Abstract
Presence of artifacts in electroencephalographs (EEG) is major hurdles for the precise analysis of spectral behavior. For suppression of ocular artifact in EEG this paper proposes a component based Independent Component Analysis (ICA) model. It involves the generating a set of individual components of given signal followed by rejection of unwanted artifacts. Further this paper presents a novel method with combination of ICA, information sharing and double density wavelet transform to reject the artifacts from the signal. The Independent Component Analysis (ICA) here is used to segment artifact peaks in the signal. Then the Discrete Wavelet Transform is applied for multi-level transfer of signal data until the reception of significant result. The Wavelet ICA suppression not only removes artifacts but also preserves the spectral and coherence properties of brain signals.
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