9783846509067 - Wavelet-Fractal & Neuro-Fuzzy for Brain Computer Interface Application: A Series of Novel Approaches Toward EEG-based Brain-Computer Interface Systems de Hsu, Wei-Y... (9 resultados)

Condición: New. pp. 104.

Taschenbuch. Condición: Neu. Wavelet-Fractal & Neuro-Fuzzy for Brain Computer Interface Application | A Series of Novel Approaches Toward EEG-based Brain-Computer Interface Systems | Wei-Yen Hsu | Taschenbuch | 104 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846509067 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 104 pp. Englisch.

Condición: New. Print on Demand pp. 104 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Hsu Wei-YenHe 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 includ.

Condición: New. PRINT ON DEMAND pp. 104.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.