Evidence map›Paper›PMID 41438236›Full record

ReviewFrontiers in neuroscience2025

Advancements in the application of brain-computer interfaces based on different paradigms in amyotrophic lateral sclerosis.

Tong Li, Yuling Gao, Jiaqi Zhou, Yize Chen, Shengchao Zhang, Xiaoyang Gong, Yong Liu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Tong Li *Department of Rehabilitation Medicine, The First Affiliated Hospital of Dalian Medical University, College of Health-Preservation and Wellness, Dalian Medical University, Dalian, China.
Yuling Gao *Department of Rehabilitation Medicine, The First Affiliated Hospital of Dalian Medical University, College of Health-Preservation and Wellness, Dalian Medical University, Dalian, China.
Jiaqi ZhouDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Dalian Medical University, College of Health-Preservation and Wellness, Dalian Medical University, Dalian, China.
Yize ChenDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Dalian Medical University, College of Health-Preservation and Wellness, Dalian Medical University, Dalian, China.
Shengchao ZhangDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Dalian Medical University, College of Health-Preservation and Wellness, Dalian Medical University, Dalian, China.
Xiaoyang GongDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Dalian Medical University, College of Health-Preservation and Wellness, Dalian Medical University, Dalian, China.
Yong LiuDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Dalian Medical University, College of Health-Preservation and Wellness, Dalian Medical University, Dalian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amyotrophic lateral sclerosis (ALS) is a progressive neurological condition that leads to the gradual loss of movement and communicative abilities, significantly diminishing the quality of life for affected individuals. Recent advancements in neuroscience and engineering have propelled the swift evolution of brain-computer interfaces (BCIs), which are now extensively utilised in medical rehabilitation, military applications, assistive technologies, and various other domains. As a communication medium facilitating direct interaction between the brain and the external world independent of the peripheral nervous system, BCI provides ALS patients with an innovative method for communication and control, offering unparalleled prospects for improving their quality of life. Recent collaborative endeavours among several specialists have markedly enhanced the precision and velocity of diverse BCI paradigms, signifying a breakthrough in BCI applications for ALS. Nonetheless, obstacles and constraints remain. This study methodically extracted pertinent literature from the Web of Science and PubMed databases in accordance with PRISMA guidelines. Following stringent inclusion and exclusion criteria, 23 studies were identified. This data allows us to summarise the application results and current limitations of several BCI paradigms in motor control and communication, while delineating prospects in multimodal fusion and adaptive calibration. This review presents evidence-based references for the effective translation and application of BCI technology in ALS rehabilitation.

Indexed as

advancementsamyotrophic lateral sclerosisbrain-computer interfacesneurodegenerative diseaseparadigms

Identifiers

PMID41438236
PMCPMC12719500

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.