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Linear vs nonlinear classification of social joint attention in autism using VR P300-based brain computer interfaces
Citation
Arancibia Casillas, Lucía de, Sánchez González, Patricia 
ORCID: https://orcid.org/0000-0001-9871-0884, Gómez Aguilera, Enrique J. ORCID: https://orcid.org/0000-0001-6998-1407, Hernando Pérez, María Elena ORCID: https://orcid.org/0000-0001-6182-313X and Oropesa García, Ignacio ORCID: https://orcid.org/0000-0003-4560-285X
(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 |
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| Author/s: |
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| 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 |
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 |
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| DC Identifier: | https://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: | 01 Apr 2023 08:18 |
