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Epilepsy treatment in clinical practices with surface electroencephalogram (EEG) often faces training dataset shortage issue, which is aggravated by seizure pattern variation among patients. To facilitate future optimization of the detection accuracy as new datasets are available, a fully programmable patient-specific closed-loop epilepsy tracking and suppression system-on-chip (SoC) is proposed with the first-in-literature oneshot learning and online tuning to the best of our knowledge. The proposed two-cycle analog front end (2C-AFE) obtains a 9.8-b effective number of bits (ENOB) with 8× capacitive digital-toanalog converter (CAPDAC) area reduction and 4× switching energy saving compared to a conventional 10-b SAR with an identical unit capacitor size. The entire SoC with 16 surface EEG recording channels consumes an ultra-low energy of 0.97 µJ/class and occupies a miniaturized area of 0.13 mm 2 /ch. in 40-nm CMOS, achieving real-time concurrent seizure detection and raw EEG recording. Verified with the CHB-MIT database, the guided time-channel averaging (GTCA) neural processor achieves the vector-based sensitivity, the specificity, and the latency of 97.8%, 99.5%, and <1 s, respectively. The initial one-shot learning and follow-up online tuning function is validated with the EEG recording from a local hospital patient, which demonstrates a 1.8× vector-based sensitivity boost.

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