Conference Proceeding

Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings

Univ. of Reggio Calabria, Reggio Calabria
09/2007; DOI:10.1109/IJCNN.2007.4371184 pp.1524 - 1529 In proceeding of: Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Source: IEEE Xplore

ABSTRACT Electroencephalographic (EEG) recordings are often contaminated by the artifacts, signals that have non-cerebral origin and that might mimic cognitive or pathologic activity and therefore distort the analysis of EEG. In this paper the issue of artifact extraction from Electroencephalographic data is addressed and a new technique for EEG artifact removal, based on the joint use of Wavelet transform and Independent Component Analysis (WICA), is presented and compared to two other techniques based on ICA and wavelet denoising. An artificial artifact-laden EEG dataset was created mixing a real EEG with a set of synthesized artifacts. This dataset was processed by WICA and the two other methods. The proposed technique had the best artifact separation performance for every kind of artifact also allowing for the minimum information loss.

0 0
 · 
0 Bookmarks
 · 
65 Views
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

Keywords

artifact
 
artifact extraction
 
artifact separation performance
 
artifacts
 
artificial artifact-laden EEG dataset
 
dataset
 
EEG artifact removal
 
Electroencephalographic data
 
Independent Component Analysis
 
joint use
 
minimum information loss
 
proposed technique
 
synthesized artifacts
 
techniques
 
Wavelet
 
wavelet denoising