The application of brain-computer interface in the diagnosis and treatment of addiction and sleep disorder
10.12025/j.issn.1008-6358.2026.20251622
- VernacularTitle:脑机接口在成瘾及睡眠障碍诊疗中的应用
- Author:
Yifei ZHU
1
;
Zhe DU
2
;
Kai YUAN
2
Author Information
1. Department of Sleep Medicine, Sanya Second People’s Hospital, Sanya 572100, Hainan, China.
2. School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi, China.
- Publication Type:Monographicreport:Technologicalbreakthroughandclinicalapplicationofbrain-computerinterface
- Keywords:
brain-computer interface;
closed-loop neuromodulation;
addiction;
sleep disorder;
brain-body interaction
- From:
Chinese Journal of Clinical Medicine
2026;33(2):226-229
- CountryChina
- Language:Chinese
-
Abstract:
Brain-computer interface (BCI) is a neuro-engineering technology that establishes a direct communication pathway between the brain and external devices. It can realize bidirectional information interaction between the brain and external devices through real-time acquisition and decoding of brain signals, thereby completing brain state recognition and precise feedback regulation. This technology is promoting the transformation of the diagnosis and treatment model of neuropsychiatric diseases from “open-loop stimulation” to “closed-loop adaptation”. The brain regions and circuits involved in the neural mechanisms of addiction and sleep disorder are highly intertwined. Sleep-related brain rhythms provide a key time window for the intervention of addiction memory, and both have abnormal electrophysiological activities and functional imbalances in core neural circuits. This article reviews the latest research progress of closed-loop BCI in the fields of addiction and sleep disorder, explains the neural circuits and electrophysiological mechanisms of its regulation, and its limitations in diagnosis and treatment of the two diseases. In addition, it prospects individualized closed-loop intervention from the perspective of brain-body interaction, so as to provide reference for the clinical transformation of closed-loop BCI.
