Memorias de investigación
Ponencias en congresos:
Linear vs Nonlinear Classification of Social Joint Attention in Autism Using VR P300-Based Brain Computer Interfaces
Año:2019
Áreas de investigación
  • Tecnología electrónica y de las comunicaciones
Datos
Descripción
VR P300-based BCI has proven to be a suitable method for training social attention skills in youngsters with autism spectrum disorder (ASD). In this study, we present a method that could be used in such an application to identify which object the user is paying attention to in a virtual environment by means of EEG recordings only. Temporal and time-frequency features were explored. Furthermore, the prediction accuracy of linear and nonlinear classification methods was assessed and compared, along with their computational times and complexity, and linear discriminant analysis (LDA) yielded the best overall performance (82%). The successful predictions and low computational times demonstrate the feasibility of the proposed solution for a VR-BCI neurorehabilitation tool.
Internacional
Si
Nombre congreso
XV Mediterranean Conference on Medical and Biological Engineering and Computing ? MEDICON 2019
Tipo de participación
960
Lugar del congreso
Coimbra, Portugal
Revisores
Si
ISBN o ISSN
978-3-030-31635-8
DOI
10.1007/978-3-030-31635-8
Fecha inicio congreso
26/09/2019
Fecha fin congreso
28/09/2019
Desde la página
5
Hasta la página
11
Título de las actas
Proceedings of MEDICON 2019, September 26-28, 2019, Coimbra, Portugal
Esta actividad pertenece a memorias de investigación
Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Bioingeniería y Telemedicina
  • Departamento: Tecnología Fotónica y Bioingeniería
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB