Discovering recurring patterns in electrophysiological recordings

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Discovering recurring patterns in electrophysiological recordings. / Gips, Bart; Bahramisharif, Ali; Lowet, Eric; Roberts, Mark J.; de Weerd, Peter; Jensen, Ole; van der Eerden, Jan.

In: Journal of Neuroscience Methods, Vol. 275, 01.01.2017, p. 66-79.

Research output: Contribution to journalArticlepeer-review

Harvard

Gips, B, Bahramisharif, A, Lowet, E, Roberts, MJ, de Weerd, P, Jensen, O & van der Eerden, J 2017, 'Discovering recurring patterns in electrophysiological recordings', Journal of Neuroscience Methods, vol. 275, pp. 66-79. https://doi.org/10.1016/j.jneumeth.2016.11.001

APA

Gips, B., Bahramisharif, A., Lowet, E., Roberts, M. J., de Weerd, P., Jensen, O., & van der Eerden, J. (2017). Discovering recurring patterns in electrophysiological recordings. Journal of Neuroscience Methods, 275, 66-79. https://doi.org/10.1016/j.jneumeth.2016.11.001

Vancouver

Author

Gips, Bart ; Bahramisharif, Ali ; Lowet, Eric ; Roberts, Mark J. ; de Weerd, Peter ; Jensen, Ole ; van der Eerden, Jan. / Discovering recurring patterns in electrophysiological recordings. In: Journal of Neuroscience Methods. 2017 ; Vol. 275. pp. 66-79.

Bibtex

@article{2b3eea6a2556430d9ced662e65b71549,
title = "Discovering recurring patterns in electrophysiological recordings",
abstract = "BACKGROUND: Fourier-based techniques are used abundantly in the analysis of electrophysiological data. However, these techniques are of limited value when the signal of interest is non-sinusoidal or non-periodic.NEW METHOD: We present sliding window matching (SWM): a new data-driven method for discovering recurring temporal patterns in electrophysiological data. SWM is effective in detecting recurring but unknown patterns even when they appear non-periodically.RESULTS: To demonstrate this, we used SWM on oscillations in local field potential (LFP) recordings from the rat hippocampus and monkey V1. The application of SWM yielded two interesting findings. We could show that rat hippocampal theta and monkey V1 gamma oscillations were both skewed (i.e. asymmetric in time), rather than being sinusoidal. Furthermore, gamma oscillations in monkey V1 were skewed differently in the superficial compared to the deeper cortical layers. Second, we used SWM to analyze responses evoked by stimuli or microsaccades even when the onset timing of stimulus or microsaccades was unknown.COMPARISON WITH EXISTING METHODS: We first validated the method on simulated datasets, and we checked that for recordings with a sufficiently low noise level the SWM results were consistent with results from the widely used phase alignment (PA) method.CONCLUSIONS: We conclude that the proposed method has wide applicability in the exploration of noisy time series data where the onset times of particular events are unknown by the experimenter such as in resting state and sleep recordings.",
keywords = "evoked response, theta, gamma, oscillation, Markov Chain Monte Carlo",
author = "Bart Gips and Ali Bahramisharif and Eric Lowet and Roberts, {Mark J.} and {de Weerd}, Peter and Ole Jensen and {van der Eerden}, Jan",
note = "Copyright {\^A}{\textcopyright} 2016 Elsevier B.V. All rights reserved.",
year = "2017",
month = jan,
day = "1",
doi = "10.1016/j.jneumeth.2016.11.001",
language = "English",
volume = "275",
pages = "66--79",
journal = "Journal of Neuroscience Methods",
issn = "0165-0270",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Discovering recurring patterns in electrophysiological recordings

AU - Gips, Bart

AU - Bahramisharif, Ali

AU - Lowet, Eric

AU - Roberts, Mark J.

AU - de Weerd, Peter

AU - Jensen, Ole

AU - van der Eerden, Jan

N1 - Copyright © 2016 Elsevier B.V. All rights reserved.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - BACKGROUND: Fourier-based techniques are used abundantly in the analysis of electrophysiological data. However, these techniques are of limited value when the signal of interest is non-sinusoidal or non-periodic.NEW METHOD: We present sliding window matching (SWM): a new data-driven method for discovering recurring temporal patterns in electrophysiological data. SWM is effective in detecting recurring but unknown patterns even when they appear non-periodically.RESULTS: To demonstrate this, we used SWM on oscillations in local field potential (LFP) recordings from the rat hippocampus and monkey V1. The application of SWM yielded two interesting findings. We could show that rat hippocampal theta and monkey V1 gamma oscillations were both skewed (i.e. asymmetric in time), rather than being sinusoidal. Furthermore, gamma oscillations in monkey V1 were skewed differently in the superficial compared to the deeper cortical layers. Second, we used SWM to analyze responses evoked by stimuli or microsaccades even when the onset timing of stimulus or microsaccades was unknown.COMPARISON WITH EXISTING METHODS: We first validated the method on simulated datasets, and we checked that for recordings with a sufficiently low noise level the SWM results were consistent with results from the widely used phase alignment (PA) method.CONCLUSIONS: We conclude that the proposed method has wide applicability in the exploration of noisy time series data where the onset times of particular events are unknown by the experimenter such as in resting state and sleep recordings.

AB - BACKGROUND: Fourier-based techniques are used abundantly in the analysis of electrophysiological data. However, these techniques are of limited value when the signal of interest is non-sinusoidal or non-periodic.NEW METHOD: We present sliding window matching (SWM): a new data-driven method for discovering recurring temporal patterns in electrophysiological data. SWM is effective in detecting recurring but unknown patterns even when they appear non-periodically.RESULTS: To demonstrate this, we used SWM on oscillations in local field potential (LFP) recordings from the rat hippocampus and monkey V1. The application of SWM yielded two interesting findings. We could show that rat hippocampal theta and monkey V1 gamma oscillations were both skewed (i.e. asymmetric in time), rather than being sinusoidal. Furthermore, gamma oscillations in monkey V1 were skewed differently in the superficial compared to the deeper cortical layers. Second, we used SWM to analyze responses evoked by stimuli or microsaccades even when the onset timing of stimulus or microsaccades was unknown.COMPARISON WITH EXISTING METHODS: We first validated the method on simulated datasets, and we checked that for recordings with a sufficiently low noise level the SWM results were consistent with results from the widely used phase alignment (PA) method.CONCLUSIONS: We conclude that the proposed method has wide applicability in the exploration of noisy time series data where the onset times of particular events are unknown by the experimenter such as in resting state and sleep recordings.

KW - evoked response

KW - theta

KW - gamma

KW - oscillation

KW - Markov Chain Monte Carlo

U2 - 10.1016/j.jneumeth.2016.11.001

DO - 10.1016/j.jneumeth.2016.11.001

M3 - Article

C2 - 27836729

VL - 275

SP - 66

EP - 79

JO - Journal of Neuroscience Methods

JF - Journal of Neuroscience Methods

SN - 0165-0270

ER -