DocumentCode :
3145699
Title :
Automatic EEG artifact removal based on ICA and Hierarchical Clustering
Author :
Zou, Yuan ; Hart, John, Jr. ; Jafari, Roozbeh
Author_Institution :
Dept. of Electr. Eng., Univ. of Texas at Dallas, Richardson, TX, USA
fYear :
2012
fDate :
25-30 March 2012
Firstpage :
649
Lastpage :
652
Abstract :
Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques, however, they are typically influenced by extraneous interference, like muscle movements, eye blinks, eye movements, background noise, etc. Therefore, a preprocessing step to remove artifacts is extremely important. This paper presents an effective artifact removal algorithm, based on Independent Component Analysis (ICA) and Hierarchical Clustering. Our technique utilizes general temporal and spectral features and particular information about target Event-Related Potentials (ERPs) (e.g. the timing of N200 and P300 on inhibition task or the specific electrodes contributing to the ERPs) to separate ERPs and artifact activities. Our method considers templates for desired ERPs to select event-related components for signal reconstruction. In our experimental study, we show that our proposed method can effectively enhance the ERPs for all fifteen subjects in the study, even for those that barely display ERPs in the raw recordings.
Keywords :
electroencephalography; independent component analysis; medical signal processing; pattern clustering; signal reconstruction; ERP; ICA; N200 timing; P300 timing; artifact removal algorithm; automatic EEG artifact removal; electroencephalography; event related potentials; extraneous interference; hierarchical clustering; independent component analysis; inhibition task; scalp electrical activity recording; signal preprocessing step; signal processing techniques; signal reconstruction; spectral features; temporal features; Decision support systems; EEG; Hierarchical Clustering; ICA;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on
Conference_Location :
Kyoto
ISSN :
1520-6149
Print_ISBN :
978-1-4673-0045-2
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2012.6287967
Filename :
6287967
Link To Document :
بازگشت