现代仪器与医疗

脑机接口应用于睡眠障碍的研究进展

PDF下载

朱皓 ¹ , 万力 ² , 陈树耿 ³^,^* , 章明勇 ¹ , 贾杰 ³

现代仪器与医疗 | 专栏 2025,31(5): 9-15

收起

现代仪器与医疗 |专栏 2025 , 31 (5) : 9 -15

脑机接口应用于睡眠障碍的研究进展

全屏

朱皓¹, 万力², 陈树耿³^,^*, 章明勇¹, 贾杰³

作者信息

^1.路桥区中医医院康复医学科，台州　318050

^2.深圳市第二人民医院康复医学科，深圳　518035

^3.复旦大学附属华山医院康复医学科，上海　200040

通讯作者:

陈树耿，博士研究生，主管治疗师，研究方向：脑机接口与神经康复

作者简介:

朱皓，硕士研究生，主治中医师，研究方向：脑机接口与神经康复

Research progress of brain-computer interface in sleep disorders

Hao Zhu¹, Li Wan², Shugeng Chen³^,^*, Mingyong Zhang¹, Jie Jia³

Affiliations

^1.Department of Rehabilitation Medicine, Luqiao District Hospital of Traditional Chinese Medicine, Taizhou 318050, China

^2.Department of Rehabilitation Medicine, Shenzhen Second People's Hospital, Shenzhen 518035, China

^3.Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China

出版时间: 2025-10-10 doi: 10.11876/mimt202505002

文章导航

摘要

收起

背景

随着现代社会工作与生活节奏的加快，睡眠障碍已成为一个日益普遍的问题。脑机接口（brain-computer interface，BCI）作为一种新兴的技术手段，为睡眠障碍的诊断与治疗提供了新的可能性。

目的

分析和总结近些年BCI在睡眠障碍中的应用，探讨BCI在睡眠障碍诊治中的临床应用价值。

方法

以“脑机接口”“脑电”“睡眠”“睡眠障碍”“睡眠监测”“睡眠干预”为中文检索词，在中国知网进行相关文献检索；以“brain-computer interface（BCI）”“electroencephalogram（EEG）”“sleep”“sleep disorders”“sleep monitoring”“sleep intervention”为英文检索词，在PubMed数据库进行相关文献检索，检索时间范围为2015年1月至2025年8月。初步检索获得678篇文献，经严格筛选以及手动加入2篇，最终纳入34篇相关文献。

结果

BCI通过高精度脑电信号分析与智能化算法，正在革新睡眠障碍的诊疗模式。借助多模态深度学习模型，BCI在睡眠分期中实现较高的分类准确率，并通过声、光、电等闭环刺激系统显著改善睡眠质量。然而，硬件端信号噪声干扰、算法泛化性不足、设备便携性、伦理隐私问题及临床标准化问题仍是其核心瓶颈。

结论

未来的研究应该集中突破设备在“材料—算法—硬件”协同、多模态闭环干预成熟度、标准化临床验证及伦理安全框架方面的问题，以期推动脑机接口技术在睡眠障碍研究领域的进一步发展。

关键词

脑机接口 / 睡眠障碍 / 睡眠分期 / 临床应用

Abstract

收起

Background

With the accelerating pace of work and life in modern society, sleep disorders have become an increasingly prevalent issue. Brain-computer interface (BCI), as an emerging technological tool, offers new possibilities for the diagnosis and treatment of sleep disorders.

Objective

To analyze and summarize the application of BCI in sleep disorders in recent years and explore its clinical value in the diagnosis and treatment of sleep disorders.

Methods

Literature searches were conducted using the search terms “brain-computer interface”, “electroencephalogram”, “sleep”, “sleep disorders”, “sleep monitoring”, and “sleep intervention” both on the PubMed and China National Knowledge Infrastructure (CNKI) database. The search period spanned from January 2015 to August 2025. The initial search yielded 678 documents. After strict screening and manual addition of 2 more, a total of 34 relevant documents were finally included.

Results

BCI technology is revolutionizing the diagnosis and treatment of sleep disorders by leveraging high-precision EEG signal analysis and intelligent algorithms. Multimodal deep learning models enable BCI systems to achieve high accuracy in sleep-stage classification, while closed-loop auditory, visual, and electrical stimulation systems significantly improve sleep quality. However, core challenges persist, including hardware-side signal noise interference, limited algorithmic generalizability, constraints in device portability, and ethical and privacy concerns, alongside the need for clinical standardization.

