Abstract
Despite the common use of anesthetics to modulate consciousness in the clinic, brain-based monitoring of consciousness is uncommon. We combined electroencephalographic measurement of brain activity with deep neural networks to automatically discriminate anesthetic states induced by propofol. Our results with leave-one-participant-out-cross-validation show that convolutional neural networks significantly outperform multilayer perceptrons in discrimination accuracy when working with raw time series. Perceptrons achieved comparable accuracy when provided with power spectral densities. These findings highlight the potential of deep convolutional networks for completely automatic extraction of useful spatio-temporo-spectral features from human EEG.
Original language | English |
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Title of host publication | Brain Informatics |
Subtitle of host publication | International Conference, BI 2018, Arlington, TX, USA, December 7–9, 2018, Proceedings |
Editors | Shouyi Wang, Vicky Yamamoto, Jianzhong Su, Yang Yang, Erick Jones, Leon Iasemidis, Tom Mitchell |
Publisher | Springer |
Pages | 216-225 |
Number of pages | 10 |
ISBN (Print) | 9783030055868 |
DOIs | |
Publication status | Published - 2018 |
Event | International Conference on Brain Informatics (BI) - Arlington, United States Duration: 7 Dec 2018 → 9 Dec 2018 http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=73741©ownerid=94309 |
Publication series
Series | Lecture Notes in Artificial Intelligence |
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Volume | 11309 |
ISSN | 0302-9743 |
Conference
Conference | International Conference on Brain Informatics (BI) |
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Country/Territory | United States |
City | Arlington |
Period | 7/12/18 → 9/12/18 |
Internet address |
Keywords
- Consciousness
- Anesthesia
- EEG
- Deep learning