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We proposed a technique to remove ocular artifact (OA) from electroencephalogram (EEG) using lifting wavelet transform (LWT). The objectives of OA filtering are threefold 1) to reduce the OA magnitude, 2) to retain the neural EEG potentials and 3) to make corrections at the right time localization. Three basic steps involved are to transform the EEG, hard thresholding the wavelet coefficients and...
We proposed a novel approach to select a wavelet for artifact removal in electroencephalogram (EEG) by comparing their relative wavelet energies before and after thresholding. Relative wavelet energy (RWE) gives information about the relative energy associated with different frequency bands and can be considered as a time-scale density. RWE can be used as a tool to detect and characterize a specific...
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