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

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
Softcover
ISBN 10: 384650906X ISBN 13: 9783846509067
Verlag: LAP LAMBERT Academic Publishing, 2011

Inhaltsangabe

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.

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Reseña del editor

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.

Biografía del autor

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.

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Verlag
LAP LAMBERT Academic Publishing
Erscheinungsdatum
2011
Sprache
Englisch
ISBN 10 
384650906X
ISBN 13 
9783846509067
Einband
Tapa blanda
Anzahl der Seiten
104
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Verantwortliche Person
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