Characterization of the Cicatrization Process in Diabetic Foot Ulcers Based on the Production of Reactive Oxygen Species

Autor: Alberto López-Delis, Suélia de S. Rodrigues Fleury Rosa, Paulo Eduardo Narcizo de Souza, Marcella Lemos Brettas Carneiro, Mário Fabrício Fleury Rosa, Yasmin Carneiro Lobo Macedo, Fabiane Hiratsuka Veiga-Souza, Adson Ferreira da Rocha

Publikováno v: Journal of Diabetes Research, Vol 2018 (2018)

The present study aims at evaluating the correlation between the free radical formation and the healing action of lower limbs’ ulcers in a randomized controlled trial with the use of an adhesive derived from natural latex associated with a light-em

Externí odkaz: https://doaj.org/article/b186f286ea364ee0a129b03b00366e19

Adaptive spatial filter based on similarity indices to preserve the neural information on EEG signals during on-line processing

Autor: Teodiano Bastos-Filho, Eduardo Rocon, Alberto López-Delis, Denis Delisle-Rodriguez, Ana Cecilia Villa-Parra, Anselmo Frizera-Neto, Sridhar Krishnan

Publikováno v: Sensors (Basel, Switzerland)
Sensors; Volume 17; Issue 12; Pages: 2725
Sensors, Vol 17, Iss 12, p 2725 (2017)
Digital.CSIC. Repositorio Institucional del CSIC
instname

This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53dd32c0f500a40644e1ae37e2b0ec63
https://doi.org/10.32920/14668848.v1

A Hybrid Brain-Computer Interface using Extreme Learning Machines for Motor Intention Detection

Autor: Oluwarotimi Williams Samuel, Alberto López Delis, Denis Delisle Rodriguez, Ramón Osmany Ramírez Tasé

Publikováno v: Progress in Artificial Intelligence and Pattern Recognition ISBN: 9783030896904
IWAIPR

This work proposes a hybrid brain-computer interface (HBCI) using Bayesian method to fuse information from electroencephalography (EEG) and electromyography signals (EMG) for decoding movement intention. For EEG signal feature extraction, Riemannian

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b1d62613dfd9e70982eec53411c57ed7
https://doi.org/10.1007/978-3-030-89691-1_12

Enhancing Shoulder Pre-Movements Recognition Through EEG Riemannian Covariance Matrices For a BCI-based Exoskeleton

Autor: Yunier Prieur-Coloma, Teodiano Bastos, Leondry Mayeta-Revilla, Denis Delisle-Rodriguez, Alberto López-Delis, Lisset Balart-Fernandez, Tiago H. Falk, Ramon A. Reinoso-Leblanch

Publikováno v: ICHMS

This work proposes a system to recognize motor intention during shoulder flexion/extension, in order to convey control commands towards an upper-limb robotic exoskeleton. For this purpose, two recognition systems were explored: 1) spatial features fr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::35789ee6421376e95cdc7e52ca0162b2
https://doi.org/10.1109/ichms49158.2020.9209345

Virtual Hand Training Platform Controlled Through Online Recognition of Motion Intention

Autor: Fernando Barrera, Andrés Ruiz, César Quinayás, Alberto López Delis

Publikováno v: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications ISBN: 9783030339036
CIARP

Patients with amputation or defects in their limbs use prosthetic devices that require a great cognitive and physical effort to control them, especially during rehabilitation and training phases, being this one of the most frequent reasons of why the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c3ebebf8345ce34a980e64f413ac3719
https://doi.org/10.1007/978-3-030-33904-3_72