Residual Learning Attention CNN for Motion Intention Recognition Based on EEG Data

Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network

“…Of the seven baseline models, five models employ attention mechanisms. It is worth noting that [46] and [44] employ a global attention mechanism that encompasses all three dimensions. However, their approach is characterized by extracting attention scores individually for each dimension after using the pooling methods to minimize unrelated dimensions.…”

“…This method intertwines a two-layer CNN and an attention model, feeding their concatenated outputs into a classifier. A notable trend is the use of global attention in conjunction with three sequential models, as seen in [44]- [46]. These works employ three sequential attention models, each dedicated to a single dimension.…”

“…The models are trained for 20 epochs. We employ a well-trained CNN model as a pre-trained network for its corresponding SNN model to accelerate the x Huang et al [32] x Liu et al [12] x+1,644,200 Luo et al [41] x+2,469,600 Fan et al [46], Wang et al [44] x+45,200 Zhang et al [43] x+180,000…”