Desenvolvimento de um dispositivo SSVEP rápido e confiável utilizando eletrodos a seco e frequências acima de 25 Hz
This paper presents a new approach for the processing and classification of visual evoked potentials of steady state (SSVEP). It introduces a ensemble tree model that combines canonical correlation analysis data with methods based on estimation of power spectral density. The stimuli were created usi...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Brasil |
| Institución: | Universidade Federal de Goiás (UFG) |
| Repositorio: | Repositório Institucional da UFG |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.bc.ufg.br:tede/8215 |
| Acceso en línea: | http://repositorio.bc.ufg.br/tede/handle/tede/8215 |
| Access Level: | acceso abierto |
| Palabra clave: | EEG ICC Potenciais evocados visuais SSVEP BCI Visual evoked potentials CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO |
| Sumario: | This paper presents a new approach for the processing and classification of visual evoked potentials of steady state (SSVEP). It introduces a ensemble tree model that combines canonical correlation analysis data with methods based on estimation of power spectral density. The stimuli were created using LEDs, from 7.04 Hz to 38.46 Hz. Data were collected using the Texas Instruments ADS1299EEG-Fe and three electrodes. The tests were performed for different distances and light intensities to evaluate the performance of the algorithm under different conditions. In all, 22 participants were recruited, and the average classification was 99.1 ± 2.27% with fixed decision time of 1 second. |
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