Introduction. Neurophysiological phenomena, such as muscle coactivation, have been used to identify motor tasks requiring greater joint stability in healthy people or with movement disorders. Nonetheless, there are many ways to calculate the coactivation index (CI). This article aimed to create a processing pipeline to calculate the muscular CI by designing two functions with the Python language. The first function calculates the CI utilising the formula proposed by Falconer and Winter, defined as “coactivation_index”. It is required to introduce two signals of antagonist muscles with the same data long and sample frequency. These signals were previously normalised to the maximum voluntary contraction using the averaged rectified values. The second function was defined as “plot_coactivacion”, which unfolds a figure that describes the amplitude changes for both muscles and their common area. These functions were designed with a freely accessible language (Python), highlighting its clear syntax and the number of libraries associated with biomedical signal processing.
|Original language||Spanish (Chile)|
|Number of pages||3|
|State||Published - Jun 2022|