Conclusion

Future research should focus on breakthroughs in the synergy of “materials-algorithms-hardware”, enhancing the maturity of multimodal closed-loop intervention systems, establishing standardized clinical validation protocols, and developing robust ethical and safety frameworks. These efforts are expected to advance the further development of BCI technology in the field of sleep disorder research.

Key words

Brain-computer interface / Sleep disorders / Sleep staging / Clinical applications

引用本文

朱皓, 万力, 陈树耿, 章明勇, 贾杰. 脑机接口应用于睡眠障碍的研究进展. 现代仪器与医疗, 2025 , 31 (5) : 9 -15 . DOI: 10.11876/mimt202505002

Hao Zhu, Li Wan, Shugeng Chen, Mingyong Zhang, Jie Jia. Research progress of brain-computer interface in sleep disorders[J]. Modern Instruments & Medical Treatment, 2025 , 31 (5) : 9 -15 . DOI: 10.11876/mimt202505002

基金

收起

国家自然科学基金资助项目(82202798)
上海扬帆专项(22YF1404200)
上海市养老科技专项(24YL1900202)
深圳市科技计划项目(JCYJ20210324103409023)

参考文献

收起

文献

收起

参考文献引证文献

排序方式：

[1]

Morin C M, Buysse D J. Management of insomnia[J]. N Engl J Med, 2024, 391(3): 247-258.

[2]

中华医学会神经病学分会睡眠障碍学组. 中国成人失眠诊断与治疗指南(2023版)[J]. 中华神经科杂志, 2024, 57(6): 560-584.

[3]

Wickwire E M, Shaya F T, Scharf S M. Health economics of insomnia treatments: the return on investment for a good night's sleep[J]. Sleep Med Rev, 2016, 30: 72-82.

[4]

Riemann D, Espie C A, Altena E, et al. The European Insomnia Guideline: an update on the diagnosis and treatment of insomnia 2023[J]. J Sleep Res, 2023, 32(6): e14035.

[5]

Edinger J D, Arnedt J T, Bertisch S M, et al. Behavioral and psychological treatments for chronic insomnia disorder in adults: an American Academy of Sleep Medicine clinical practice guideline[J]. J Clin Sleep Med, 2021, 17(2): 255-262.

[6]

Leinders S, Aarnoutse E J, Branco M P, et al. Do not lose sleep over it: implanted brain-computer interface functionality during nighttime in late-stage amyotrophic lateral sclerosis[EB/OL]. (2024-10-11) [2025-07-03]. https://www.medrxiv.org/content/10.1101/2024.10.11.24315027v1.

[7]

Pahwa M, Kusner M, Hacker C D, et al. Optimizing the detection of wakeful and sleep-like states for future electrocorticographic brain computer interface applications[J]. PLoS One, 2015, 10(11): e0142947.

[8]

Fehér K D, Wunderlin M, Maier J G, et al. Shaping the slow waves of sleep: a systematic and integrative review of sleep slow wave modulation in humans using non-invasive brain stimulation[J]. Sleep Med Rev, 2021, 58: 101438.

[9]

Zrenner C, Belardinelli P, Müller-Dahlhaus F, et al. Closed-loop neuroscience and non-invasive brain stimulation: a tale of two loops[J]. Front Cell Neurosci, 2016, 10: 92.

[10]

Ngo H V, Martinetz T, Born J, et al. Auditory closed-loop stimulation of the sleep slow oscillation enhances memory[J]. Neuron, 2013, 78(3): 545-553.

[11]

Arif S, Khan M J, Naseer N, et al. Vector phase analysis approach for sleep stage classification: a functional near-infrared spectroscopy-based passive brain-computer interface[J]. Front Hum Neurosci, 2021, 15: 658444.

[12]

Huang H, Zhang J, Zhu L, et al. EEG-based sleep staging analysis with functional connectivity[J]. Sensors (Basel), 2021, 21(6): 1988.

[13]

Xu F, Zhao J, Liu M, et al. Exploration of sleep function connection and classification strategies based on sub-period sleep stages[J]. Front Neurosci, 2023, 16: 1088116.

[14]

Li J, Wang F, Huang H, et al. A novel semi-supervised meta learning method for subject-transfer brain-computer interface[J]. Neural Netw, 2023, 163: 195-204.

[15]

Zhang X, Landsness E C, Miao H, et al. Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data[EB/OL]. (2024-01-16) [2025-07-03]. https://arxiv.org/abs/2401.08098.

[16]

Narasimhan R, Gopalan M, Sikkandar M Y, et al. Employing deep-learning approach for the early detection of mild cognitive impairment transitions through the analysis of digital biomarkers[J]. Sensors (Basel), 2023, 23(21): 8867.

