Histogram of gradient orientations of EEG signal plots for brain computer interfaces

"Brain Computer Interface (BCI) or Brain Machine Interfaces (BMI), has proved the feasibility of a distinct non-biological communication channel to transmit information from the Central Nervous System (CNS) to a computer device. Promising success has been achieved with invasive BCI, though bioc...

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Autor principal: Ramele, Rodrigo
Otros Autores: Santos, Juan Miguel
Formato: Tesis de doctorado
Lenguaje:Inglés
Publicado: 2018
Materias:
ELECTROENCEFALOGRAFIA
PROCESAMIENTO DE SEÑALES
ALGORITMOS
INTERFAZ CEREBRO COMPUTADORA
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/1369
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-1369
record_format dspace
spelling I32-R138-123456789-13692022-12-07T15:17:52Z Histogram of gradient orientations of EEG signal plots for brain computer interfaces Ramele, Rodrigo Santos, Juan Miguel Villar, Ana Julia ELECTROENCEFALOGRAFIA PROCESAMIENTO DE SEÑALES ALGORITMOS INTERFAZ CEREBRO COMPUTADORA "Brain Computer Interface (BCI) or Brain Machine Interfaces (BMI), has proved the feasibility of a distinct non-biological communication channel to transmit information from the Central Nervous System (CNS) to a computer device. Promising success has been achieved with invasive BCI, though biocompatibilities issues and the complexity and risks of surgical procedures are the main drive to enhance current non-invasive technologies. Electroencephalography (EEG) is the most widespread method to gather information from the CNS in a non-invasive way. Clinical EEG has traditionally focused on temporal waveforms, but signal analysis methods which follow this path have been neglected in BCI research. This Thesis proposes a method and framework to analyze the waveform, the shape of the EEG signal, using the histogram of gradient orientations, a fruitful technique from Computer Vision which is used to characterize image local features. Inspiration comes from what traditionally electroencephalographers have been doing for almost a century: visually inspecting raw EEG signal plots." "Las interfaces BCI (Brain Computer Interfaces, interfaces cerebro computadora) o BMI (Brain Machine Interfaces, interfaces cerebro máquina) han surgido como un nuevo canal de comunicación entre el cerebro y las computadoras, máquinas o robots, distinto de los canales biológicos estándar. Se han obtenido resultados prometedores en el empleo de la variante invasiva de BCI pero, además de los problemas de biocompatibilidad, los procedimientos quirúrgicos requeridos son complejos y riesgosos. Estas razones, han impulsado las mejoras de las tecnologías no invasivas. La electroencefalografía (EEG) es el método más difundido para obtener información del sistema nervioso central de manera no invasiva. La electroencefalografía clínica se ha enfocado tradicionalmente en el estudio de las formas de ondas temporales, pero los métodos de procesamiento de señales que exploren esta metodología han sido ignorados en las investigaciones sobre BCI." Tesis Ingeniería Informática (doctorado) - Instituto Tecnológico de Buenos Aires, Buenos Aires, 2018 2018-12-10T13:55:49Z 2018-12-10T13:55:49Z 2018 Tesis de doctorado http://ri.itba.edu.ar/handle/123456789/1369 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 ELECTROENCEFALOGRAFIA
PROCESAMIENTO DE SEÑALES
ALGORITMOS
INTERFAZ CEREBRO COMPUTADORA
spellingShingle ELECTROENCEFALOGRAFIA
PROCESAMIENTO DE SEÑALES
ALGORITMOS
INTERFAZ CEREBRO COMPUTADORA
Ramele, Rodrigo
Histogram of gradient orientations of EEG signal plots for brain computer interfaces
topic_facet ELECTROENCEFALOGRAFIA
PROCESAMIENTO DE SEÑALES
ALGORITMOS
INTERFAZ CEREBRO COMPUTADORA
description "Brain Computer Interface (BCI) or Brain Machine Interfaces (BMI), has proved the feasibility of a distinct non-biological communication channel to transmit information from the Central Nervous System (CNS) to a computer device. Promising success has been achieved with invasive BCI, though biocompatibilities issues and the complexity and risks of surgical procedures are the main drive to enhance current non-invasive technologies. Electroencephalography (EEG) is the most widespread method to gather information from the CNS in a non-invasive way. Clinical EEG has traditionally focused on temporal waveforms, but signal analysis methods which follow this path have been neglected in BCI research. This Thesis proposes a method and framework to analyze the waveform, the shape of the EEG signal, using the histogram of gradient orientations, a fruitful technique from Computer Vision which is used to characterize image local features. Inspiration comes from what traditionally electroencephalographers have been doing for almost a century: visually inspecting raw EEG signal plots."
author2 Santos, Juan Miguel
author_facet Santos, Juan Miguel
Ramele, Rodrigo
format Tesis de doctorado
author Ramele, Rodrigo
author_sort Ramele, Rodrigo
title Histogram of gradient orientations of EEG signal plots for brain computer interfaces
title_short Histogram of gradient orientations of EEG signal plots for brain computer interfaces
title_full Histogram of gradient orientations of EEG signal plots for brain computer interfaces
title_fullStr Histogram of gradient orientations of EEG signal plots for brain computer interfaces
title_full_unstemmed Histogram of gradient orientations of EEG signal plots for brain computer interfaces
title_sort histogram of gradient orientations of eeg signal plots for brain computer interfaces
publishDate 2018
url http://ri.itba.edu.ar/handle/123456789/1369
work_keys_str_mv AT ramelerodrigo histogramofgradientorientationsofeegsignalplotsforbraincomputerinterfaces
_version_ 1765660960343719936

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