Better approach for denoising EEG signals
Gautam Kaushal, Amanpreet Singh, Vibha Jain · 2016
Electro-encephalogram (EEG) is the electrical signal recorded from the scalp, and it represents the neural activity of human brain. EEG is often contaminated by the ocular artifacts viz. Among many methods based on adaptive filters, wavelet transform, independent component analysis (ICA) have shown promising results for the removal of such artifacts. In ICA method the efficiency of suppression of occular artifacts depend on manual identification of independent artifactual components. In this paper an unsupervised robust and computationally fast algorithm is used to automatically identify independent artifactual components and then denoising these components using wavelet decomposition. This proposed algorithm offers potential for automation and do not require any additional Electro-oculographic signals as a reference signal.