Biased instantaneous regional muscle activation maps: Embedded fuzzy topology and image feature analysis

Carlos De la Fuente, Alejandro Weinstein, Alejandro Neira, Oscar Valencia, Carlos Cruz-Montecinos, Rony Silvestre, Patricio A. Pincheira, Felipe Palma, Felipe P. Carpes*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The instantaneous spatial representation of electrical propagation produced by muscle contraction may introduce bias in surface electromyographical (sEMG) activation maps. Here, we described the effect of instantaneous spatial representation (sEMG segmentation) on embedded fuzzy topological polyhedrons and image features extracted from sEMG activation maps. We analyzed 73,008 topographic sEMG activation maps from seven healthy participants (age 21.4 ± 1.5 years and body mass 74.5 ± 8.5 kg) who performed submaximal isometric plantar flexions with 64 surface electrodes placed over the medial gastrocnemius muscle. Window lengths of 50, 100, 150, 250, 500, and 1,000 ms and overlap of 0, 25, 50, 75, and 90% to change sEMG map generation were tested in a factorial design (grid search). The Shannon entropy and volume of global embedded tri-dimensional geometries (polyhedron projections), and the Shannon entropy, location of the center (LoC), and image moments of maps were analyzed. The polyhedron volume increased when the overlap was <25% and >75%. Entropy decreased when the overlap was <25% and >75% and when the window length was <100 ms and >500 ms. The LoC in the x-axis, entropy, and the histogram moments of maps showed effects for overlap (p < 0.001), while the LoC in the y-axis and entropy showed effects for both overlap and window length (p < 0.001). In conclusion, the instantaneous sEMG maps are first affected by outer parameters of the overlap, followed by the length of the window. Thus, choosing the window length and overlap parameters can introduce bias in sEMG activation maps, resulting in distorted regional muscle activation.

Original languageEnglish
Article number934041
JournalFrontiers in Bioengineering and Biotechnology
StatePublished - 22 Dec 2022

Bibliographical note

Funding Information:
FC acknowledges the support from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil). AW was supported by grant BASAL FB0008.

Funding Information:
This study was supported by the “Fondo interdisciplina del departamento de ciencias de la salud de la Pontificia Universidad Católica de Chile,” and “Grant support for publication of the Carrera de kinesiologia del departamiento de Ciencias de la Salud de la Pontificia Universidad Católica de Chile.” FC was supported by a CNPq research fellowship. CD was supported by the De Luca Foundation and Delsys Inc. through Delsys’ donation initiative 2020. OV was supported by “Fondo de Ayuda a la Investigación” (Universidad de los Andes, Santiago, Chile) with project number INV-IN201701.

Publisher Copyright:
Copyright © 2022 De la Fuente, Weinstein, Neira, Valencia, Cruz-Montecinos, Silvestre, Pincheira, Palma and Carpes.


  • UMAP
  • barycenter
  • entropy
  • high-density electromyography
  • image moments
  • muscle
  • segmentation


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