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Overall architecture of the Compact Convolutional Swin Transformer (CCST) model. The design includes four key stages: patch embedding for feature extraction, positional embedding for encoding temporal structure, multiple Swin Transformer Blocks for hierarchical self-attention, and a Classification Head for final motor imagery EEG decoding.
