Evidence map›Paper›PMID 37033911›Full record

ReviewFrontiers in human neuroscience2023

On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders.

Abdelkader Nasreddine Belkacem, Nuraini Jamil, Sumayya Khalid, Fady Alnajjar

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
10.6field-weighted citation impact, top 1% of its field
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

24 citing papers in PubMed, 60 citations in OpenAlex.

  1. Article
  2. The classification of walking and phases of gait using EEG: a scoping review.Journal of neuroengineering and rehabilitation · 2026
    Article
  3. Review
  4. Review
  5. Article
  6. [A review of noninvasive brain-computer interfaces combined with transcranial electrical stimulation for neural rehabilitation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026
    Review
  7. Review
  8. Article
  9. Review
  10. Article
  11. Review
  12. Review
  13. Review
  14. Article
  15. Article
  16. A Horizon Scan of Neurotechnology Innovations.International journal of environmental research and public health · 2025
    Review
  17. tDCS and neurofeedback in ADHD treatment.Frontiers in systems neuroscience · 2025
    Review
  18. Article
  19. Review
  20. Optogenetic Brain-Computer Interfaces.Bioengineering (Basel, Switzerland) · 2024
    Review
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 at 2 institutions in 2 countries.

Abdelkader Nasreddine BelkacemDepartment of Computer and Network Engineering, College of Information Technology, UAE University, Al-Ain, United Arab Emirates.
Nuraini JamilDepartment of Computer Science and Software Engineering, College of Information Technology, UAE University, Al-Ain, United Arab Emirates.
Sumayya KhalidDepartment of Computer Science and Software Engineering, College of Information Technology, UAE University, Al-Ain, United Arab Emirates.
Fady AlnajjarDepartment of Computer Science and Software Engineering, College of Information Technology, UAE University, Al-Ain, United Arab Emirates.
United Arab Emirates University · AERIKEN Center for Brain Science · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Emerging brain technologies have significantly transformed human life in recent decades. For instance, the closed-loop brain-computer interface (BCI) is an advanced software-hardware system that interprets electrical signals from neurons, allowing communication with and control of the environment. The system then transmits these signals as controlled commands and provides feedback to the brain to execute specific tasks. This paper analyzes and presents the latest research on closed-loop BCI that utilizes electric/magnetic stimulation, optogenetic, and sonogenetic techniques. These techniques have demonstrated great potential in improving the quality of life for patients suffering from neurodegenerative or psychiatric diseases. We provide a comprehensive and systematic review of research on the modalities of closed-loop BCI in recent decades. To achieve this, the authors used a set of defined criteria to shortlist studies from well-known research databases into categories of brain stimulation techniques. These categories include deep brain stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, transcranial alternating-current stimulation, and optogenetics. These techniques have been useful in treating a wide range of disorders, such as Alzheimer's and Parkinson's disease, dementia, and depression. In total, 76 studies were shortlisted and analyzed to illustrate how closed-loop BCI can considerably improve, enhance, and restore specific brain functions. The analysis revealed that literature in the area has not adequately covered closed-loop BCI in the context of cognitive neural prosthetics and implanted neural devices. However, the authors demonstrate that the applications of closed-loop BCI are highly beneficial, and the technology is continually evolving to improve the lives of individuals with various ailments, including those with sensory-motor issues or cognitive deficiencies. By utilizing emerging techniques of stimulation, closed-loop BCI can safely improve patients' cognitive and affective skills, resulting in better healthcare outcomes.

Indexed as

brain computer interface (BCI)brain stimulationclosed-loop BCIneurodegenerative diseasespsychiatric diseases

Identifiers

PMID37033911
PMCPMC10076878
OpenAlexW4360616832

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.