Evidence map›Paper›PMID 42688366›Full record

ReviewFrontiers in neuroscience2026

From deep brain stimulation to brain-computer interfaces: current progress in implantable neurotechnology.

Volodymyr Mavrych, Olena Bolgova, Leen Alhamd, Rasha Alissa

Abstract readReview
In one paragraph

Review in Frontiers in neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Volodymyr MavrychCollege of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Olena BolgovaCollege of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Leen AlhamdCollege of Medicine, Sulaiman Alrajhi University, Al Bukayriyah, Alqassim, Saudi Arabia.
Rasha AlissaCollege of Medicine, Alfaisal University, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

brain–computer interfacesdeep brain stimulationneuromodulationneuromorphic computingspeech neuroprosthesis

Identifiers

PMID42688366
PMCPMC13534989

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.