Histogram of gradient orientations of signal plots applied to P300 detection

"The analysis of Electroencephalographic (EEG) signals is of ulterior importance to aid in the diagnosis of mental disease and to increase our understanding of the brain. Traditionally, clinical EEG has been analyzed in terms of temporal waveforms, looking at rhythms in spontaneous activity, su...

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Autores principales: Ramele, Rodrigo, Villar, Ana Julia, Santos, Juan Miguel
Formato: Artículos de Publicaciones Periódicas publishedVersion
Lenguaje:Inglés
Publicado: 2019
Materias:
ELECTROENCEFALOGRAFIA
INTERFAZ CEREBRO COMPUTADORA
ONDAS
PROCESAMIENTO DE SEÑALES DIGITALES
ALGORITMOS
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/1769
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-1769
record_format dspace
spelling I32-R138-123456789-17692022-12-07T13:06:38Z Histogram of gradient orientations of signal plots applied to P300 detection Ramele, Rodrigo Villar, Ana Julia Santos, Juan Miguel ELECTROENCEFALOGRAFIA INTERFAZ CEREBRO COMPUTADORA ONDAS PROCESAMIENTO DE SEÑALES DIGITALES ALGORITMOS "The analysis of Electroencephalographic (EEG) signals is of ulterior importance to aid in the diagnosis of mental disease and to increase our understanding of the brain. Traditionally, clinical EEG has been analyzed in terms of temporal waveforms, looking at rhythms in spontaneous activity, subjectively identifying troughs and peaks in Event-Related Potentials (ERP), or by studying graphoelements in pathological sleep stages. Additionally, the discipline of Brain Computer Interfaces (BCI) requires new methods to decode patterns from non-invasive EEG signals. This field is developing alternative communication pathways to transmit volitional information from the Central Nervous System. The technology could potentially enhance the quality of life of patients affected by neurodegenerative disorders and other mental illness. This work mimics what electroencephalographers have been doing clinically, visually inspecting, and categorizing phenomena within the EEG by the extraction of features from images of signal plots. These features are constructed based on the calculation of histograms of oriented gradients from pixels around the signal plot. It aims to provide a new objective framework to analyze, characterize and classify EEG signal waveforms. The feasibility of the method is outlined by detecting the P300, an ERP elicited by the oddball paradigm of rare events, and implementing an offline P300-based BCI Speller. The validity of the proposal is shown by offline processing a public dataset of Amyotrophic Lateral Sclerosis (ALS) patients and an own dataset of healthy subjects." 2019-09-26T14:34:05Z 2019-09-26T14:34:05Z 2019-07 Artículos de Publicaciones Periódicas info:eu-repo/semantics/publishedVersion 1662-5188 http://ri.itba.edu.ar/handle/123456789/1769 en info:eu-repo/grantAgreement/ITBA/ITBACyT/15/AR. Ciudad Autónoma de Buenos Aires info:eu-repo/semantics/reference/doi/10.3389/fncom.2019.00043 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
INTERFAZ CEREBRO COMPUTADORA
ONDAS
PROCESAMIENTO DE SEÑALES DIGITALES
ALGORITMOS
spellingShingle ELECTROENCEFALOGRAFIA
INTERFAZ CEREBRO COMPUTADORA
ONDAS
PROCESAMIENTO DE SEÑALES DIGITALES
ALGORITMOS
Ramele, Rodrigo
Villar, Ana Julia
Santos, Juan Miguel
Histogram of gradient orientations of signal plots applied to P300 detection
topic_facet ELECTROENCEFALOGRAFIA
INTERFAZ CEREBRO COMPUTADORA
ONDAS
PROCESAMIENTO DE SEÑALES DIGITALES
ALGORITMOS
description "The analysis of Electroencephalographic (EEG) signals is of ulterior importance to aid in the diagnosis of mental disease and to increase our understanding of the brain. Traditionally, clinical EEG has been analyzed in terms of temporal waveforms, looking at rhythms in spontaneous activity, subjectively identifying troughs and peaks in Event-Related Potentials (ERP), or by studying graphoelements in pathological sleep stages. Additionally, the discipline of Brain Computer Interfaces (BCI) requires new methods to decode patterns from non-invasive EEG signals. This field is developing alternative communication pathways to transmit volitional information from the Central Nervous System. The technology could potentially enhance the quality of life of patients affected by neurodegenerative disorders and other mental illness. This work mimics what electroencephalographers have been doing clinically, visually inspecting, and categorizing phenomena within the EEG by the extraction of features from images of signal plots. These features are constructed based on the calculation of histograms of oriented gradients from pixels around the signal plot. It aims to provide a new objective framework to analyze, characterize and classify EEG signal waveforms. The feasibility of the method is outlined by detecting the P300, an ERP elicited by the oddball paradigm of rare events, and implementing an offline P300-based BCI Speller. The validity of the proposal is shown by offline processing a public dataset of Amyotrophic Lateral Sclerosis (ALS) patients and an own dataset of healthy subjects."
format Artículos de Publicaciones Periódicas
publishedVersion
author Ramele, Rodrigo
Villar, Ana Julia
Santos, Juan Miguel
author_facet Ramele, Rodrigo
Villar, Ana Julia
Santos, Juan Miguel
author_sort Ramele, Rodrigo
title Histogram of gradient orientations of signal plots applied to P300 detection
title_short Histogram of gradient orientations of signal plots applied to P300 detection
title_full Histogram of gradient orientations of signal plots applied to P300 detection
title_fullStr Histogram of gradient orientations of signal plots applied to P300 detection
title_full_unstemmed Histogram of gradient orientations of signal plots applied to P300 detection
title_sort histogram of gradient orientations of signal plots applied to p300 detection
publishDate 2019
url http://ri.itba.edu.ar/handle/123456789/1769
work_keys_str_mv AT ramelerodrigo histogramofgradientorientationsofsignalplotsappliedtop300detection
AT villaranajulia histogramofgradientorientationsofsignalplotsappliedtop300detection
AT santosjuanmiguel histogramofgradientorientationsofsignalplotsappliedtop300detection
_version_ 1765661035929272320

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