Brain electrical activity discriminant analysis using Reproducing Kernel Hilbert spaces
A deep an adequate understanding of the human brain functions has been an objective for interdisciplinar teams of scientists. Different types of technological acquisition methodologies, allow to capture some particular data that is related with brain activity. Commonly, the more used strategies are...
- Autores:
-
Torres Valencia, Cristian Alejando
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Pereira
- Repositorio:
- Repositorio Institucional UTP
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utp.edu.co:11059/12974
- Acceso en línea:
- https://hdl.handle.net/11059/12974
- Palabra clave:
- Encefalografía
Sistema nervioso - Enfermedades - Fisioterapia
Electrical Engineering
- Rights
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
| Summary: | A deep an adequate understanding of the human brain functions has been an objective for interdisciplinar teams of scientists. Different types of technological acquisition methodologies, allow to capture some particular data that is related with brain activity. Commonly, the more used strategies are related with the brain electrical activity, where reflected neuronal interactions are reflected in the scalp and obtained via electrode arrays as time series. The processing of this type of brain electrical activity (BEA) data, poses some challenges that should be addressed carefully due their intrinsic properties. BEA in known to have a nonstationaty behavior and a high degree of variability dependenig of the stimulus or responses that are being adressed... |
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