Sommer Werner, Stapor Katarzyna, Kończak Grzegorz, Kotowski Krzysztof, Fabian Piotr, Ochab Jeremi, Bereś Anna, Ślusarczyk Grażyna
Department of Psychology, Humboldt-University of Berlin, 10099 Berlin, Germany.
Department of Psychology, Zhejiang Normal University, Jinhua 321000, China.
Brain Sci. 2022 Apr 21;12(5):525. doi: 10.3390/brainsci12050525.
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.
在许多处理噪声时间序列的领域中，一个重要问题是检测时间序列背后模型的变化，比如学习过程中的心理生理单次试验数据或长期监测治疗效果。本文提出了一种用于检测线性时间序列回归模型中单个变化点的新方法，称为基于残差排列的方法（RESPERM）。RESPERM中的最优变化点通过线性模型中残差排列估计的参数使科恩效应量最大化。使用大量模拟数据集，改变噪声的数量和分布特征以及变化点的位置，将RESPERM与一种成熟且推荐的变化点检测方法SEGMENTED进行比较。在中等到大量噪声的时间序列中，RESPERM检测到的变化点方差始终比SEGMENTED小。最后，将这两种方法应用于面部学习过程中N250事件相关电位成分单次试验振幅的样本数据集。总之，RESPERM似乎非常适合变化点检测，尤其是在噪声数据中，使其成为神经科学、医学和许多其他领域的首选方法。
