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Descripción
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| Brain Computer Interface is an emerging technology that allows new output paths to communicate the user?s intentions without the use of normal output paths, such as muscles or nerves. In order to obtain their objective, BCI devices make use of classifiers which translate inputs from the user?s brain signals into commands for external devices. This paper describes and compares the results of three types of classifiers based on three different types of neural networks: Radial Basis Functions (RBF), Probabilistic Neural Networks (PNN), and Multi-Layer Perceptrons (MLP). Before classifying the electroencephalographic signal into one of the different mental tasks used during the training phase, the signal is windowed with seven different types of preprocessing windows; so as to increase its discrimination capability. Tests carried out on five healthy volunteers resulted in the attainment of an estimation of the success rate of each classifier. This allows for the selection of the best type and architecture of neural network, as well as usage of the preprocessing window in the classifier. | |
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Internacional
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Si |
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DOI
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Edición del Libro
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Editorial del Libro
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InTech |
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ISBN
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978-953-307-175-6 |
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Serie
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Título del Libro
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Recent Advances in Brain-Computer Interface Systems |
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Desde página
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25 |
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Hasta página
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66 |