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Removal of Power Line Interference from EEG using Wavelet-Ica

IJCA Proceedings on International Conference on Advancements in Engineering and Technology
© 2015 by IJCA Journal
ICAET 2015 - Number 6
Year of Publication: 2015
Gautalm Kaushal
V. K. Jain
Amanpreet Singh

Gautalm Kaushal andv.k.jain and Amanpreet Singh. Article: Removal of Power Line Interference from EEG using Wavelet-Ica. IJCA Proceedings on International Conference on Advancements in Engineering and Technology ICAET 2015(6):29-31, August 2015. Full text available. BibTeX

	author = {Gautalm Kaushal andv.k.jain and Amanpreet Singh},
	title = {Article: Removal of Power Line Interference from EEG using Wavelet-Ica},
	journal = {IJCA Proceedings on International Conference on Advancements in Engineering and Technology},
	year = {2015},
	volume = {ICAET 2015},
	number = {6},
	pages = {29-31},
	month = {August},
	note = {Full text available}


Electroencephalogram (EEG) signals are of having very small amplitudes and so these can be easily contaminated by different Artifacts. Due to the presence of various artifacts in EEG, its analysis becomes difficult for the clinical evaluation. Major types of artifacts that affect the EEG are Power Line noise, eye movements, Electromyogram (EMG), and Electrocardiogram (ECG). Out of these artifacts Power Line noise and eye movements related are most prominent. To deal with these artifacts, there are various methods evolved by different researchers. In this paper, to remove power line noise of 50 Hz frequency, a new Wavelet analysis and Independent Component Analysis (ICA) based technique is presented, which is applied to a single channel EEG Signal. The signal is first decomposed into spectrally non-overlapping components using Stationary Wavelet Transform (SWT). The SWT decomposes single channel EEG signal into components based upon different frequency levels. The ICA algorithm is then applied to derive the independent components. The wavelet-ICA components associated with artifact related event is selected and cancelled out. The artifact free wavelet components are reconstructed to form artifact free EEG. The performance analysis of the algorithm is done using Signal to Noise Ratio (SNR).


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