Epileptic EEG classification based on extreme learning machine and nonlinear features.

By Qi Yuan, Weidong Zhou, Shufang Li, Dongmei Cai. Epilepsy Research, 2011

302 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/40685851a9a1044883f935ee674c00da4206da01

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Shiji Song
2. Gao Huang
3. G. Huang
4. Keyou You
5. Yudong Zhang
6. Siyuan Lu
7. Shui-hua Wang
8. Jian Wang

In Cognitive Neuroscience

1. Yudong Zhang
2. G. Huang

In Electrical and Electronic Engineering

1. Shiji Song
2. Gao Huang
3. Keyou You
4. Yudong Zhang
5. Siyuan Lu
6. Jian Wang
7. Shui-hua Wang
8. G. Huang

Citations in the corpus

• Trends in extreme learning machines: A review (2015, background): “Wang, Cao, and Yuan (2011) proved that for certain activation functions, such as the RBF function, there always exist input weights such that the mappingmatrix H isoffullcolumnrankoroffullrowrank.”
• A review on extreme learning machine (2021, background): “In 2011, Yuan and Zhou [212] proposed an approach for epileptic EEG classification.”