ISBN: 9786139913367 - design and implementation of multi-channel active electrode eeg device: performance analysis using motor imagery brainwaves (7 risultati)

Paperback. Condizione: Brand New. 52 pages. 8.66x5.91x0.12 inches. In Stock.

Taschenbuch. Condizione: Neu. Design and Implementation of Multi-channel Active Electrode EEG Device | Performance analysis using Motor Imagery Brainwaves | Saroj Bista (u. a.) | Taschenbuch | 52 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139913367 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a cost-effective method to record brainwave signals using three channel active electrode EEG device and classify brainwaves related to motor imagery (MI) left and right-hand movement, based on electroencephalography (EEG) measured from the central lobe, that could be used for the Brain Computer Interface (BCI) systems. The book has explained about the use of Independent Component Analysis (ICA) for the removal of EEG artifacts, and then extract the brainwaves features for MI left hand and MI right-hand movement using Wavelet Decomposition (WD). The 'Morlet' mother wavelet is used for wavelet decomposition as it shows better performance for analysis of non-stationary biomedical signals like EEG. The brainwave features like Maximum Power among all decomposition level (MMP), the Frequency corresponding to MMP (MAF), and Maximum Amplitude of the signal with MAF (MMA) are chosen as the classification features for the classification of MI brainwaves. The classification of MI brainwave signals is done using Linear Discriminant Analysis (LDA). 52 pp. Englisch.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents a cost-effective method to record brainwave signals using three channel active electrode EEG device and classify brainwaves related to motor imagery (MI) left and right-hand movement, based on electroencephalography (EEG) measured from the central lobe, that could be used for the Brain Computer Interface (BCI) systems. The book has explained about the use of Independent Component Analysis (ICA) for the removal of EEG artifacts, and then extract the brainwaves features for MI left hand and MI right-hand movement using Wavelet Decomposition (WD). The 'Morlet' mother wavelet is used for wavelet decomposition as it shows better performance for analysis of non-stationary biomedical signals like EEG. The brainwave features like Maximum Power among all decomposition level (MMP), the Frequency corresponding to MMP (MAF), and Maximum Amplitude of the signal with MAF (MMA) are chosen as the classification features for the classification of MI brainwaves. The classification of MI brainwave signals is done using Linear Discriminant Analysis (LDA). …

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Bista SarojI have completed B.E in Electronics & Communication from Nepal College of Information Technology (NCIT), Pokhara University in 2011 and I have completed MSc in Information & Communication Engineering from IOE, Pulchowk Cam.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents a cost-effective method to record brainwave signals using three channel active electrode EEG device and classify brainwaves related to motor imagery (MI) left and right-hand movement, based on electroencephalography (EEG) measured from the central lobe, that could be used for the Brain Computer Interface (BCI) systems. The book has explained about the use of Independent Component Analysis (ICA) for the removal of EEG artifacts, and then extract the brainwaves features for MI left hand and MI right-hand movement using Wavelet Decomposition (WD). The 'Morlet' mother wavelet is used for wavelet decomposition as it shows better performance for analysis of non-stationary biomedical signals like EEG. The brainwave features like Maximum Power among all decomposition level (MMP), the Frequency corresponding to MMP (MAF), and Maximum Amplitude of the signal with MAF (MMA) are chosen as the classification features for the classification of MI brainwaves. The classification of MI brainwave signals is done using Linear Discriminant Analysis (LDA).VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch. …