Blind Source Separation and Artifacts Removal in EEG Signals

Blind source separation (BSS) is a well-known method to remove the artifacts from EEG signals. Electroencephalogram (EEG) is an electrophysiological process that records electrical activity of the brain by placing electrodes on the external surface of the scalp. record the electrical pattern of the brain. Deep learning algorithms like adaptive filtering, regression-based methods and independent component analysis (ICA) deal with artifacts removal in EEG. As per research, independent component analysis and the hybrid versions of ICA based algorithms have shown to be effective in removing all kinds of artifacts in EEG recordings.

This whitepaper broadly talks about blind source separation method to remove the artifacts from Electroencephalogram signals. Some of the key topics this paper covers are following:

Theory behind blind source separation
The need of blind source separation
Concept of Independent Component Analysis
Use of ICA in removing ocular artifacts in a recorded EEG signal
Implementation of ICA using fixed point techniques and numerical methods

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Blind Source Separation and Artifacts Removal in EEG Signals

This whitepaper broadly talks about blind source separation method to remove the artifacts from Electroencephalogram signals. Some of the key topics this paper covers are following:

Theory behind blind source separation
The need of blind source separation
Concept of Independent Component Analysis
Use of ICA in removing ocular artifacts in a recorded EEG signal
Implementation of ICA using fixed point techniques and numerical methods

Download the Whitepaper to know more.