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Real-time brain-computer interfaces (BCIs) demand fully on-chip neural decoding with tight power, area, and latency budgets. This work presents the first fully-integrated transformer-based BCI SoC that performs energy-efficient motion intention decoding from ECoG signals. The chip integrates a 64-channel analog front-end (AFE) and a distilled, light-weight transformer-based decoder featuring customized 7T-SRAM computing-in-memory (CIM) macros with hybrid sign-magnitude and two's-complement (SM-2C) computation. Fabricated in 65nm, the decoder achieves a 67.8% accuracy on a 6-class upper-limb motion-intention dataset, consuming 47μW in 4.7mm2, while the complete SoC occupies 10.1mm2.