Title :
A Fully Automatic Method for Ocular Artifact Suppression from EEG Data Using Wavelet Transform and Independent Component Analysis
Author :
Ghandeharion, Hosna ; Erfanian, Abbas
Author_Institution :
Dept. of Biomed. Eng., Iran Univ. of Sci. & Technol., Tehran
fDate :
Aug. 30 2006-Sept. 3 2006
Abstract :
Contamination of electroencephalographic (EEG) recordings with different kinds of artifacts is the main obstacle to the analysis of EEG data. Independent component analysis (ICA) is a general accepted tool for isolating artifactual components. One major challenge to artifact removal using ICA is the automatic identification of the artifactual components. However there is still little consensus on criteria for automatic rejection of undesired components. In this paper we present a new identification procedure based on an efficient combination of statistical and wavelet-based measures for ocular artifact suppression. The results on 420 4-s EEG epochs indicate that the artifact components can be identified correctly with 96.4%
Keywords :
electroencephalography; independent component analysis; interference suppression; medical signal processing; statistical analysis; wavelet transforms; 4 s; EEG data; ICA; artifact removal; automatic artifactual component identification; automatic rejection; electroencephalographic recording contamination; fully automatic method; independent component analysis; ocular artifact suppression; statistical analysis; wavelet transform; Cities and towns; Electroencephalography; Electrooculography; Independent component analysis; Pollution measurement; Principal component analysis; Sensor arrays; USA Councils; Wavelet analysis; Wavelet transforms;
Conference_Titel :
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Conference_Location :
New York, NY
Print_ISBN :
1-4244-0032-5
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2006.259609
