Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/93908
Title: Headband for reading and processing brain signals
Other Titles: Bandolete para leitura e processamento de ondas cerebrais
Authors: Araújo, Cláudio Eduardo Sousa Carvalho
Orientador: Coimbra, António Paulo Mendes Breda Dias
Crisóstomo, Manuel Marques
Keywords: BCI - Brain Computer Interface; IOT - Internet of Things; EEG - Elec- troencephalography; Brain Signals; Machine Learning; BCI - Brain Computer Interface; IOT - Internet of Things; EEG - Elec- troencephalography; Brain Signals; Machine Learning
Issue Date: 24-Nov-2020
Serial title, monograph or event: Headband for reading and processing brain signals
Place of publication or event: DEEC
Abstract: Sensores de leitura de ondas cerebrais, baseados em EEG, tornaram-se populares ultima-mente, tornando-se cada vez mais precisos e baratos. Nos dias de hoje, qualquer pessoa é capazde medir ondas cerebrais e padrões de qualquer indivı́duo fora dos laboratórios médicos. Paraalém de analizar os sinais cerebrais, as aplicações podem até implementar um método de controlardispositivos electrónicos, conhecido como Brain Computer Interface. Brain-computer interface e“Internet of Things,“ estão a tornar-se cada vez mais populares já que as pessoas têm aceitadobem dispositivos electrónicos e dispositivos electrónicos inteligentes como parte do seu quotidi-ano. Nesta tese, proponho-me a explorar dispositivos EEG para um ambiente IOT, investigarsinais EEG, e desenvolver uma aplicação prova de conceito capaz de detectar tarefas mentaisespecificas utilizando algoritmos de inteligência artificial, com boa precisão de deteção. O obe-jectivo principal será o desenvolvimento de um interface entre um condutor utilizando uma BCIheadband e um dispositivo electrónico inteligente, onde os sinais cerebrains estão a ser constan-temente colectados e analizados, e numa eventual estância onde o condutor apresenta sonolência,o dispositivo electrónico inteligente deverá prontamente emitir um sinal auditório avisando outilizador de perigo.Keywords: BCI - Brain Computer Interface, IOT - Internet of Things, EEG - Elec-troencephalography, Brain Signals, EEG signals, Machine LearningKeywords: BCI - Brain Computer Interface, IOT - Internet of Things, EEG - Elec-troencephalography, Brain Signals, EEG signals, Machine Learning
Brain-wave measurement sensors, EEG based, have been popularized lately, becoming evermore precise and cheap. Nowadays, anyone is able to measure brain waves and patterns ofsomeone outside of medical laboratories. Besides analyzing brain signals, applications can evenbe implemented as a way of controlling electronic devices, know as a brain-computer interface.Brain-computer interface along with the “Internet of Things“, are becoming evermore popular aspeople have accepted wearables and smart devices as a part of our everyday life. In this thesis, Iwill attempt to explore EEG for an IOT environment, investigate EEG signals, and build a proofof concept application able to detect specific mental tasks using machine learning algorithms,with good detection accuracy. The end goal is an interface between a driver using a BCI head-band and a smart device, where brain signals are being constantly collected and processed, inthe event that the driver becomes sleepy, the smart device will promptly send an auditory signalwarning the driver of danger.According to the World Health Organization (WHO) over 1.2 million people die each year onroad accidents, between 20 and 50 million suffer non-fatal injuries and is estimated that by 2030road accidents will become the fifth major cause of death.Keywords: BCI - Brain Computer Interface, IOT - Internet of Things, EEG - Elec-troencephalography, Brain Signals, EEG signals, Machine LearningKeywords: BCI - Brain Computer Interface, IOT - Internet of Things, EEG - Elec-troencephalography, Brain Signals, EEG signals, Machine Learning
Description: Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia
URI: http://hdl.handle.net/10316/93908
Rights: openAccess
Appears in Collections:UC - Dissertações de Mestrado

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