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Stress data analysis with EEG using machine learning

Grant number: 17/12213-0
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): February 01, 2018
Effective date (End): January 31, 2019
Field of knowledge:Engineering - Biomedical Engineering - Bioengineering
Principal Investigator:Carlos Dias Maciel
Grantee:Rafael Augusto Caracciolo Arone
Home Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

The electroencephalogram is a device that allows obtaining brain signals of the specific skull region, one of these informations that can be taken from it is the stress level of anyone. in that can be done by signal filtrage and clustering with machine learning algorithms, more specifically using Neural Networks with multilayer Perceptrons. Besides, this research uses the Montreal Imaging Stress Task for create in one person a situation that can get signal with can get the signals, and the created algorithm, be capable of measure various stress levels. (AU)