Hartmann Raphael, Koger Anton, Straub Elisa R, Johannsen Leif, Koch Iring, Stephan Denise N, Müller Hermann, Kiesel Andrea
University of Freiburg, Freiburg, Germany.
University of Aachen, Aachen, Germany.
Behav Res Methods. 2025 Jun 3;57(7):187. doi: 10.3758/s13428-025-02657-8.
Evidence supporting the interaction between cognitive and motor processes is increasing. Conventional approaches to analyze balance control aggregate sway data over seconds up to minutes, which presents a challenge in discerning the impact of single cognitive processes on balance control. In this paper, we propose a novel, event-related approach to investigate how cognitive task performance affects balance control on small time scales using a force plate. A force plate continuously measures forces and moments in each spatial dimension over time. To facilitate the processing of the resulting time-series data, we developed an R-package called forceplate. This package segments the data so that each trial, corresponding to a cognitive task, has its own time-series data. A low-pass filter can be applied to remove artifacts (e.g., muscle twitches or electrical noise), and a baseline correction can be performed to improve the comparability of trials. For each trial's time-series data, user-defined descriptive statistics (e.g., mean or standard deviation) can be calculated for user-defined time bins around an event (e.g., stimulus or response onset). The package generates a dataset with one or more measures per trial (depending on the number of time bins) that can be used for further analysis, such as a (mixed-effects) analysis of variance. The R-package and the described underlying procedure aim to establish a standard to process force-plate data collected in the context of cognitive experiments for the event-related approach. This facilitates the processing of force-plate data and enhances comparability between studies.
支持认知与运动过程之间相互作用的证据越来越多。传统的分析平衡控制的方法是在数秒到数分钟内汇总摇摆数据，这在辨别单个认知过程对平衡控制的影响方面提出了挑战。在本文中，我们提出了一种新颖的、与事件相关的方法，以研究认知任务表现如何在小时间尺度上使用测力板影响平衡控制。测力板随时间连续测量每个空间维度上的力和力矩。为了便于处理由此产生的时间序列数据，我们开发了一个名为forceplate的R包。该包对数据进行分段，以便每个对应于认知任务的试验都有自己的时间序列数据。可以应用低通滤波器去除伪迹（例如肌肉抽搐或电噪声），并可以进行基线校正以提高试验的可比性。对于每个试验的时间序列数据，可以针对围绕事件（例如刺激或反应开始）的用户定义的时间间隔计算用户定义的描述性统计量（例如均值或标准差）。该包生成一个每个试验有一个或多个测量值的数据集（取决于时间间隔的数量），可用于进一步分析，例如（混合效应）方差分析。R包和所描述的底层程序旨在建立一个标准，用于处理在认知实验背景下为与事件相关的方法收集的测力板数据。这便于处理测力板数据并提高研究之间的可比性。
