Cycle-by-cycle analysis of neural oscillations
Scott R. Cole,Bradley Voytek +1 more
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TLDR
A new analysis framework is presented that is complementary to existing Fourier- and Hilbert-transform based approaches that quantifies oscillatory features in the time domain, on a cycle-by-cycle basis and is validated in simulation and against experimental recordings of patients with Parkinson's disease.Abstract:
We introduce a fully documented, open-source Python package, bycycle, for analyzing neural oscillations on a cycle-by-cycle basis. This approach is complementary to traditional Fourier- and Hilbert...read more
Citations
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Deep learning-based electroencephalography analysis: a systematic review.
Yannick Roy,Hubert Banville,Isabela Albuquerque,Alexandre Gramfort,Tiago H. Falk,Jocelyn Faubert +5 more
TL;DR: In this paper, the authors present a review of 154 studies that apply deep learning to EEG, published between 2010 and 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring.
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Parameterizing neural power spectra into periodic and aperiodic components.
Thomas Donoghue,Matar Haller,Erik J. Peterson,Paroma Varma,Priyadarshini Sebastian,Richard Gao,Torben Noto,Antonio H. Lara,Joni D. Wallis,Joni D. Wallis,Robert T. Knight,Robert T. Knight,Avgusta Y. Shestyuk,Bradley Voytek +13 more
TL;DR: An algorithm to parameterize electrophysiological neural power spectra as a combination of an aperiodic component and putative periodic oscillatory peaks is introduced, addressing limitations of common approaches.
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Theta Oscillations in Human Memory.
TL;DR: Theta frequency fluctuations of the local field potential have long been implicated in learning and memory, but human studies of episodic memory have provided mixed evidence for theta's role in successful learning and remembering.
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Data Augmentation for Deep-Learning-Based Electroencephalography.
TL;DR: DA increasingly used and considerably improved DL decoding accuracy on EEG and holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc.
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Longitudinal changes in aperiodic and periodic activity in electrophysiological recordings in the first seven months of life.
TL;DR: It is shown that the progressive flattening of the EEG power spectrum begins in very early development, continuing through the first months of life, and highlights the importance of separating the periodic and aperiodic neuronal signals, because the a periodic signal can bias measurement of neuronal oscillations.
References
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EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis
TL;DR: In this article, it is suggested to adjust the frequency windows of alpha and theta for each subject by using individual alpha frequency as an anchor point, based on this procedure, a consistent interpretation of a variety of findings is made possible.
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Ryan T. Canolty,Erik Edwards,Erik Edwards,Sarang S. Dalal,Maryam Soltani,Maryam Soltani,Srikantan S. Nagarajan,Heidi E. Kirsch,Mitchel S. Berger,Nicholas M. Barbaro,Robert T. Knight +10 more
TL;DR: The results indicate that transient coupling between low- and high-frequency brain rhythms coordinates activity in distributed cortical areas, providing a mechanism for effective communication during cognitive processing in humans.
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Abnormal neural oscillations and synchrony in schizophrenia
TL;DR: Dysfunctional oscillations may arise owing to anomalies in the brain's rhythm-generating networks of GABA (γ-aminobutyric acid) interneurons and in cortico-cortical connections.
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The sleep slow oscillation as a traveling wave.
TL;DR: It is shown here that each cycle of the slow oscillation is a traveling wave, which provides a blueprint of cortical excitability and connectivity and may play a role in spike timing-dependent synaptic plasticity during sleep.
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Prefrontal Phase Locking to Hippocampal Theta Oscillations
TL;DR: It is demonstrated that a significant portion of neurons in the medial prefrontal cortex of freely behaving rats are phase locked to the hippocampal theta rhythm, and phase locking of prefrontal cells is predicted by the presence of significant correlations with hippocampal cells at positive delays up to 150 ms.