Abstract
Abstract
Neurological and psychiatric diseases can lead to motor, language, emotional disorder, and cognitive, hearing or visual impairment By decoding the intention of the brain in real time, the Brain–computer interface (BCI) can first assist in the diagnosis of diseases, and can also compensate for its damaged function by directly interacting with the environment; In addition, provide output signals in various forms, such as actual motion, tactile or visual feedback, to assist in rehabilitation training; Further intervention in brain disorders is achieved by close‐looped neural modulation. In this article, we envision the future BCI digital prescription system for patients with different functional disorders and discuss the key contents in the prescription the brain signals, coding and decoding protocols and interaction paradigms, and assistive technology. Then, we discuss the details that need to be specially included in the digital prescription for different intervention technologies. The third part summarizes previous examples of intervention, focusing on how to select appropriate interaction paradigms for patients with different functional impairments. For the last part, we discussed the indicators and influencing factors in evaluating the therapeutic effect of BCI as intervention.
Topics

No keywords indexed for this article. Browse by subject →

References
42
[1]
Deep learning with convolutional neural networks for EEG decoding and visualization

Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer et al.

Human Brain Mapping 10.1002/hbm.23730
[5]
Ruiz S "Abnormal neural connectivity in schizophrenia and fMRI‐brain‐computer Interface as a potential therapeutic approach" Front Psych (2013)
[8]
Closed-loop neuromodulation in an individual with treatment-resistant depression

Katherine W. Scangos, Ankit N. Khambhati, Patrick M. Daly et al.

Nature Medicine 10.1038/s41591-021-01480-w
[9]
A POMDP Approach to Optimizing P300 Speller BCI Paradigm

Jaeyoung Park, Kee-Eung Kim

IEEE Transactions on Neural Systems and Rehabilita... 10.1109/tnsre.2012.2191979
[17]
High-performance brain-to-text communication via handwriting

Francis R. Willett, Donald T. Avansino, Leigh R. Hochberg et al.

Nature 10.1038/s41586-021-03506-2
[18]
WillettF KunzE FanC et al.A high‐performance speech neuroprosthesis.bioRxiv2023. doi:10.1101/2023.01.21.524489 10.1101/2023.01.21.524489
[19]
Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria

David A. Moses, Sean L. Metzger, Jessie R. Liu et al.

New England Journal of Medicine 10.1056/nejmoa2027540
[21]
Brain‐computer interfaces for post‐stroke motor rehabilitation: a meta‐analysis

María A. Cervera, Surjo R. Soekadar, Junichi Ushiba et al.

Annals of Clinical and Translational Neurology 10.1002/acn3.544
[24]
Assessment of Safety of a Fully Implanted Endovascular Brain-Computer Interface for Severe Paralysis in 4 Patients

PETER MITCHELL, Sarah C. M. Lee, Peter E. Yoo et al.

JAMA Neurology 10.1001/jamaneurol.2022.4847
[30]
Striano P "Willful modulation of brain activity in disorders of consciousness" N Engl J Med (2010)
Metrics
34
Citations
42
References
Details
Published
Feb 01, 2024
Vol/Issue
30(2)
License
View
Authors
Funding
Natural Science Foundation of Beijing Municipality Award: 7232049
Cite This Article
Xiaoke Chai, Tianqing Cao, Qiheng He, et al. (2024). Brain–computer interface digital prescription for neurological disorders. CNS Neuroscience & Therapeutics, 30(2). https://doi.org/10.1111/cns.14615
Related

You May Also Like

REVIEW: Curcumin and Alzheimer's Disease

Tsuyoshi Hamaguchi, Kenjiro Ono · 2010

388 citations

Altered Processing of Contextual Information during Fear Extinction in PTSD: An fMRI Study

Ansgar Rougemont-Bücking, Clas Linnman · 2010

252 citations

Seizures and Epilepsy in Alzheimer's Disease

Daniel Friedman, Lawrence S. Honig · 2011

199 citations