Versatile implementation of a hardware–software architecture for development and testing of brain–computer interfaces

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:30:16Z
Fecha de publicación2020-01-01
ResumenBrain–computer interfaces (BCI) have been focused on improving people’s lifestyles with motor or communication disabilities. However, the utilization of this technology has found news applications, such as increasing human capacities. Nowadays, several researchers are working on probing human capabilities to control several robotic devices simultaneously. The design of BCI is an intricate work that needs a long time to its implementation. For this reason, an architecture to design and implement different types of BCIs is presented in this article. The architecture has a modular design capable of reading various electroencephalography (EEG) sensors and controlling several robotic devices similar to the plug-and-play paradigm. To test the proposed architecture, a BCI was able to manage a hexapod robot and a drone was implemented. Firstly, a mobile robotic platform was designed and implemented. The BCI is based on eye blinking, where a single blinking represents a robot command. The command orders the robot to initiate or stops their locomotion for the hexapod robot. For the drone, a blink represents the takeoff or landing order. The blinking signals are obtained from the prefrontal and frontal regions of the head by EEG sensors. The signals are then filtered using temporal filters, with cutoff frequencies based on delta, theta, alpha, and beta waves. The filtered signals were labeled and used to train a classifier based on the multilayer perceptron (MLP) model. To generate the robot command, the proposal BCI used two models of MLP to ensure the classifier prediction. So, when the two classifiers make the same prediction, within a defined time interval, send the signal to the robot to start or stop its movement. The obtained results show that it is possible to get high precision to control the hexapod robot with a precision of 91.7% and an average of 81.4%.es_ES
Doihttps://doi.org/10.1177/1729881420980256es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/2231
Idiomaeses_ES
EditorialSAGE Publicationses_ES
RelaciónInternational Journal of Advanced Robotic Systemses_ES
URL relacionadohttps://doi.org/10.1177/1729881420980256es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteInternational Journal of Advanced Robotic Systems
TítuloVersatile implementation of a hardware–software architecture for development and testing of brain–computer interfaceses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorMartinez-Ledezma, Jorge Antonio
AutorBarron-Zambrano, Jose Hugo
AutorDiaz-Manriquez, Alan
AutorElizondo-Leal, Juan Carlos
AutorSaldivar-Alonso, Vicente Paul
AutorRostro-Gonzalez, Horacio
AutorMartinez-Ledezma, Jorge Antonioes_ES
AutorBarron-Zambrano, Jose Hugoes_ES
AutorDiaz-Manriquez, Alanes_ES
AutorElizondo-Leal, Juan Carloses_ES
AutorSaldivar-Alonso, Vicente Paules_ES
AutorRostro-Gonzalez, Horacioes_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número6es_ES
URL relacionadahttps://doi.org/10.1177/1729881420980256
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen17es_ES

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