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.
