Quasi-Newton iteration algorithm for ICA and its application in VEP feature extraction / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 45-48, 2006.
Article
Dans Chinois
| WPRIM
| ID: wpr-309888
ABSTRACT
Some noises still exist in the single-trial averaged visual evoked potentials (VEP), so further extraction of the above results is of significance. Independent component analysis (ICA)can separate the sources from their mixtures and make the output statistically as independent as possible; it can remove noises effectively. In this paper, the principle, experiment analyses and results of ICA based on quasi-Newton iteration rule for VEP feature extraction are introduced, It is compared with the fixed-point FastICA algorithm. The experiment results show that the provided algorithm may reinforce signals effectively and extract distinct P300 from the single-trial averaged VEP. It is of good applicability.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Physiologie
/
Algorithmes
/
Traitement du signal assisté par ordinateur
/
Reconnaissance automatique des formes
/
Potentiels évoqués cognitifs P300
/
Analyse en composantes principales
/
Potentiels évoqués visuels
/
Méthodes
Type d'étude:
Étude pronostique
Limites du sujet:
Humains
langue:
Chinois
Texte intégral:
Journal of Biomedical Engineering
Année:
2006
Type:
Article
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