Accelerating 3d convolutional neural network with channel bottleneck module for eeg-based emotion recognition

HIGHLIGHTS

• who: Sungkyu Kim and colleagues from the Department of Software Convergence, Kyung University, Yongin, Korea have published the paper: Accelerating 3D Convolutional Neural Network with Channel Bottleneck Module for EEG-Based Emotion Recognition, in the Journal: Sensors 2022, 22, 6813. of /2022/
• what: The authors propose a novel 3D neural network with a channel bottleneck module (CNN-BN) model for EEG-based emotion recognition with the aim of the CNN computation without a significant loss in classification accuracy. In the experiment, the authors generated 3D spatiotemporal representation of EEG signals as the input of the . . .
  

   

  

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