Safe Semi-Supervised Extreme Learning Machine for EEG Signal Classification

Safe Semi-Supervised Extreme Learning Machine for EEG Signal Classification

One major challenge in the current brain-computer interface research is the accurate classification of time-varying electroencephalographic (EEG) signals. The labeled EEG samples are usually scarce, while the unlabeled samples are available in large quantities and easy to collect in real application...

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Bibliographic Details
Main Authors: Qingshan She, Bo Hu, Haitao Gan, Yingle Fan, Thinh Nguyen, Thomas Potter, Yingchun Zhang
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Brain–computer interface
electroencephalogram
semi-supervised learning
extreme learning machine
risk degree
Online Access:https://ieeexplore.ieee.org/document/8458126/

Internet

https://ieeexplore.ieee.org/document/8458126/

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