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Linear vs Nonlinear Classification of Social Joint Attention in Autism Using VR P300-Based Brain Computer Interfaces

Año:2019

Áreas de investigación

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

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