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Design and Implementation of Multi-channel Active Electrode EEG Device: Performance analysis using Motor Imagery Brainwaves - Couverture souple

Bista, Saroj; Adhikari, Nanda Bikram

 
9786139913367: Design and Implementation of Multi-channel Active Electrode EEG Device: Performance analysis using Motor Imagery Brainwaves
Couverture souple
ISBN 10 : 6139913365 ISBN 13 : 9786139913367
Editeur : LAP LAMBERT Academic Publishing, 2018

Synopsis

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).

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

Présentation de l'éditeur

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).

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
2018
Langue
anglais
ISBN 10 
6139913365
ISBN 13 
9786139913367
Reliure
Broché
Nombre de pages
52
Coordonnées du fabricant
non disponible
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