Artifacts Removal of EEG Signals By the Application of ICA and Double Density DWT Algorithm | Zendy

Vandana Roy | Zendy; Shailja Shukla | Zendy

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Artifacts Removal of EEG Signals By the Application of ICA and Double Density DWT Algorithm

Author(s) -

Vandana Roy,

Shailja Shukla

Publication year - 2014

Publication title -

international journal of engineering and manufacturing

Language(s) - English

Resource type - Journals

eISSN - 2306-5982

pISSN - 2305-3631

DOI - 10.5815/ijem.2014.02.04

Subject(s) - independent component analysis , artifact (error) , signal (programming language) , computer science , discrete wavelet transform , wavelet , electroencephalography , component (thermodynamics) , algorithm , artificial intelligence , pattern recognition (psychology) , segmentation , wavelet transform , psychology , physics , psychiatry , thermodynamics , programming language

Independent Component Analysis is used for the automation and detection of brain artifacts. The Independent Component Analysis (ICA) here is used for the segmentation of 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. We have extended our search and applied the Double Density Algorithm for the multi-level transfer. The results obtained were analyzed from the data set of EEG signals taken with a outsource reference. Since the method is parameter free implementations in clinical settings are imaginable.

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