Discriminador binario de imaginación visual a partir de señales EEG basado en redes neuronales convolucionales
[EN] A Brain-Computer Intarface (BCI) is a technology that allows direct communication between the brain and the outside world without the need to use the peripheral nervous system. Most BCI systems focus on the use of motor imagination, evoked potentials, or slow cortical rhythms. In this work, the...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/178698 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/178698 |
| Access Level: | acceso abierto |
| Palavra-chave: | Brain-switch Visual imagery Convolutional neuronal network Power spectral density EEG Discriminador binario Interfaz cerebro-máquina Red neuronal convolucional Densidad potencia espectral |
| Resumo: | [EN] A Brain-Computer Intarface (BCI) is a technology that allows direct communication between the brain and the outside world without the need to use the peripheral nervous system. Most BCI systems focus on the use of motor imagination, evoked potentials, or slow cortical rhythms. In this work, the possibility of using visual imagination to construct a binary discriminator has been studied. EEG signals from seven people have been recorded while imagining seven geometric figures. Using convolutional neural networks it has been possible to distinguish between the imagination of a geometric figure and relaxation with an average success rate of 91 % with a Cohen kappa value of 0.77 and a percentage of false positives of 9 %. |
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