Analyzing Neural Time Series Data: Theory and Practice

By Michael X. Cohen. 2014

1,924 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e919db71bdd077e14b38961a7a8846b807562187

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Citations in the corpus

SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach (2019, background): “The idea behind these variable sizes of filters comes from the signal processing community to have a trade-off between extracting time domain and frequency domain features [26].”
A better way to define and describe Morlet wavelets for time-frequency analysis (2018, background): “There are several time-frequency analysis methods, most of which produce qualitatively or quantitatively similar results (Bruns 2004; Cohen 2014).”
Tensor decomposition of EEG signals: a brief review. (2015, unknown): “…(EEG) studies, EEG data were represented by a time series and all data samples were carried by a vector (Berger, 1929) Since then, power spectrum analysis of the time series has been often applied for investigating EEG oscillations (Cohen, 2014; Niedermeyer and Lopes da Silva, 2005 .”
DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG (2017, methodology): “For the representation learning part, the parameters of the CNN-1 and CNN-2 were selected with the aim to capture temporal and frequency information from the EEG according to the guideline provided by [14].”
Data Augmentation for Deep-Learning-Based Electroencephalography. (2020, background): “EEG also suffers from low signal-to-noise ratio (SNR) [1-4].”
Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data (2016, methodology): “As this review is targeted at providing a broad overview to a general audience, we avoid formal mathematical definitions and implementation details of the methods and instead focus on the rationale behind the decoding approach as applied to time series data.”
Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks (2016, methodology): “…study we used the Fpz-Cz electrode and time-frequency analysis-based feature extraction ﬁne-tuned to capture sleep stage-speciﬁc signal features using Morlet wavelets (see for example, Chapters 12 and 13, pp. 141–174 in [5]), with stacked sparse autoencoders [2, 1] as the classi-ﬁcation algorithm.”
DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices (2018, methodology): “The final step for preparing the EEG data for further analysis is the application of the Common Average Reference (CAR) method, as recommended by Cohen [32], which computes the average value over all electrodes and subtracts it from each sample of each electrode.”
Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders (2015, methodology): “Our methodology is based on time-frequency analysis [4] and stacked sparse autoencoders [1].”
Multiple task-demands flexibly optimize neural geometry in human ventral temporal cortex (2026, unknown): “HGB Power was baseline corrected on a trial-by-trial basis 92 using the decibel (dB) conversion method 93 .”
The dominance of large-scale phase dynamics in human cortex, from delta to gamma (2026, unknown): “We are therefore confident that the present results are not confabulated by ‘ringing’ effects ( Cohen, 2014 ).”
Neural correlates of perceptual consciousness from within: A narrative review of human intracranial research (2025, unknown): “Furthermore, while scattering and artifactual activity in scalp signals limit the analysis of high-frequency components ( Cohen, 2014 ; Pfurtscheller and Cooper, 1975 ), intracranial signals capture brain oscillations in the high gamma range that have been associated with local neuronal firings (…”
Intracranial EEG signals disentangle multi-areal neural dynamics of vicarious pain perception (2024, methodology): “Given that power correlation was an undirectedmeasurewhichcannotconveydirectionalinformationabout inter-regional interactions 45,93 , we further estimated the directionality of power correlation results by computing TE values using the neuroscience information theory toolbox 48 .”
A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings (2023, unknown): “Given that the speech contaminations can be assessed by means of coherence with audio channels (ITPC) 8 , PCO (another data-driven spatial-filtering method) is ideally suited to decompose the acoustic artifact source from brain recordings.”
Developmental differences in threat learning are associated with changes in frontal-central theta activity (2025, unknown): “Third, due to the limited capacity (32-channels) of the EEG device used, we were unable to perform precise source localization [67 ]; consequently, interpretations of signal origins are conservative and rely on prior studies.”