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...
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: | |
Online Access: | https://ieeexplore.ieee.org/document/8458126/ |
Similar Items
-
Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning
by: Qingshan She, et al.
Published: (2019-11-01) -
Adaptive Safe Semi-Supervised Extreme Machine Learning
by: Jun Ma, et al.
Published: (2019-01-01) -
Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine
by: Zhewei Liu, et al.
Published: (2021-04-01) -
Dual Learning-Based Safe Semi-Supervised Learning
by: Haitao Gan, et al.
Published: (2018-01-01) -
Possibilistic Clustering-Promoting Semi-Supervised Learning for EEG-Based Emotion Recognition
by: Yufang Dan, et al.
Published: (2021-06-01)