Cycle-by-cycle analysis of neural oscillations

By S. Cole, Bradley Voytek. 2018

Subfields: Cognitive Neuroscience, Signal Processing

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Citations in the corpus

The Interaction between Sleep and Development on Wake EEG Oscillations (2026, unknown): “Bursts of oscillations were detected using cycle-by-cycle analysis (Cole and Voytek, 2019) implemented in MATLAB (Snipes et al., 2023).”
Theta activity and cognitive functioning: Integrating evidence from resting-state and task-related developmental electroencephalography (EEG) research (2024, methodology): “In addition to the spectral parameterization approach described above, theta activity can also be studied using a cycle-by-cycle approach (Cole and Voytek, 2019).”
Wake EEG oscillation dynamics reflect both sleep pressure and brain maturation across childhood and adolescence (2024, unknown): “The primary limitation of this study is the scarcity of datasets under 8.”
Spontaneous α Brain Dynamics Track the Episodic “When” (2023, methodology): “The cycle-by-cycle time-domain analysis was used to detect a oscillatory bursts in the continuous MEG recordings and to quantify each oscillatory cycle amplitude (Cole and Voytek, 2019).”
Early development of electrophysiological activity: contribution of periodic and aperiodic components of the EEG signal (2022, background): “Therefore, testing the appearance of those bursts and computing the power with them would probably increase the reliability of results and give other parameters of interest (Cole & Voytek, 2019; Ostlun et al., 2022; Rayson, et al., 2022).”
Spectral parameterization for studying neurodevelopment: How and why (2021, unknown): “alyses approaches which can provide alternate and complementary approaches to detecting the oscillatory activity, and examining oscillatory features such as waveform shape (e.g., Jones, 2016; Cole and Voytek, 2019).”
Hierarchical consciousness: the Nested Observer Windows model (2024, background): “We suggest that the cycle characteristics of electrical oscillations, e.g. peak-trough asymmetry (Cole and Voytek 2019), are potential physiological correlates of open-close time constants.”
Targeting neural oscillations with transcranial alternating current stimulation (2021, background): “…that activity in the time domain can be further decomposed to a few metrics: rise-decay symmetry (e.g. pathological beta oscillations in Parkinson ’ s disease (Cole, 2017), trough-peak symmetry (e.g. beta deflection in somatosenation (Sherman, 2016)), and number of cycles (Cole and Voytek, 2019).”