EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification

@article{Zhang2021EEGinceptionAA,
  title={EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification},
  author={Ce Zhang and Young-Keun Kim and Azim Eskandarian},
  journal={Journal of Neural Engineering},
  year={2021},
  volume={18}
}
Objective. Classification of electroencephalography (EEG)-based motor imagery (MI) is a crucial non-invasive application in brain–computer interface (BCI) research. This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based MI classification that outperforms the state-of-the-art methods. Approach. The proposed CNN model, namely EEG-inception, is built on the backbone of the inception-time network, which has showed to be highly efficient and… 

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