This article presents a few-shot transfer learning method for detecting braking intent from EEG signals using a neuromorphic chip. Researchers trained a group‐level convolutional spiking neural network (CSNN), ported it to the BrainChip Akida AKD1000 neuromorphic processor, and then adapted it to each individual driver with just a few training epochs. They achieved over 90% accuracy while reducing power consumption by more than 97% compared to a conventional Intel Xeon CPU — highlighting the potential of edge neuromorphic hardware for personalized, energy-efficient real-time cognitive applications.