A direct, device-level comparison of contemporary implantable closed-loop systems for epilepsy, examining sensing architecture, artifact management, bandwidth, onboard data handling, stimulation-source design, and degree of autonomous control and summarizing their relative capabilities across a common set of scored dimensions.
Abstract
Drug-resistant epilepsy affects roughly one-third of people with epilepsy, and options remain limited for patients whose seizures arise from eloquent cortex, involve multiple foci, or persist after prior surgery. For these patients, closed-loop neurostimulation offers a nondestructive and adjustable alternative that detects pathological activity and delivers stimulation only when and where it is needed. The NeuroPace responsive neurostimulation (RNS) System, approved in 2013, remains the most established clinical example: it senses at the therapeutic target, drives stimulation from biomarkers recorded there, and stores raw neural signals for iterative programming. These same principles increasingly define a newer generation of adaptive deep brain stimulation (DBS) platforms developed largely for movement disorders, motivating a direct, device-level comparison of contemporary implantable closed-loop systems for epilepsy. We compare four clinically approved systems in depth (the NeuroPace RNS, Medtronic Percept PC, Newronika AlphaDBS, and PINS G106RS) together with three investigational platforms (the Picostim-DyNeuMo, CorTec Brain Interchange, and Cadence Neuroscience system), examining sensing architecture, artifact management, bandwidth, onboard data handling, stimulation-source design, and degree of autonomous control, and summarizing their relative capabilities across a common set of scored dimensions. Across platforms, the most consequential differences arise less from stimulation parameter ranges than from sensing design: which contacts can record, whether sensing can continue during stimulation, what frequency range is accessible, how stimulation artifact is suppressed, and whether raw waveforms or only derived features are retained. Because much of the sensing and adaptive-control evidence for the DBS platforms derives from movement-disorder rather than epilepsy applications, we interpret their epilepsy relevance conservatively and highlight where epilepsy-specific validation is still needed.
It is argued that the same handful of bottlenecks recur across all three technologies (long-term stability, neural coding, and equitable access) and the governance frameworks needed alongside continued engineering progress are outlined.
Volodymyr Mavrych, O. Bolgova, Leen Alhamd et al.· Frontiers in Neuroscience· 0 citations
The integration of neural biomarkers, adaptive control algorithms, and connectomic DBS approaches is expected to establish the foundation for next-generation intelligent neuromodulation systems and support the implementation of precision psychiatry in the management of TR-OCD.
Hedya Nadhrati Surura, Rina Hastuti Lubis, Nashrul Fazli Mohd Nasir et al.· JET (Journal of Electrical T...· 0 citations
Major depressive disorder remains a leading cause of global disability, with a substantial fraction of patients exhibiting inadequate response to conventional pharmacological and psychotherapeutic treatments. Vagus nerve stimulation has emerged as an effective neuromodulation strategy for treatment-resistant depression...
Vivian Wei, Xiaofeng Chen, Jiayi Li et al.· Med-X· 0 citations
This review provides a comprehensive synthesis of TI's mechanistic foundations, safety profiles, and therapeutic trajectory, while critically discussing the integration of closed-loop systems, multi-target paradigms, and patient-specific optimization as the next frontiers in non-invasive deep brain stimulation.
A multicenter crossover study is proposed for adults with levodopa-responsive Parkinson’s disease and motor fluctuations despite optimized conventional stimulation, which combines blinded motor-state improvement with reduced troublesome dyskinesia and off time.
The study stresses the necessity of embedding relational autonomy and neural rights into BCI development, tying technological trajectories to governance demands in order to shape responsible paths for future neurotechnologies.
Yu-Zhang Wu· Theoretical and Natural Scie...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.