• JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
 
  Bookmark and Share
 
 
Master's Dissertation
DOI
Document
Author
Full name
Christian Humberto Flores Vega
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2010
Supervisor
Committee
Ramírez Fernandez, Francisco Javier (President)
Ballester, Gerson
Lorena, Ana Carolina
Title in Portuguese
Reconhecimento de estados cognitivos em sinas EEG.
Keywords in Portuguese
Detrended Fluctuations Analysis (DFA)
EEG
Processos cognitivos
Transformada de Hilbert
Transformada Ondeleta
Abstract in Portuguese
O processamento de sinais EEG permite interpretar, analisar, estudar, pesquisar e experimentar a atividade elétrica do cérebro como resposta para diferentes processos cognitivos, efeitos de drogas ou fármacos, estudo de doenças psiquiátricas ou neurológicas, entre outras. Esta dissertação é orientada ao reconhecimento de padrões cerebrais que permitam classificar estados cognitivos mediante os sinais de EEG registrados em sujeitos realizando tarefas programadas. Ademais espera-se obter a maior quantidade de padrões para cada estado cognitivo e procurar os parâmetros que oferecem maior informação, analisando as principais bandas cerebrais e todos os eletrodos disponíveis na base de dados. A metodologia usada compreende o registro de cinco tarefas cognitivas analisadas com três abordagens diferentes: análises de longe-range tenporal correlations com o algoritmo de Detrended Fluctuations Analysis (DFA), análise da potência dos sinais cerebrais utilizando a Transformada Ondeleta e finalmente o estudo da sincronia cerebral usando a Transformada de Hilbert. Conclui-se que as abordagens utilizadas nesta dissertação reportam alentadores resultados para diferenciar as tarefas cognitivas estudadas, demonstrando que a utilização da informação de todos os eletrodos e de suas principais bandas cerebrais contribuem de forma positiva. Também se consegue reconhecer e identificar quais parâmetros produzem maior informação para esta análise.
Title in English
Recognition of cognitive states in EEG signals.
Keywords in English
Cognitive processes
Detrended Fluctuations Analysis (DFA)
EEG
Hilbert transform
Wavelet Transform
Abstract in English
EEG signal processing allows interpreting, analyzing, studying, researching and experiencing the brain electrical activity in response to different cognitive processes, effects of drugs or drugs, the study of neurological or psychiatric diseases, among others. This thesis is oriented to the recognition of brain patterns to classify cognitive states using the EEG signals recorded from subjects performing mental tasks. Also, we expect to collect as many patterns as possible for each cognitive status and to seek parameters that provide more information, examine the major bands and all brain electrodes available in the database. The methodology used includes the registration of five cognitive tasks analyzed with three different approaches: analysis of long-range temporal-correlations with the Detrended Fluctuations Analysis (DFA) algorithm, the power analysis of brain signals using the Wavelet Transform and finally the study of phased looked brain using the Hilbert transform. The approaches used for this research report excellent results for differentiating the cognitive tasks studied, showing that the use of information from all the electrodes and their main brain bands contribute positively. Also, one can recognize and identify which parameters produce more information for this analysis.
 
WARNING - Viewing this document is conditioned on your acceptance of the following terms of use:
This document is only for private use for research and teaching activities. Reproduction for commercial use is forbidden. This rights cover the whole data about this document as well as its contents. Any uses or copies of this document in whole or in part must include the author's name.
Publishing Date
2010-09-23
 
WARNING: Learn what derived works are clicking here.
All rights of the thesis/dissertation are from the authors
CeTI-SC/STI
Digital Library of Theses and Dissertations of USP. Copyright © 2001-2021. All rights reserved.