Zobrazeno 1 - 10
of 142
pro vyhledávání: '"Eduardo Iáñez"'
Autor:
Laura Ferrero, Paula Soriano-Segura, Jacobo Navarro, Oscar Jones, Mario Ortiz, Eduardo Iáñez, José M. Azorín, José L. Contreras-Vidal
Publikováno v:
Journal of NeuroEngineering and Rehabilitation, Vol 21, Iss 1, Pp 1-14 (2024)
Abstract Background This research focused on the development of a motor imagery (MI) based brain–machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of trad
Externí odkaz:
https://doaj.org/article/ef97d457680b494fad9c7e0aeea56663
Autor:
Mario Ortiz, Luis de la Ossa, Javier Juan, Eduardo Iáñez, Diego Torricelli, Jesús Tornero, José M. Azorín
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-15 (2023)
Abstract One important point in the development of a brain-machine Interface (BMI) commanding an exoskeleton is the assessment of the cognitive engagement of the subject during the motor imagery tasks conducted. However, there are not many databases
Externí odkaz:
https://doaj.org/article/d70e2f0983024dde91fa7ee72261cd19
Publikováno v:
Frontiers in Neuroinformatics, Vol 18 (2024)
IntroductionIn recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their
Externí odkaz:
https://doaj.org/article/bc8489b41c5e4c0499186cdecfcecba7
Publikováno v:
iScience, Vol 26, Iss 5, Pp 106675- (2023)
Summary: This study explores the use of a brain-computer interface (BCI) based on motor imagery (MI) for the control of a lower limb exoskeleton to aid in motor recovery after a neural injury. The BCI was evaluated in ten able-bodied subjects and two
Externí odkaz:
https://doaj.org/article/76f85ee5be84486690fc8a975cc353e3
Publikováno v:
Frontiers in Neuroscience, Vol 17 (2023)
IntroductionBrain-machine interfaces (BMIs) attempt to establish communication between the user and the device to be controlled. BMIs have great challenges to face in order to design a robust control in the real field of application. The artifacts, h
Externí odkaz:
https://doaj.org/article/1b0dc178ed054a0e8f477c24e0e66dcd
Publikováno v:
Revista Iberoamericana de Automática e Informática Industrial RIAI, Vol 19, Iss 1, Pp 108-116 (2021)
Las interfaces cerebro-máquina (Brain-Computer Intarface, BCI, en inglés) son una tecnología que permite la comunicación directa entre el cerebro y el mundo exterior sin necesidad de utilizar el sistema nervioso periferico. La mayoría de sistema
Externí odkaz:
https://doaj.org/article/f457804c45df4d77b8b8854379fc5dff
Autor:
Desirée I. Gracia, Mario Ortiz, Tatiana Candela, Eduardo Iáñez, Rosa M. Sánchez, Carmina Díaz, José M. Azorín
Publikováno v:
Sensors, Vol 23, Iss 13, p 5880 (2023)
A new pandemic was declared at the end of 2019 because of coronavirus disease 2019 (COVID-19). One of the effects of COVID-19 infection is anosmia (i.e., a loss of smell). Unfortunately, this olfactory dysfunction is persistent in around 5% of the wo
Externí odkaz:
https://doaj.org/article/d647bf1982384bbcab27301f6c7c8a25
Autor:
Álvaro Costa‐García, Eduardo Iáñez, Moeka Yokoyama, Sayako Ueda, Shotaro Okajima, Shingo Shimoda
Publikováno v:
Physiological Reports, Vol 10, Iss 10, Pp n/a-n/a (2022)
Abstract Superficial Electromyography (sEMG) spectrum contains aggregated information from several underlying physiological processes. Due to technological limitations, the isolation of these processes is challenging, and therefore, the interpretatio
Externí odkaz:
https://doaj.org/article/e779d0e0814346be8464634719d28871
Publikováno v:
Neuroscience Informatics, Vol 1, Iss 4, Pp 100029- (2021)
Brain-Computer interface systems allow the recognition of neuronal activity to create a direct communication channel between the brain and the outside world without using the peripheral nervous system. Many of the paradigms used are based on the dete
Externí odkaz:
https://doaj.org/article/eb69e89f416247488946e1839731c03f
Publikováno v:
Biosensors, Vol 12, Iss 8, p 555 (2022)
In the EEG literature, there is a lack of asynchronous intention models that realistically propose interfaces for applications that must operate in real time. In this work, a novel BMI approach to detect in real time the intention to turn is proposed
Externí odkaz:
https://doaj.org/article/e04530d87b99434fba4696503e03fba6