Design and Implementation of Multi-channel Active Electrode EEG Device
Performance analysis using Motor Imagery Brainwaves
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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...
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).
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