human neuroscience · 2026Article
- RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features.PloS one · 2026Article
11 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
EEG provides a rich measure of brain activity that can be characterized as neuronal oscillations. However, most developmental EEG work to date has focused on analyzing EEG data as Event-Related Potentials (ERPs) or power based on the Fourier transform. While these measures have been productive, they do not leverage all the information contained within the EEG signal. Namely, ERP analyses ignore non-phase-locked signals and Fourier-based power analyses ignore temporal information. Time-frequency analyses can better characterize the oscillations contained in the EEG data. By separating power and phase information across different frequencies, time-frequency measures provide a closer interpretation of the neurophysiological mechanisms, facilitate translation across neurophysiology disciplines, and capture processes not observed by ERP or Fourier-based analyses (e.g., connectivity). Despite their unique contributions, a literature review of this journal reveals that time-frequency analyses of EEG are yet to be embraced by the developmental cognitive neuroscience field. This manuscript presents a conceptual introduction to time-frequency analyses for developmental researchers. To facilitate the use of time-frequency analyses, we include a tutorial of accessible scripts, based on Cohen (2014), to calculate time-frequency power (signal strength), inter-trial phase synchrony (signal consistency), and two types of phase-based connectivity (inter-channel phase synchrony and weighted phase lag index).
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