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

Brain Sciences

Krzysztof Kotowski¹  Katarzyna Stapor¹  Piotr Fabian¹  Anna Bereś²  Grażyna Ślusarczyk³  Werner Sommer⁴  Grzegorz Kończak⁵  Jeremi Ochab⁶ 

[1] Department of Applied Informatics, Silesian University of Technology, 44-100 Gliwice, Poland;Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, 30-348 Krakow, Poland;Department of Design and Computer Graphics, Jagiellonian University, 30-348 Krakow, Poland;Department of Psychology, Humboldt-University of Berlin, 10099 Berlin, Germany;Department of Statistics, Econometrics and Mathematics, University of Economics in Katowice, 40-287 Katowice, Poland;Department of Theory of Complex Systems, Jagiellonian University, 30-348 Krakow, Poland;

关键词: noisy time series;    event-related potentials;    changepoint detection;    segmented method;    permutation method;   

DOI  :  10.3390/brainsci12050525

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown