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...
- 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
- Rights
- restrictedAccess
- License
- http://purl.org/coar/access_right/c_16ec
| Summary: | 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. |
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