Interface Cérebro-Computador Baseada em EEG Utilizando Redes Neurais Auto-Organizadas

Detalhes bibliográficos
Ano de defesa: 2017
Autor(a) principal: Bueno, Leandro
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal do Espírito Santo
BR
Doutorado em Engenharia Elétrica
Centro Tecnológico
UFES
Programa de Pós-Graduação em Engenharia Elétrica
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
EEG
Link de acesso: http://repositorio.ufes.br/handle/10/9689
Resumo: This Doctoral Thesis presents the development of a Brain Computer Inter-face (BCI) system using Electroencephalography (EEG) signals and Self OrganizingMaps (SOM) artificial neural networks as classifier. In this Thesis the problems ofa BCI are analyzed and the classification results of the system is presented. Thissystem uses a clinic acquisition equipment for EEG signal acquisition and a personalcomputer to process the data, using the energy of the frequency components of theEEG signal as characteristics and a classifier based on a Self Organizing Map asclassifier. The great challenge in using SOM as a classifier is the interpretation ofthe outputs of the map, as it has as many outputs as it has neurons in the map.The contribution of this Thesis is in the interpretation method of the outputs ofthe map, which is done by means of the use of a set of masks that represents theprobability of the activation of a neuron in the map representing a specific class.The algorithms used on this Doctoral Thesis can be easily adapted to be executed inembedded systems with less processing power, like Digital Signal Processors (DSP)or microcontrollers. The Brain Computer Interface developed in this Doctoral The-sis was tested and validated off–line, with an external database, and with data fromvolunteers, presenting satisfactory results in both cases, according to similar resultsfrom the literature.