ReviewiScience2026
Brain-computer interfaces for neurodegenerative diseases: A comprehensive review of closed-loop systems from diagnosis to neural rehabilitation.
Review in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-computer interface (BCI) technology serves as an important bridge between the nervous system and external devices and has shown considerable promise for the diagnosis and treatment of neurodegenerative diseases, whose global burden continues to increase: in 2021, more than 57 million people were living with dementia worldwide, with Alzheimer's disease (AD) accounting for approximately 60-70% of cases, while an estimated 11.77 million people were living with Parkinson's disease (PD) in 2021, according to the Global Burden of Disease Study 2021. This growing disease burden, together with the progressive loss of cognitive, motor, and communicative functions, highlights the need for more precise approaches to early detection, functional assistance, and individualized intervention. Conventional diagnostic and therapeutic approaches remain poorly suited to early detection and timely intervention. This paper analyzes the development of BCI technology, from the first detection of electroencephalogram (EEG) signals to the parallel development of invasive and non-invasive systems, and outlines the basic principles of signal acquisition, processing, and decoding, together with the relative advantages and limitations of different approaches. It then focuses on the use of BCI in Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), covering early diagnosis, functional reconstruction, and neuromodulation. The article also discusses the difficulties encountered by BCI technology in terms of technological realization, clinical translation, and ethical considerations, as well as several future research directions, including multimodal fusion, adaptive algorithms, closed-loop control systems, and personalized treatment so as to drive the applications of BCI in precision medicine and in neurological diseases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.