|
Título:
|
EEG signal analysis via a cleaning procedure based on multivariate empirical mode decomposition
|
|
Autor/a:
|
Gallego Jutglà, Esteve; Rutkowski, E.; Cichocki, Andrej; Solé-Casals, Jordi
|
|
Otros autores:
|
Universitat de Vic. Escola Politècnica Superior; Universitat de Vic. Grup de Recerca en Tecnologies Digitals; International Joint Conference on Computational Intelligence (4rt : 2012 : Barcelona, Catalunya) |
|
Notas:
|
IJCCI 2012
Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret
or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode
decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method
to raw EEG data. Then, a synchrony measure is applied on the raw and the clean data in order to compare
the improvement of the classification rate. Two classifiers are used, linear discriminant analysis and neural
networks. For both cases, the classification rate is improved about 20%. |
|
Materia(s):
|
-Alzheimer, Malaltia d' |
|
Derechos:
|
(c) SciTePress - Science and Technology Publications
Tots els drets reservats
|
|
Tipo de documento:
|
Objeto de conferencia |
|
Editor:
|
SciTePress - Science and Technology Publications
|
|
Compartir:
|
|
