Regressive and Blind Source Separation Techniques for Ocular Artifact Removal
|
 |
|
Post a Comment
|
|
|
|
|
CONTRIBUTORS:
|
|
|
JOURNAL:
|
|
|
YEAR:
|
2012
|
|
PUB TYPE:
|
Journal Article
|
|
SUBJECT(S):
|
Electroencephalography, Electrooculography, Eye Artifact Removal, Independent Compo-
nent Analysis, Strength of Eye Blink
|
|
DISCIPLINE:
|
Computer Science
|
|
HTTP:
|
http://www.ijipbangalore.org
|
|
LANGUAGE:
|
English
|
|
PUB ID:
|
103-511-665
(Last edited on
2013/01/16 22:18:16 US/Mountain)
|
|
SPONSOR(S):
|
|
|
ABSTRACT:
Several ocular artifact removal techniques for electroencephalographic data are evaluated in this paper. EEG
recordings are taken from an emotion recognition experiment, which contains several instances of ocular
artifacts like eye blinks and eye movements. The data is preprocessed through a Butterworth band-pass filter
and a 60Hz notch filter to remove most electrical and high frequency noise. Once preprocessed, the data will
be used to evaluate three different types of ocular artifact removal techniques: EOG based linear regression,
Principal Component Analysis, and Independent Component Analysis. A new metric called Strength of
Eye Blink (SEB) is created to automatically determine the removal of different components used in the
Blind Source Separation techniques. Each technique is tested using two different metrics: Kurtosis, and
a new metric called Zero-Mean Normalized Sum Squared Error. The new metric shows that Independent
Component Analysis reduced eye artifacts, the best out of all methods while keeping uncontaminated EEG
signals unchanged (Average SSE of 0.1126).
|
|
|
|
STATISTICS
|
|
Click on # to view
|
|
Citations
|
|
0
|
|
References
|
|
0
|
|
Comments
|
|
0
|
|
Quality
|
|
0/0.00
|
|
Interest
|
|
0/0.00
|
|
View(er)s
|
|
1/109
|
|
|
|
|
|
|
| Prev |
Next |
|