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Motor imagery recognition with automatic EEG channel selection and deep learning.

Han ZhangXing ZhaoZexu WuBiao SunTing Li
Published in: Journal of neural engineering (2020)
The proposed automatic channel selection method has been found to be significantly advantageous compared to the typical approach of using a fixed channel configuration. This work shows that fewer EEG channels not only reduces computational complexity but also improves the MI classification performance. The proposed method selects EEG channels related to hand and feet movement, which paves the way to real-time and more natural interfaces between the patient and the robotic device.
Keyphrases
  • deep learning
  • functional connectivity
  • resting state
  • working memory
  • machine learning
  • artificial intelligence
  • convolutional neural network
  • high density
  • minimally invasive