Aug 2026· Frontiers in Neuroscience· 0 citations· 53 references
TL;DR
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.
Abstract
Brain implants, including deep brain stimulation (DBS) systems, brain–computer interfaces (BCIs), and speech neuroprostheses, are moving from the proof-of-concept stage to early clinical deployment. Although these systems target different clinical problems, we argue that they share a common closed-loop architecture (sensing, decoding, stimulation or output, power and telemetry, and chronic clinical validation) and that their translational pace is set by bottlenecks at these shared stages rather than by challenges unique to each technology. Recent milestones, including the regulatory clearance of a cortical interface and an expanding set of implanted-BCI trials, have accelerated progress in the field. This mini-review uses this shared architecture to compare progress across three fronts: the shift from open-to-closed-loop DBS and its widening range of neurological and psychiatric indications; BCIs restoring motor and sensory function; and speech neuroprostheses that decode attempted speech into text, voice, and facial animation. We highlight enabling advances in flexible electrodes, AI-assisted decoding, and neuromorphic edge processing, and examine unresolved controversies over electrode architecture and system design. We conclude that the same handful of bottlenecks recur across all three technologies (long-term stability, neural coding, and equitable access) and outline the governance frameworks needed alongside continued engineering progress.
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
Advances in neural recording, real-time decoding and bioelectronic stimulation have enabled a new class of systems that re-establish functional communication across disrupted neural pathways. In the context of paralysis, these neural bypass interfaces link upstream neural intent to effector activation downstream of the lesion, effectively circumventing sites of injury within the nervous system to restore volitional movement. In this review, we define neural bypass interfaces as an emerging category of bioelectronic medicine, distinct from conventional brain-computer interfaces and neuromodulation technologies. We first outline key neurophysiological and systems-level considerations underlying bypass design, before tracing their evolution from bench to bedside. We then focus on the clinical translation challenges that govern real-world deployment, specifically signal stability and fidelity, stimulation performance, decoding robustness, closed-loop integration and long-term implant viability. Importantly, this review highlights two emerging directions that may shape the next generation of neural bypasses: the use of the spinal cord itself as a source of neural intent, and the development of bidirectional bypasses integrating sensory feedback to enable more adaptive, physiologically aligned control. Ultimately, neural bypasses may go beyond simply restoring movement to drive biological recovery, redefining neurorestorative therapies for paralysis.
M. R. Lee, A. K. S. Tan, Yu Tung Lo et al.· Journal of NeuroEngineering...· 0 citations
Deep brain stimulation (DBS) is transforming from a static therapy toward adaptive systems that adjust stimulation based on neural biomarkers. However, the detection of reliable biomarkers that capture the multi-dimensional nature of complex symptoms is often challenging. Here we demonstrate volitional DBS (vDBS)--a paradigm in which patients use brain-computer interface (BCI) training to learn self-regulation of a neural signal that then controls closed-loop DBS. Two patients with Parkinson's disease implanted with sensing-enabled neurostimulators completed chronic, at-home BCI training by playing an airplane simulation game. Through training, they were able to effectively down-regulate their cortical beta signal (p's < 1e-10), represented as the real-time position of a plane in the BCI game. Following training, this cortical beta signal served as the input to a closed-loop DBS algorithm. By modulating their beta signal to cross personalized thresholds, patients voluntarily increased or decreased neurostimulation amplitude at will, in the absence of physical movement (p's < 1e-10). This proof-of-principle demonstration establishes that volitional control of intracranial neurostimulation is achievable without the need of an externalized manual controller. BCI-vDBS could potentially be used for a range of neuropsychiatric conditions and brain rehabilitation to support personalized control of neurostimulation.
J.-X. Zhang, J. Suh, P. Daniel et al.· medRxiv· 0 citations
Neurological and psychiatric disorders frequently arise from dysfunctional deep-brain circuits, yet targeting these subcortical structures with conventional non-invasive neuromodulation remains a significant challenge due to the lack of focal precision at depth. Temporal Interference (TI) stimulation has emerged as a transformative paradigm, leveraging the intersection of multiple high-frequency electric fields to generate a low-frequency amplitude-modulated envelope within deep-seated targets. This biophysical strategy enables the modulation of subcortical dynamics while minimizing the activation of overlying cortical tissues. Emerging preclinical evidence demonstrates that TI can robustly orchestrate neurotransmitter release, facilitate synaptic plasticity, and ameliorate deficits in both motor and cognitive domains. Preliminary clinical translations further underscore its potential in enhancing memory precision, accelerating motor skill acquisition, and suppressing epileptic biomarkers. The mechanistic understanding of TI has evolved from passive low-pass filtering to include nonlinear ion-channel rectification and, more recently, network-mediated inhibition-particularly the recruitment of parvalbumin-positive interneurons in superficial layers-as a critical determinant of spatial selectivity. Human intracranial studies have further refined this framework, revealing that TI operates in a subthreshold regime, produces carrier-independent deep modulation, and elicits a sustained carry-over effect absent from unmodulated kilohertz stimulation. The efficacy of TI is fundamentally governed by a complex interplay of controllable parameters-including carrier frequency offset (Δf), current intensity, electrode geometry, and timing-alongside uncontrollable factors such as individual anatomical heterogeneity and endogenous brain states. Furthermore, advanced computational modeling, particularly finite element simulations incorporating personalized head models, has become indispensable for characterizing electric field distributions and achieving individualized, high-precision targeting. 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.
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.
BACKGROUND
Post-stroke hand dysfunction severely limits patients' independence, and conventional rehabilitation often fails those without active movement. Noninvasive EEG-based brain-computer interface (BCI) technology addresses this by creating a closed-loop feedback system rooted in Hebbian learning principles. This system decodes rhythmic signals from the sensorimotor cortex during imagined hand movements in real-time. The decoded intention is then translated into commands to drive exoskeletons, functional electrical stimulation (FES), or virtual reality (VR) devices, thereby moving the affected limb. This process strengthens or remodels damaged neural pathways, promoting motor recovery.
METHODS
This article systematically outlines the neurophysiological basis of EEG-BCI and three major rehabilitation paradigms: motor imagery with physical feedback, motor imagery with virtual/multisensory feedback, and the steady-state visual evoked potential (SSVEP)-driven paradigm.
RESULTS
Studies confirm these approaches can improve upper limb function, showing significant potential. However, widespread clinical use faces challenges like low signal-to-noise ratios, significant individual variability, and "BCI blindness."
CONCLUSIONS
Future work should focus on improving decoding algorithms, developing more user-friendly devices, deepening mechanistic understanding, and establishing standardized clinical assessments. This review aims to offer valuable guidance for subsequent research.
Wang Peng, Yang Yang, Juehan Wang et al.· Topics in Stroke Rehabilitat...· 0 citations