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Article

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

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Department of Psychology, Humboldt-University of Berlin, 10099 Berlin, Germany
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Department of Psychology, Zhejiang Normal University, Jinhua 321000, China
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Department of Applied Informatics, Silesian University of Technology, 44-100 Gliwice, Poland
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Department of Statistics, Econometrics and Mathematics, University of Economics in Katowice, 40-287 Katowice, Poland
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Department of Theory of Complex Systems, Jagiellonian University, 30-348 Krakow, Poland
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Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, 30-348 Krakow, Poland
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Department of Design and Computer Graphics, Jagiellonian University, 30-348 Krakow, Poland
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Author to whom correspondence should be addressed.
Academic Editor: Eric S. Drollette
Brain Sci. 2022, 12(5), 525; https://doi.org/10.3390/brainsci12050525
Received: 16 March 2022 / Revised: 13 April 2022 / Accepted: 19 April 2022 / Published: 21 April 2022
(This article belongs to the Section Computational Neuroscience and Neuroinformatics)
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 in a linear time series regression model, termed residuals permutation-based method (RESPERM). The optimal changepoint in RESPERM maximizes Cohen’s effect size with the parameters estimated by the permutation of residuals in a linear model. RESPERM was compared with the SEGMENTED method, a well-established and recommended method for detecting changepoints, using extensive simulated data sets, varying the amount and distribution characteristics of noise and the location of the change point. In time series with medium to large amounts of noise, the variance of the detected changepoint was consistently smaller for RESPERM than SEGMENTED. Finally, both methods were applied to a sample dataset of single trial amplitudes of the N250 ERP component during face learning. In conclusion, RESPERM appears to be well suited for changepoint detection especially in noisy data, making it the method of choice in neuroscience, medicine and many other fields. View Full-Text
Keywords: noisy time series; event-related potentials; changepoint detection; segmented method; permutation method noisy time series; event-related potentials; changepoint detection; segmented method; permutation method
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MDPI and ACS Style

Sommer, W.; Stapor, K.; Kończak, G.; Kotowski, K.; Fabian, P.; Ochab, J.; Bereś, A.; Ślusarczyk, G. Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs. Brain Sci. 2022, 12, 525. https://doi.org/10.3390/brainsci12050525

AMA Style

Sommer W, Stapor K, Kończak G, Kotowski K, Fabian P, Ochab J, Bereś A, Ślusarczyk G. Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs. Brain Sciences. 2022; 12(5):525. https://doi.org/10.3390/brainsci12050525

Chicago/Turabian Style

Sommer, Werner, Katarzyna Stapor, Grzegorz Kończak, Krzysztof Kotowski, Piotr Fabian, Jeremi Ochab, Anna Bereś, and Grażyna Ślusarczyk. 2022. "Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs" Brain Sciences 12, no. 5: 525. https://doi.org/10.3390/brainsci12050525

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