A recursive-memory brain-state classifier with 32-channel track-and-zoom Δ² Σ ADCs and Charge-Balanced Programmable Waveform Neurostimulators

Gerard O'Leary,M. Reza Pazhouhandeh,Michael Chang,David Groppe,Taufik A. Valiante,Naveen Verma,Roman Genov

2018 IEEE International Solid-State Circuits Conference - (ISSCC)（2018）

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摘要

The advancement of closed-loop neuromodulation for treating neurological disorders demands: (1) analog circuits monitoring the brain activity uninterruptedly even during neurostimulation, (2) energy-efficient high-efficacy processors for responsive, adaptive, personalized neurostimulation, and (3) safe neurostimulation paradigms with rich spatio-temporal stimuli for controlling the brain's complex dynamics. This paper presents an implantable neural interface processor (NURIP) that addresses these requirements — it performs brain state classification for reliable seizure prediction and contingent seizure abortion.

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关键词

analog circuits,neurological disorders,safe neurostimulation paradigms,32-channel track-and-zoom Δ2Σ ADCs,contingent seizure abortion,closed-loop neuromodulation,programmable waveform neurostimulators,recursive-memory brain-state classifier,brain state classification,implantable neural interface processor,rich spatio-temporal stimuli,personalized neurostimulation,adaptive neurostimulation,responsive neurostimulation,high-efficacy processors,brain activity

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A recursive-memory brain-state classifier with 32-channel track-and-zoom Δ2 Σ ADCs and Charge-Balanced Programmable Waveform Neurostimulators

A recursive-memory brain-state classifier with 32-channel track-and-zoom Δ² Σ ADCs and Charge-Balanced Programmable Waveform Neurostimulators