Wavelet-Fractal & Neuro-Fuzzy for Brain Computer Interface Application: A Series of Novel Approaches Toward EEG-based Brain-Computer Interface Systems - Couverture souple

Hsu, Wei-Yen

 
9783846509067: Wavelet-Fractal & Neuro-Fuzzy for Brain Computer Interface Application: A Series of Novel Approaches Toward EEG-based Brain-Computer Interface Systems
Couverture souple
ISBN 10 : 384650906X ISBN 13 : 9783846509067
Editeur : LAP LAMBERT Academic Publishing, 2011

Synopsis

The ultimate objective of brain-computer interface (BCI) is to provide humans an alternative communication channel allowing direct transmission of messages from the brain to a computer by analyzing the brain’s mental activities. The BCI is usually described that a person has the ability to communicate with others without the prerequisite of brain’s normal output pathways of peripheral nerves and muscles by controlling his own electroencephalographic (EEG) signals. In BCI applications, the performance and reliability of mental task analysis greatly depend on feature extraction and representation. In this book, a series of methods are proposed, including t-test-weighted time-scale plot, active segment selection, multiresolution fractal feature vector (MFFV) associated with genetic algorithm (GA), and neuro-fuzzy time-series prediction to effectively extract, select, and represent features for better classification accuracy. Compared to other well-known approaches, the experimental results show that the proposed methods are superior on both real finger movement and motor-imagery (MI) data for BCI applications.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

The ultimate objective of brain-computer interface (BCI) is to provide humans an alternative communication channel allowing direct transmission of messages from the brain to a computer by analyzing the brain’s mental activities. The BCI is usually described that a person has the ability to communicate with others without the prerequisite of brain’s normal output pathways of peripheral nerves and muscles by controlling his own electroencephalographic (EEG) signals. In BCI applications, the performance and reliability of mental task analysis greatly depend on feature extraction and representation. In this book, a series of methods are proposed, including t-test-weighted time-scale plot, active segment selection, multiresolution fractal feature vector (MFFV) associated with genetic algorithm (GA), and neuro-fuzzy time-series prediction to effectively extract, select, and represent features for better classification accuracy. Compared to other well-known approaches, the experimental results show that the proposed methods are superior on both real finger movement and motor-imagery (MI) data for BCI applications.

Biographie de l'auteur

He received the MS and PhD degrees in the Department of Computer Science and Information Engineering from National Cheng Kung University, Tainan, Taiwan in 2001 and 2008, respectively. His current research interests include medical image processing, biomedical signal processing, neuroscience methods, and computer vision and graphics.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2011
Langue
anglais
ISBN 10 
384650906X
ISBN 13 
9783846509067
Reliure
Broché
Nombre de pages
104
Coordonnées du fabricant
non disponible
Personne responsable
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