Caracterização da atividade eletroencefalográfica em diferentes faixas etárias, por meio da análise discriminante linear

• Ano de defesa: 2012
• Autor(a) principal: Paiva, Lílian Ribeiro Mendes
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; BR; Programa de Pós-graduação em Engenharia Elétrica; Engenharias; UFU
• Palavras-chave em Português: Envelhecimento; Sinais eletroencefalográficos (EEG); Linear discriminant analysis (LDA); Engenharia biomédica; Eletroencefalografia; Aging; Electroencephalographic signals (EEG); CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/14308; https://doi.org/10.14393/ufu.te.2012.36

Aging is directly linked to adverse impacts related to factors that affect the chronological age-related changes such as heredity, environment, diet, lifestyle, and habit of practicing physical exercises, among other features. The Central Nervous System (CNS) and neuronal signals carry information that represents changes throughout life. In this context, this study seeks to establish some correlation between brain activity as a function of age, from the record of electroencephalographic signals (EEG), in subjects not suffering from neurological disorders, while performing a certain task. There were 59 healthy subjects that voluntarily participated in this study, which were divided into 07 groups, with ages between 20 and 86 years and both sexes. EEG signals were collected \"simultaneously\" in three different experimental protocols during the execution of the Spiral of Archimedes (Ingoing Spiral, Outgoing Spiral and stopped up in the center). The electrodes were positioned according to the international standard 10/20, using the channels C3 and C4 of the central region. Statistical analyses were performed to identify differences and allow discrimination between the characteristics of each group according to the presented changes. The data were processed with software MATLAB. Among the results, significant differences were observed, via LDA-value, Linear Discriminant Analysis (LDA), a technique to optimize the extraction of discriminating information from a data set. The tool has satisfactorily performed the separation of discriminant features, classifying each group of individuals that have high correlation as a function of age. It can be concluded by the analysis of the characteristics used that there is the separability between groups according to age, contributing significantly to register the changes that occurred during the aging process.