Automatic identification of artifacts and unwanted physiologic signals in EEG and EOG during wakefulness
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- 作者：V. Gerlaa ; Vaclav.Gerla@cvut.cz" class="auth_mail" title="E-mail the corresponding author ; V. Kremena ; b ; Kremen.Vaclav@mayo.edu" class="auth_mail" title="E-mail the corresponding author ; N. Covassinb ; Covassin.Naima@mayo.edu" class="auth_mail" title="E-mail the corresponding author ; L. Lhotskaa ; Lenka.Lhotska@cvut.cz" class="auth_mail" title="E-mail the corresponding author ; E.A. Saifutdinovac ; saifueli@fel.cvut.cz" class="auth_mail" title="E-mail the corresponding author ; J. Bukartykb ; Bukartyk.Jan@mayo.edu" class="auth_mail" title="E-mail the corresponding author ; V. Marika ; Vladimir.Marik@ciirc.cvut.cz" class="auth_mail" title="E-mail the corresponding author ; V.K. Somersb ; Somers.Virend@mayo.edu" class="auth_mail" title="E-mail the corresponding author
- 关键词：EEG ; Wakefulness ; Artifacts ; Automatic ; Detection ; Identification
- 刊名：Biomedical Signal Processing and Control
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：31
- 期：Complete
- 页码：381-390
- 全文大小：2433 K
文摘
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Computationally inexpensive methods for robust detection of undesired signals in the EEG.
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Detection of eye blinks, eye movements, muscle activity and flat line artifacts.
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Automated, fast and reliable preprocessing of long-term EEG recordings.
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Verified on real awake EEG data.