Fig. 2
From: Advancing BCI with a transformer-based model for motor imagery classification
Architecture of the Downsampling projector. The figure provides a detailed schematic of the Downsampling projector’s architecture. It includes three convolutional layers, with the second and third layers each followed by a batch normalization (BN) layer and an ELU activation layer. Additionally, two average pooling layers and two dropout layers are incorporated to foster model generalization. Specific parameters, such as the kernel size and stride for the convolutional layers, and the kernel size for the average pooling layers, are also depicted. For example, ”Conv 1x16, (64,1)” signifies a convolutional layer transitioning from an input channel depth of 1 to an output channel depth of 16, with a stride of 64 along the width and 1 along the height of the input feature map.
