Assessment of sub-band division and energy computation techniques as fundamental stages for a neuro-feedback training system

The improvement of skills and cognitive abilities by means of neurofeedback training has been turned into an issue of interest in healthy population. These studies have shown a positive correlation between the neurofeedback training and the improvement of the cognitive skills of the people. Typicall...

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Autores:
Tipo de recurso:
Fecha de publicación:
2016
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
eng
OAI Identifier:
oai:repository.udem.edu.co:11407/3143
Acceso en línea:
http://hdl.handle.net/11407/3143
Palabra clave:
Feedback
Separation
Signal processing
Vision
Cognitive ability
Computation techniques
Energy calculation
Frequency sub band
Healthy population
Non-stationary behaviors
Positive correlations
Training Systems
Image processing
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restrictedAccess
License
http://purl.org/coar/access_right/c_16ec
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oai_identifier_str oai:repository.udem.edu.co:11407/3143
network_acronym_str REPOUDEM2
network_name_str Repositorio UDEM
repository_id_str
spelling 2017-05-12T16:05:56Z2017-05-12T16:05:56Z20169781509037971http://hdl.handle.net/11407/314310.1109/STSIVA.2016.7743332The improvement of skills and cognitive abilities by means of neurofeedback training has been turned into an issue of interest in healthy population. These studies have shown a positive correlation between the neurofeedback training and the improvement of the cognitive skills of the people. Typically, in a neurofeedback system the first stage is the artifact remotion, the next stage is the separation of the EEG signal into frequency sub-bands and the last stage is the characterization of the sub-bands energy. Aiming to obtain the desired feedback, the mentioned stages have to be done as quickly and as accurately as possible. A mistake in these stages can lead to consequences as simple as a fruitless training, altering the desired cognitive improvement. In this paper, different techniques for sub-band separation and characterization are compared, aiming to find the most suitable techniques in order to be applied in a neurofeedback system, the techniques are collated according to the non-stationary behavior of the EEG signal and the stability (variability) of the outputs. Results show that the most stable and stationary combination is that determined by the EEG separation through IFFT and the energy calculation through the Teager-Kaiser, followed by its improved version. As conclusion, the IFFT for EEG sub-band separation, and Teager-Kaiser or its improvement for energy calculation, are recommend for a Neurofeedback system for cognitive improvement in healthy population. © 2016 IEEE.engInstitute of Electrical and Electronics Engineers Inc.http://ieeexplore.ieee.org/document/7743332/2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016ScopusAssessment of sub-band division and energy computation techniques as fundamental stages for a neuro-feedback training systemConference Paperinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/coar/resource_type/c_c94finfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/access_right/c_16ecSepulveda-Cano, L.M., Universidad de Medellín, Medellín, ColombiaDaza-Santacoloma, G., Instituto de Epilepsia y Parkinson Del Eje Cafetero S.A., Pereira, ColombiaSepulveda-Cano L.M.Daza-Santacoloma G.FeedbackSeparationSignal processingVisionCognitive abilityComputation techniquesEnergy calculationFrequency sub bandHealthy populationNon-stationary behaviorsPositive correlationsTraining SystemsImage processing11407/3143oai:repository.udem.edu.co:11407/31432020-05-27 16:26:01.714Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co
dc.title.spa.fl_str_mv Assessment of sub-band division and energy computation techniques as fundamental stages for a neuro-feedback training system
title Assessment of sub-band division and energy computation techniques as fundamental stages for a neuro-feedback training system
spellingShingle Assessment of sub-band division and energy computation techniques as fundamental stages for a neuro-feedback training system
Feedback
Separation
Signal processing
Vision
Cognitive ability
Computation techniques
Energy calculation
Frequency sub band
Healthy population
Non-stationary behaviors
Positive correlations
Training Systems
Image processing
title_short Assessment of sub-band division and energy computation techniques as fundamental stages for a neuro-feedback training system
title_full Assessment of sub-band division and energy computation techniques as fundamental stages for a neuro-feedback training system
title_fullStr Assessment of sub-band division and energy computation techniques as fundamental stages for a neuro-feedback training system
title_full_unstemmed Assessment of sub-band division and energy computation techniques as fundamental stages for a neuro-feedback training system
title_sort Assessment of sub-band division and energy computation techniques as fundamental stages for a neuro-feedback training system
dc.contributor.affiliation.spa.fl_str_mv Sepulveda-Cano, L.M., Universidad de Medellín, Medellín, Colombia
Daza-Santacoloma, G., Instituto de Epilepsia y Parkinson Del Eje Cafetero S.A., Pereira, Colombia
dc.subject.keyword.eng.fl_str_mv Feedback
Separation
Signal processing
Vision
Cognitive ability
Computation techniques
Energy calculation
Frequency sub band
Healthy population
Non-stationary behaviors
Positive correlations
Training Systems
Image processing
topic Feedback
Separation
Signal processing
Vision
Cognitive ability
Computation techniques
Energy calculation
Frequency sub band
Healthy population
Non-stationary behaviors
Positive correlations
Training Systems
Image processing
description The improvement of skills and cognitive abilities by means of neurofeedback training has been turned into an issue of interest in healthy population. These studies have shown a positive correlation between the neurofeedback training and the improvement of the cognitive skills of the people. Typically, in a neurofeedback system the first stage is the artifact remotion, the next stage is the separation of the EEG signal into frequency sub-bands and the last stage is the characterization of the sub-bands energy. Aiming to obtain the desired feedback, the mentioned stages have to be done as quickly and as accurately as possible. A mistake in these stages can lead to consequences as simple as a fruitless training, altering the desired cognitive improvement. In this paper, different techniques for sub-band separation and characterization are compared, aiming to find the most suitable techniques in order to be applied in a neurofeedback system, the techniques are collated according to the non-stationary behavior of the EEG signal and the stability (variability) of the outputs. Results show that the most stable and stationary combination is that determined by the EEG separation through IFFT and the energy calculation through the Teager-Kaiser, followed by its improved version. As conclusion, the IFFT for EEG sub-band separation, and Teager-Kaiser or its improvement for energy calculation, are recommend for a Neurofeedback system for cognitive improvement in healthy population. © 2016 IEEE.
publishDate 2016
dc.date.created.none.fl_str_mv 2016
dc.date.accessioned.none.fl_str_mv 2017-05-12T16:05:56Z
dc.date.available.none.fl_str_mv 2017-05-12T16:05:56Z
dc.type.eng.fl_str_mv Conference Paper
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_c94f
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/conferenceObject
dc.identifier.isbn.none.fl_str_mv 9781509037971
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11407/3143
dc.identifier.doi.none.fl_str_mv 10.1109/STSIVA.2016.7743332
identifier_str_mv 9781509037971
10.1109/STSIVA.2016.7743332
url http://hdl.handle.net/11407/3143
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.isversionof.spa.fl_str_mv http://ieeexplore.ieee.org/document/7743332/
dc.relation.ispartofes.spa.fl_str_mv 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
dc.rights.accessrights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
rights_invalid_str_mv http://purl.org/coar/access_right/c_16ec
dc.publisher.spa.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.spa.fl_str_mv Scopus
institution Universidad de Medellín
repository.name.fl_str_mv Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv repositorio@udem.edu.co
_version_ 1814159139622879232

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