Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs

Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs

An important problem in many fields dealing with noisy time series, such as psychophysiological single trial data during learning or monitoring treatment effects over time, is detecting a change in the model underlying a time series. Here, we present a new method for detecting a single changepoint i...

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Bibliographic Details
Main Authors: Werner Sommer, Katarzyna Stapor, Grzegorz Kończak, Krzysztof Kotowski, Piotr Fabian, Jeremi Ochab, Anna Bereś, Grażyna Ślusarczyk
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Brain Sciences
Subjects:
noisy time series
event-related potentials
changepoint detection
segmented method
permutation method
Online Access:https://www.mdpi.com/2076-3425/12/5/525

Internet

https://www.mdpi.com/2076-3425/12/5/525

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