Krotov Aleksei, Sharif Razavian Reza, Sadeghi Mohsen, Sternad Dagmar
Department of Bioengineering, Northeastern University, Boston, USA.
Department of Mechanical Engineering, Northern Arizona University, Flagstaff, AZ, USA.
Sci Rep. 2025 Apr 5;15(1):11718. doi: 10.1038/s41598-025-95108-5.
Any set of biological signals has variability, both in the temporal and spatial domains. To extract characteristic features of the ensemble, these spatiotemporal profiles are typically summarized by their mean and variance, often requiring prior padding or resampling of the data to equalize signal length. Such compression can conceal essential information in the signal. This work presents the method of time-warping, reformulated as elastic functional data analysis (EFDA), in an accessible way. This powerful approach rescales the temporal evolution of signals, aligns them accurately, decouples their spatial and temporal variability, and faithfully extracts their characteristics. This technique was compared to conventional methods of normalizing or padding data followed by averaging, using synthetized signals with controlled variability and real human data from a complex manipulation task. Comparative analysis demonstrates that EFDA successfully reveals otherwise concealed features and teases apart temporal and spatial variability. Critical advances to the more common method of dynamic time-warping (DTW) are discussed. Application of EFDA and potential new insights are illustrated in the context of human motor neuroscience. Annotated code to facilitate the use of this technique is provided.
任何一组生物信号在时间和空间域中都具有变异性。为了提取总体的特征，这些时空分布通常通过其均值和方差进行总结，这通常需要对数据进行先验填充或重采样以均衡信号长度。这种压缩可能会掩盖信号中的重要信息。这项工作以一种易懂的方式介绍了时间规整方法，重新表述为弹性函数数据分析（EFDA）。这种强大的方法重新调整信号的时间演变，精确对齐它们，解耦其空间和时间变异性，并忠实地提取其特征。该技术与传统的数据归一化或填充后求平均的方法进行了比较，使用了具有可控变异性的合成信号和来自复杂操作任务的真实人类数据。比较分析表明，EFDA成功地揭示了其他方法隐藏的特征，并区分了时间和空间变异性。讨论了对更常见的动态时间规整（DTW）方法的关键改进。在人类运动神经科学的背景下展示了EFDA的应用和潜在的新见解。提供了便于使用该技术的带注释代码。
