EEG waveform identification based on deep learning techniques

"The use of Brain-Computer Interfaces can provide substantial improvements to the quality of life of patients with diseases such as severe Amyotrophic lateral sclerosis that cause Locked-in syndrome, by creating new avenues in which these people can communicate and interact with the outside wor...

Descripción completa

Detalles Bibliográficos
Autor principal: Ail, Brian Ezequiel
Otros Autores: Ramele, Rodrigo
Formato: Proyecto final de Grado
Lenguaje:Inglés
Publicado: 2022
Materias:
APRENDIZAJE PROFUNDO
INTERFAZ CEREBRO COMPUTADORA
ELECTROENCEFALOGRAFIA
APRENDIZAJE AUTOMATICO
REDES NEURONALES
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/3815
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-3815
record_format dspace
spelling I32-R138-123456789-38152022-12-07T14:35:10Z EEG waveform identification based on deep learning techniques Ail, Brian Ezequiel Ramele, Rodrigo APRENDIZAJE PROFUNDO INTERFAZ CEREBRO COMPUTADORA ELECTROENCEFALOGRAFIA APRENDIZAJE AUTOMATICO REDES NEURONALES "The use of Brain-Computer Interfaces can provide substantial improvements to the quality of life of patients with diseases such as severe Amyotrophic lateral sclerosis that cause Locked-in syndrome, by creating new avenues in which these people can communicate and interact with the outside world. The P300 speller is an interface which provide the patients the ability to spell letters and eventually words, so that they can speak while unable to use their mouth. The P300 speller works by reading signals from the brain using an Electroencephalogram. Traditionally, these signals were plotted and interpreted by specialized technicians or neurologists, but the development of Machine learning algorithms for classification allow the computers to perform this analysis and detect the P300 signals, which is an Event Related Potential triggered when certain stimuli such as a bright light is triggered on a place that the patient is focused on. In this thesis we used a Convolutional Neural Network to train multi-channel EEG readings, and attempted to detect P300 signals from a P300 speller. The results are corroborated against a public ALS dataset." Proyecto final Ingeniería Informática (grado) - Instituto Tecnológico de Buenos Aires, Buenos Aires, 2022 2022-04-22T14:16:03Z 2022-04-22T14:16:03Z 2022 Proyecto final de Grado http://ri.itba.edu.ar/handle/123456789/3815 en application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic APRENDIZAJE PROFUNDO
INTERFAZ CEREBRO COMPUTADORA
ELECTROENCEFALOGRAFIA
APRENDIZAJE AUTOMATICO
REDES NEURONALES
spellingShingle APRENDIZAJE PROFUNDO
INTERFAZ CEREBRO COMPUTADORA
ELECTROENCEFALOGRAFIA
APRENDIZAJE AUTOMATICO
REDES NEURONALES
Ail, Brian Ezequiel
EEG waveform identification based on deep learning techniques
topic_facet APRENDIZAJE PROFUNDO
INTERFAZ CEREBRO COMPUTADORA
ELECTROENCEFALOGRAFIA
APRENDIZAJE AUTOMATICO
REDES NEURONALES
description "The use of Brain-Computer Interfaces can provide substantial improvements to the quality of life of patients with diseases such as severe Amyotrophic lateral sclerosis that cause Locked-in syndrome, by creating new avenues in which these people can communicate and interact with the outside world. The P300 speller is an interface which provide the patients the ability to spell letters and eventually words, so that they can speak while unable to use their mouth. The P300 speller works by reading signals from the brain using an Electroencephalogram. Traditionally, these signals were plotted and interpreted by specialized technicians or neurologists, but the development of Machine learning algorithms for classification allow the computers to perform this analysis and detect the P300 signals, which is an Event Related Potential triggered when certain stimuli such as a bright light is triggered on a place that the patient is focused on. In this thesis we used a Convolutional Neural Network to train multi-channel EEG readings, and attempted to detect P300 signals from a P300 speller. The results are corroborated against a public ALS dataset."
author2 Ramele, Rodrigo
author_facet Ramele, Rodrigo
Ail, Brian Ezequiel
format Proyecto final de Grado
author Ail, Brian Ezequiel
author_sort Ail, Brian Ezequiel
title EEG waveform identification based on deep learning techniques
title_short EEG waveform identification based on deep learning techniques
title_full EEG waveform identification based on deep learning techniques
title_fullStr EEG waveform identification based on deep learning techniques
title_full_unstemmed EEG waveform identification based on deep learning techniques
title_sort eeg waveform identification based on deep learning techniques
publishDate 2022
url http://ri.itba.edu.ar/handle/123456789/3815
work_keys_str_mv AT ailbrianezequiel eegwaveformidentificationbasedondeeplearningtechniques
_version_ 1765660928838205440

Ejemplares similares