Linear vs nonlinear classification of social joint attention in autism using VR P300-based brain computer interfaces

Arancibia Casillas, Lucía de and Sánchez González, Patricia and Gómez Aguilera, Enrique J. and Hernando Pérez, Maria Elena and Oropesa García, Ignacio (2019). Linear vs nonlinear classification of social joint attention in autism using VR P300-based brain computer interfaces. In: "15th Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON 2019)", 26/09/2019 - 28/09/2019, Coimbra, Portugal. pp. 1869-1874. https://doi.org/10.1007/978-3-030-31635-8_227.

Description

Title: Linear vs nonlinear classification of social joint attention in autism using VR P300-based brain computer interfaces
Author/s:
  • Arancibia Casillas, Lucía de
  • Sánchez González, Patricia
  • Gómez Aguilera, Enrique J.
  • Hernando Pérez, Maria Elena
  • Oropesa García, Ignacio
Item Type: Presentation at Congress or Conference (Article)
Event Title: 15th Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON 2019)
Event Dates: 26/09/2019 - 28/09/2019
Event Location: Coimbra, Portugal
Title of Book: Proceedings of 15th Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON 2019)
Date: 2019
Subjects:
Freetext Keywords: BCI; P300; Autism Spectrum Disorder; Linear discriminant analysis; Support vector machines; Virtual reality; Neurorehabilitation
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Tecnología Fotónica y Bioingeniería
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 63271
DC Identifier: http://oa.upm.es/63271/
OAI Identifier: oai:oa.upm.es:63271
DOI: 10.1007/978-3-030-31635-8_227
Official URL: https://www.springer.com/gp/book/9783030316341
Deposited by: Memoria Investigacion
Deposited on: 07 Nov 2020 10:15
Last Modified: 07 Nov 2020 10:15
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