EEG Signals classification using linear and non-linear discriminant methods
This article was developed with the particular interest of characterize and study EEG signals as a pattern which in general has a high dimensionality, and has obviously a particular behavior in frequency and time. Here we have developed a wavelet decomposition to reduce a little bit the dimensionali...
- Autores:
-
Mayor Torres, Juan Manuel
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2013
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/10822
- Acceso en línea:
- http://hdl.handle.net/10614/10822
- Palabra clave:
- linear classifiers
feature extraction
biomedical signals
feature extraction
Biomedical signals
- Rights
- openAccess
- License
- Derechos Reservados - Universidad Autónoma de Occidente
| Summary: | This article was developed with the particular interest of characterize and study EEG signals as a pattern which in general has a high dimensionality, and has obviously a particular behavior in frequency and time. Here we have developed a wavelet decomposition to reduce a little bit the dimensionality and PCA (Principal Components Analysis) to accurate the result in a better way (only two features representation). After that the EEG signals, with their respective characteristics and representation has been able to train and test some linear and non-linear classifiers such as (Parzen, k-NN, Radial Basis Neural Network, linear and non-linear perceptron and so on.) This evaluation is an analysis of general EEG’s behavior signals with this kind of characterization and classification processes respectively. |
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