NuriP: Neural interface processor for brain-state classification and programmable-waveform neurostimulation

Gerard O’Leary, David M. Groppe, Taufik A. Valiante, Naveen Verma, Roman Genov

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


The advancement of implantable medical devices for the treatment of neurological disorders demands energy-efficient, low-latency processors for responsive, safe, and personalized neuromodulation. A 130-nm CMOS neural interface processor is presented to perform the brain-state classification and closed-loop control using programmable-waveform electrical stimulation. The architecture features an autoencoder neural network for both spatial filtering and dimensionality reduction. Dedicated feature extraction blocks are implemented for univariate (signal-band energy) and multivariate (phase locking value, and cross-frequency coupling) neural signal processing. The proceeding exponentially decaying memory support vector machine (EDM-SVM) accelerator employs these features for hardware-efficient brain-state classification with a high temporal resolution. An integrated digitally charge-balanced waveform generator enables flexibility in finding optimal neuromodulation paradigms for pathological symptom suppression. The system on chip (SoC) is validated using the EU human intracranial electroencephalography epilepsy data set, achieving a seizure sensitivity of 97.7% and a false detection rate of 0.185/h while consuming 169 µJ per classification.

Original languageEnglish (US)
Pages (from-to)3150-3162
Number of pages13
JournalIEEE Journal of Solid-State Circuits
Issue number11
StatePublished - 2018

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


  • Binary exponential charge recovery (BECR)
  • Cross-frequency coupling (CFC)
  • Exponentially decaying memory (EDM)
  • Neural interface processor (NURIP)
  • Neuromodulation
  • Phase locking value (PLV)
  • Signal energy
  • Support vector machine (SVM)
  • Waveform generation


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