S-EEGNet: Electroencephalogram Signal Classification Based on a Separable Convolution Neural Network With Bilinear Interpolation

S-EEGNet: Electroencephalogram Signal Classification Based on a Separable Convolution Neural Network With Bilinear Interpolation

As one of the most important research fields in the brain-computer interface (BCI) field, electroencephalogram (EEG) classification has a wide range of application values. However, for the EEG signal, it is difficult for the traditional neural networks to capture the characteristics of the EEG signa...

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Bibliographic Details
Main Authors: Wenkai Huang, Yihao Xue, Lingkai Hu, Hantang Liuli
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Hilbert > Huang transform~(HHT)
electroencephalogram~(EEG) classification
convolutional neural network~(CNN)
bilinear interpolation
Online Access:https://ieeexplore.ieee.org/document/9142199/

Internet

https://ieeexplore.ieee.org/document/9142199/

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