Fig. 2
From: Adaptive deep feature representation learning for cross-subject EEG decoding
The diagram of the proposed ADFR framework for cross-subject EEG decoding. Source and target EEG data are first pass through convolution layers to learn deep feature representations. The MMD regularization and IDFL regularization are introduced to learn transferable and discriminative EEG features. The EM regularization is then used to adjust the classifier to pass through the low-density region between clusters