Título:	Desenvolvimento de uma ferramenta de coleta fisiológica multimodal usando aparelhos comerciais de eletroencefalograma e rastreamento ocular : estudo de caso : piscadas
Autor(es):	Souza, Ana Paula Sandes de
E-mail do autor:	anapaulasandes.s@gmail.com
Orientador(es):	Idrobo Pizo, Gerardo Antonio
Assunto:	Eletroencefalograma Rastreamento ocular Dados
Data de publicação:	19-Ago-2024
Data de defesa:	27-Jan-2023
Referência:	SOUZA, Ana Paula Sandes de. Desenvolvimento de uma ferramenta de coleta fisiológica multimodal usando aparelhos comerciais de eletroencefalograma e rastreamento ocular: estudo de caso: piscadas. 2023. 71 f. il. Dissertação (Mestrado em Engenharia Biomédica) — Universidade de Brasília, Brasília, 2023.
Abstract:	Electroencephalogram (EEG) and Eye Tracking (ET) are non-invasive ways to observe the behavior of the nervous system. The combination of these data types tends to have a higher classification capability than unimodal datasets when used in machine learning algorithms. Besides a higher accuracy, these datasets allow a better understanding of the physiological functioning by having more than one type of information. This work presents an alternative to clinical and high-cost equipment to build multimodal datasets. By using commercial equipment, this tool can generate a multimodal dataset of EEG and ET collected over time. The collection is mediated by MATLAB code, used to coordinate a pause-and-wait system between terminals connected in parallel to both hardware used. Subsequent data combination was performed in Python through resampling and linear interpolation, allowing unimodal data to be merged despite having distinct collection frequencies. The blink signal is recognized by both devices and was used to calculate the synchronization capacity between the signals. During collection, voluntary blinks occurred every 4 seconds. A total of twelve collections of 120 second duration were analyzed. A true positive blink identification rate of 93% and 99% were found for EEG and ET, respectively. The cross-correlation showed an average delay of 1.54 seconds of the ET signal relative to the EEG signal. Advances to improve the synchronization capability between the data were suggested.
Unidade Acadêmica:	Faculdade de Ciências e Tecnologias em Engenharia (FCTE) – Campus UnB Gama
Informações adicionais:	Dissertação (mestrado) — Universidade de Brasília, Faculdade Gama, Programa de Pós-Graduação em Engenharia Biomédica, 2023.
Programa de pós-graduação:	Programa de Pós-Graduação em Engenharia Biomédica
Aparece nas coleções:	Teses, dissertações e produtos pós-doutorado

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