2018-03-142018-03-02SILVA, A. D. Desenvolvimento de um dispositivo SSVEP rápido e confiável utilizando eletrodos a seco e frequências acima de 25 Hz. 2018. 98 f. Dissertação (Mestrado em Engenharia Elétrica e da Computação) - Universidade Federal de Goiás, Goiânia, 2018.http://repositorio.bc.ufg.br/tede/handle/tede/8215This 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.application/pdfAcesso AbertoEEGICCPotenciais evocados visuaisSSVEPBCIEEGVisual evoked potentialsSSVEPCIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAODissertação