Deep learning model with adaptive regularization for eeg-based emotion recognition using temporal and frequency features

HIGHLIGHTS

• who: ALIREZA SAMAVAT et al. from the of Biomedical Engineering, Islamic Azad University of Tehran-Central Branch, Tehran, Iran have published the research: Deep Learning Model With Adaptive Regularization for EEG-Based Emotion Recognition Using Temporal and Frequency Features, in the Journal: (JOURNAL)
• what: The authors propose a hybrid multi-input deep model with convolution neural networks (CNNs) bidirectional Long Short-term Memory (Bi-LSTM). The authors propose a novel hybrid multi-input deep learning approach for emotion recognition from raw EEG signals. In Figure 4, the authors show the effect of the adaptive regularization . . .
  

   

  

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