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
Deep Brain Stimulation (DBS) is an implantable neuromodulation system that integrates electronic components, intracranial electrodes, and biological neural networks into a bioelectronic control system designed to therapeutically modulate brain activity. The development of DBS technology for treatment-resistant obsessive-compulsive disorder (TR-OCD) has evolved from a purely anatomical target-based stimulation approach toward the integration of system architecture, control strategies, and biomarker-driven adaptive neurostimulation. This narrative review examines the architecture of DBS systems, open-loop and closed-loop control strategies, brain-sensing technologies, neural biomarkers, and recent advances in adaptive neurostimulation for TR-OCD. The review was conducted through an appraisal of contemporary literature addressing the intersection of neuroscience, electrical engineering, and biomedical engineering in the implementation of DBS. The findings indicate that a DBS system comprises an implantable pulse generator (IPG), stimulation electrodes, signal transmission components, and target neural networks that collectively form a neuromodulation control system. Conventional DBS remains predominantly based on an open-loop paradigm, in which continuous stimulation is delivered according to predefined parameters without real-time neurophysiological feedback. In contrast, advances in brain-sensing technologies have enabled the recording of neural biomarkers, particularly local field potentials (LFPs), which serve as the foundation for the development of closed-loop DBS systems. These systems allow automatic adjustment of stimulation parameters through feedback-driven mechanisms, thereby offering the potential to enhance therapeutic efficacy, improve device energy efficiency, and facilitate personalized treatment. 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, yet implantable systems are limited by surgical risk, cost, and restricted scalability. Recent advances in non-invasive, wearable vagus nerve stimulation technologies, particularly transcutaneous auricular and cervical approaches, offer a promising pathway toward accessible, home-based neuromodulation. This Review synthesizes the anatomical and physiological rationale underlying wearable non-invasive VNS and critically evaluates mechanistic evidence spanning monoaminergic modulation, neuroplasticity, neuroimmune regulation, autonomic control, and large-scale brain network reorganization. We further summarize clinical findings from implantable and non-invasive trials and discuss key engineering considerations, including electrode–skin interfaces, stimulation parameters, wearability, and safety. Finally, we highlight major challenges and future opportunities, emphasizing the integration of flexible bioelectronics, multimodal sensing, and Artificial Intelligence-driven closed-loop control to enable personalized, scalable neuromodulation for depression. Reviews vagus nerve stimulation mechanisms in depression, including autonomic, inflammatory, and neural pathways. Compares major vagus nerve stimulation modalities, with emphasis on transcutaneous auricular and cervical approaches. Analyzes device design, including electrode configuration, stimulation parameters, and ergonomic considerations. Summarizes clinical evidence on the efficacy, safety, and limitations of noninvasive vagunerve stimulation. Identifies challenges in standardization and personalization and proposes AI-assisted, closed-loop approaches. Reviews vagus nerve stimulation mechanisms in depression, including autonomic, inflammatory, and neural pathways. Compares major vagus nerve stimulation modalities, with emphasis on transcutaneous auricular and cervical approaches. Analyzes device design, including electrode configuration, stimulation parameters, and ergonomic considerations. Summarizes clinical evidence on the efficacy, safety, and limitations of noninvasive vagunerve stimulation. Identifies challenges in standardization and personalization and proposes AI-assisted, closed-loop approaches.
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.
Yuzhang 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.
