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Global Translational Medicine BCI-FES with static magnetic field in SCI

4.5. Study implications and limitations Prado Campos, Paulo Broniera Júnior, José J. A.

The implications of the present study include improving Mendes Junior, Eddy Krueger
therapeutic tools and assistive technologies to enhance All authors have read and agreed to the published
the functionality of individuals with SCI. Our proposal version of the manuscript.
was to utilize a laboratory interface, but further research
is needed to optimize it for home use. The results were Ethics approval and consent to participate
limited by the small number of interventions and the severe The present study was approved by the Ethics Committee
neurological conditions of the participants. Moreover, only and Research Involving Human Beings of the State
one participant underwent the experimental intervention. University of Londrina (no.: 4.060.700) on June 1, 2020.
The participant provided written consent.
5. Conclusion
The sNES-sFES interface and static magnetic field are Consent for publication
non-invasive interfaces with an average duration of 1 h Written informed consent has been obtained from the
(equivalent to conventional therapy) and a setup and patient(s) to publish this paper.
dismount time of approximately 10 min. A gradual learning
effect was perceived regarding (i) training repetition up to Availability of data
three times per session to avoid mental fatigue, (ii) classifier The neuroelectrical signals recorded through
accuracy reaching up to 84%, and (iii) maximum sFES electroencephalography were not stored. Furthermore, all
activation hits of 53%. Therefore, intervention with the available data are presented in the images provided in the
sNES-sFES interface and the static magnetic field is a viable article.
therapeutic option for study participants. Studies with
a larger number of participants are needed to generalize References
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outcome of the 9-week intervention. Nevertheless, it can 2019;14(12):2054-2062.
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in a paralyzed muscle and reduce spasticity, along with
continuous physical rehabilitation. Future studies will 2. Yang T, Dai Y, Chen G, Cui, S. Dissecting the dual role of the
include the implementation of novel machine learning glial scar and scar-forming astrocytes in spinal cord injury.
algorithms based on deep learning techniques to improve Front Cell Neurosci. 2020;14:78.
classifier performance. A better accuracy model should doi: 10.3389/fncel.2020.00078
enhance user control with SCI over the sNES-sFES 3. Aggarwal S, Chugh, N. Signal processing techniques for
interface and enable the application to be used in different motor imagery brain computer interface: A review. Array.
positions, such as orthostasis or walking. 2019;1-2:100003.
Acknowledgments doi: 10.1016/j.array.2019.100003
4. Barroso FO, Pascual-Valdunciel A, Torricelli D, et al.
None. Noninvasive modalities used in spinal cord injury
Funding rehabilitation. In: Spinal Cord Injury Therapy. London:
IntechOpen; 2019.
None. doi: 10.5772/intechopen.83654
Conflict of interest 5. Chédotal A. Roles of axon guidance molecules in neuronal
wiring in the developing spinal cord. Nat Rev Neurosci.
The authors declare that they have no competing interests. 2019;20(7):380-396.
Author contributions doi: 10.1038/s41583-019-0168-7
Conceptualization: Eddy Krueger Larissa Gomes Sartori 6. Courtine G, Sofroniew MV. Spinal cord repair: Advances in
biology and technology. Nat Med. 2019;25(6):898-908.
Methodology: All authors
Writing—original draft: Larissa Gomes Sartori Eddy doi: 10.1038/s41591-019-0475-6
Krueger 7. Friedrich P, Waschke J. Sobotta; Editora Guanabara Koogan
Writing—review & editing: Roger Burgo de Souza, Daniel LTDA. United States: Bloomberg; 2012.

Volume 3 Issue 1 (2024) 9 https://doi.org/10.36922/gtm.2285