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

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.

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Publication Details

Journal
Frontiers in Neuroscience
Published
2025-12-08
DOI
https://doi.org/10.3389/fnins.2025.1658315
Citations
1
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
1.43
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article

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

Jiaqi Zhou, Shengchao Zhang, Yize Chen, Tong Li et al.
1 citations
Frontiers in Neuroscience
EEG and Brain-Computer Interfaces
1.43
article

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

Jiaqi Zhou, Shengchao Zhang, Yize Chen, Tong Li, Yong Liu, Yuling Gao, Xiaoyang Gong
article en
1 citations

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.

Frontiers in NeuroscienceVol. 19
Dalian Medical University (CN), First Affiliated Hospital of Dalian Medical University (CN)
Openalex Percentile: Top 13%
EEG and Brain-Computer Interfaces
1.43
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