Abstract
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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. | |
International
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Si |
Congress
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XV Mediterranean Conference on Medical and Biological Engineering and Computing ? MEDICON 2019 |
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960 |
Place
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Coimbra, Portugal |
Reviewers
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Si |
ISBN/ISSN
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978-3-030-31635-8 |
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10.1007/978-3-030-31635-8 |
Start Date
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26/09/2019 |
End Date
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28/09/2019 |
From page
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5 |
To page
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11 |
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Proceedings of MEDICON 2019, September 26-28, 2019, Coimbra, Portugal |