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dc.description.abstractThe 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.eng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.titleAssessment of sub-band division and energy computation techniques as fundamental stages for a neuro-feedback training systemspa
dc.typeConference Papereng
dc.contributor.affiliationSepulveda-Cano, L.M., Universidad de Medellín, Medellín, Colombiaspa
dc.contributor.affiliationDaza-Santacoloma, G., Instituto de Epilepsia y Parkinson Del Eje Cafetero S.A., Pereira, Colombiaspa
dc.subject.keywordSignal processingeng
dc.subject.keywordCognitive abilityeng
dc.subject.keywordComputation techniqueseng
dc.subject.keywordEnergy calculationeng
dc.subject.keywordFrequency sub bandeng
dc.subject.keywordHealthy populationeng
dc.subject.keywordNon-stationary behaviorseng
dc.subject.keywordPositive correlationseng
dc.subject.keywordTraining Systemseng
dc.subject.keywordImage processingeng
dc.relation.ispartofes2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016spa

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