期刊
BRAIN SCIENCES
卷 12, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/brainsci12050525
关键词
noisy time series; event-related potentials; changepoint detection; segmented method; permutation method
资金
- Department of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland [02/080/BK22/0022]
- Department of Physics, Astronomy and Applied Informatics, Jagiellonian University, Poland [1704008]
- Foundation for Polish Science (FNP) project Bio-inspired Artificial Neural Networks [POIR.04.04.00-00-14DE/18-00]
This article introduces a new method (RESPERM) for detecting a single changepoint in a linear time series regression model. The study found that RESPERM detected changepoints with lower variance in time series with medium to large amounts of noise compared to the well-established SEGMENTED method. In practical applications, RESPERM is suitable for datasets with high noise levels.
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.
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