Mostrando 4 resultados de: 4
Filtros aplicados
Subtipo de publicación
Conference Object(4)
Área temáticas
Fisiología humana(4)
Enfermedades(2)
Medicina y salud(2)
Ciencias de la computación(1)
Física aplicada(1)
A Fine Dry-Electrode Selection to Characterize Event-Related Potentials in the Context of BCI
Conference ObjectAbstract: A brain-computer interface (BCI) detects brain activity and converts it to external commands, faciliPalabras claves:Bayesian linear discriminant analysis, EEG signal, Electrode selection, Event-related potentials, Inter- and intrasubject variability, Low-cost BCI, Oddball paradigmAutores:Rodríguez F.B., Varona P., Vinicio ChangoluisaFuentes:googlescopusHow to reduce classification error in ERP-based BCI: Maximum relative areas as a feature for p300 detection
Conference ObjectAbstract: Currently, one of the challenges in a Brain Computer Interface (BCI) technologies is the improvementPalabras claves:Brain computer interface, Event-related potentials, Lda, online, P300Autores:Rodríguez F.B., Varona P., Vinicio ChangoluisaFuentes:googlescopusP300 Characterization Through Granger Causal Connectivity in the Context of Brain-Computer Interface Technologies
Conference ObjectAbstract: The analysis of connectivity in brain networks has been widely researched and it has been shown thatPalabras claves:Bayesian linear discriminant analysis, Brain networks, EEG signal, Event-related potential, Functional connectivity, Inter-subject variability, Oddball paradigm, Sequential forward electrode selection, Standard electrodesAutores:Rodríguez F.B., Salazar V., Vanessa Salazar Palacios, Vinicio ChangoluisaFuentes:googlescopusPrecise Temporal P300 Detection in Brain Computer Interface EEG Signals Using a Long-Short Term Memory
Conference ObjectAbstract: Event-Related Potentials (ERP) detection is a latent problem in the clinical, neuroscience, and engiPalabras claves:Bayesian LDA, Detection of P300 at sample level, Event-related potential, Inter- and intra-subject variability, Oddball paradigm, P300 latency variability, Recurrent neural networksAutores:Lago-Fernández L.F., Oliva C., Rodríguez F.B., Vinicio ChangoluisaFuentes:googlescopus