NDLI: An automated detection and correction method of EOG artifacts in EEG-based BCI

Content Provider	IEEE Xplore Digital Library
Author	Wu Jin Zhang Jiacai Yao Li
Copyright Year	2009
Description	Author affiliation: College of Information Science and Technology, and the National Key Lab. of Cognitive Neuroscience and Learning, Beijing Normal University, China, 100875 (Yao Li) \|\| College of Information Science and Technology, Beijing Normal University, China (Zhang Jiacai) \|\| National Key Lab. of Cognitive Neuroscience and Learning, Beijing Normal University, China, 100875 (Wu Jin)
Abstract	In BCI (Brain-Computer Interface) research community, most BCI research is focused on bioelectrical brain signals recorded by EEG (electroencephalography) as it's noninvasive and thus readily available. While the EEG signal processing methods in EEG based BCI are appealing, they face substantial practical problems. Due to the limitation of EEG signal recording technology, physiological artifacts, especially those generated by eye (EOG, electrooculography), interfere with EEG, may change the characteristics of the neurological phenomena in EEG, and make those signal processing performs incompetently. Linear combination and regression is the most common used technique for removing ocular artifacts from EEG signals where a fraction of the EOG signal is subtracted from the EEG. One problem is that subtracting the EOG signal may also remove part of the EEG signal, for the EOG signal to be subtracted is also contaminated with the EEG signal. In this paper, a new EOG correction model is introduced for EOG artifacts, where the EEG contained in the EOG is considered, and thus avoid removing part of the EEG signal by subtracting the EOG signal. In order to apply this new model in online BCI signal processing, this paper adopts the AR (autoregressive) filtering model of the EEG activity to detect the EOG artifacts, only if it exists, the EEG correction method are performed. We test our methods in the BCI competition 2008 dataset IIa, our informal results indicate that EOG artifacts are well detected, and EOG is well removed from motor imagination related EEG signals.
Starting Page	1
Ending Page	5
File Size	995506
Page Count	5
File Format	PDF
ISBN	9781424433155
DOI	10.1109/ICCME.2009.4906624
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrooculography Character generation Signal processing Brain modeling Bioelectric phenomena Signal generators Electroencephalography Face detection Biomedical signal processing Brain computer interfaces
Content Type	Text
Resource Type	Article

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