RESUMO
Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information for accurate disease-state detection, existing systems have low channel counts and poor scalability, which could limit their therapeutic efficacy. This work presents a highly scalable and versatile closed-loop neural interface SoC that can overcome these limitations. A 256-channel time-division multiplexed (TDM) front-end with a two-step fast-settling mixed-signal DC servo loop (DSL) is proposed to record high-spatial-resolution neural activity and perform channel-selective brain-state inference. A tree-structured neural network (NeuralTree) classification processor extracts a rich set of neural biomarkers in a patient- and disease-specific manner. Trained with an energy-aware learning algorithm, the NeuralTree classifier detects the symptoms of underlying disorders (e.g., epilepsy and movement disorders) at an optimal energy-accuracy trade-off. A 16-channel high-voltage (HV) compliant neurostimulator closes the therapeutic loop by delivering charge-balanced biphasic current pulses to the brain. The proposed SoC was fabricated in 65nm CMOS and achieved a 0.227µJ/class energy efficiency in a compact area of 0.014mm2/channel. The SoC was extensively verified on human electroencephalography (EEG) and intracranial EEG (iEEG) epilepsy datasets, obtaining 95.6%/94% sensitivity and 96.8%/96.9% specificity, respectively. In-vivo neural recordings using soft µECoG arrays and multi-domain biomarker extraction were further performed on a rat model of epilepsy. In addition, for the first time in literature, on-chip classification of rest-state tremor in Parkinson's disease (PD) from human local field potentials (LFPs) was demonstrated.
RESUMO
Objective.Electrical stimulation of biological tissue is an established technique in research and clinical practice that uses implanted electrodes to deliver electrical pulses for a variety of therapies. Significant research currently explores new electrode system technologies and stimulation protocols in preclinical models, aiming at both improving the electrode performance and confirming therapeutic efficacy. Assessing the scalability of newly proposed electrode technology and their use for tissue stimulation remains, however, an open question.Approach.We propose a simplified electrical model that formalizes the dimensional scaling of stimulation electrode systems. We use established equations describing the electrode impedance, and apply them to the case of stimulation electrodes driven by a voltage-capped pulse generator.Main results.We find a hard, intrinsic upward scalability limit to the electrode radius that largely depends on the conductor technology. We finally provide a simple analytical formula predicting the maximum size of a stimulation electrode as a function of the stimulation parameters and conductor resistance.Significance.Our results highlight the importance of careful geometrical and electrical designs of electrode systems based on novel thin-film technologies and that become particularly relevant for their translational implementation with electrode geometries approaching clinical human size electrodes and interfacing with voltage-capped neurostimulation systems.