[17]

Mostafaei S H, Tanha J, Sharafkhaneh A. A novel deep learning model based on transformer and cross modality attention for classification of sleep stages[J]. J Biomed Inform, 2024, 157: 104689.

[18]

Guo Y, Nowakowski M, Dai W. FlexSleepTransformer: a transformer-based sleep staging model with flexible input channel configurations[J]. Sci Rep, 2024, 14(1): 26312.

[19]

Wang J, Zhao S, Jiang H, et al. CareSleepNet: a hybrid deep learning network for automatic sleep staging[J]. IEEE J Biomed Health Inform, 2024, 28(12): 7392-7405.

[20]

Hestermann E, Schreve K, Vandenheever D. Enhancing deep sleep induction through a wireless in-ear EEG device delivering binaural beats and ASMR: a proof-of-concept study[J]. Sensors (Basel), 2024, 24(23): 7471.

[21]

Ketz N, Jones A P, Bryant N B, et al. Closed-loop slow-wave tACS improves sleep-dependent long-term memory generalization by modulating endogenous oscillations[J]. J Neurosci, 2018, 38(33): 7314-7326.

[22]

Huber R, Esser S K, Ferrarelli F, et al. TMS-induced cortical potentiation during wakefulness locally increases slow wave activity during sleep[J]. PLoS One, 2007, 2(3): e276.

[23]

Shanahan L K, Gjorgieva E, Paller K A, et al. Odor-evoked category reactivation in human ventromedial prefrontal cortex during sleep promotes memory consolidation[J]. Elife, 2018, 7: e39681.

[24]

Okabe S, Fukuda K, Mochizuki-Kawai H, et al. Favorite odor induces negative dream emotion during rapid eye movement sleep[J]. Sleep Med, 2018, 47: 72-76.

[25]

Zhou X, He Y, Xu T, et al. 40 Hz light flickering promotes sleep through cortical adenosine signaling[J]. Cell Res, 2024, 34(3): 214-231.

[26]

Schade M M, Mathew G M, Roberts D M, et al. Enhancing slow oscillations and increasing N3 sleep proportion with supervised, non-phase-locked pink noise and other non-standard auditory stimulation during NREM sleep[J]. Nat Sci Sleep, 2020, 12: 411-429.

[27]

Wei J, Xiao C, Zhang G W, et al. A distributed auditory network mediated by pontine central gray underlies ultra-fast awakening in response to alerting sounds[J]. Curr Biol, 2024, 34(20): 4597-4611.

[28]

Hebron H, Lugli B, Dimitrova R, et al. A closed-loop auditory stimulation approach selectively modulates alpha oscillations and sleep onset dynamics in humans[J]. PLoS Biol, 2024, 22(6): e3002651.

[29]

衡玮, 徐自强, 陈璐瑶, 等. 基于脑电波监测智能助睡眠耳机设计[J]. 计算机应用与软件, 2020, 37(8): 219-226, 264.

[30]

Henao D, Navarrete M, Juez J Y, et al. Auditory closed-loop stimulation on sleep slow oscillations using in-ear EEG sensors[J]. J Sleep Res, 2022, 31(6): e13555.

[31]

Liu X Y, Wang W L, Liu M, et al. Recent applications of EEG-based brain-computer-interface in the medical field[J]. Mil Med Res, 2025, 12(1): 14.

[32]

Mohamed M, Mohamed N, Kim J G. Advancements in wearable EEG technology for improved home-based sleep monitoring and assessment: a review[J]. Biosensors (Basel), 2023, 13(12): 1019.

[33]

Markov K, Elgendi M, Menon C. EEG-based headset sleep wearable devices[J]. npj Biosensing, 2024, 1: 12.

[34]

Belkacem A N, Jamil N, Khalid S, et al. On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders[J]. Front Hum Neurosci, 2023, 17: 1085173.

2025年第31卷第5期

PDF下载

引用本文

BibTeX

文章信息

doi: 10.11876/mimt202505002

首发时间：2026-10-23
出版时间：2025-10-10

补充材料

相关文章

文章信息

作者

出版历史

基金

国家自然科学基金资助项目(82202798)

上海扬帆专项(22YF1404200)

上海市养老科技专项(24YL1900202)

深圳市科技计划项目(JCYJ20210324103409023)

作者信息

^1.路桥区中医医院康复医学科，台州　318050

^2.深圳市第二人民医院康复医学科，深圳　518035

^3.复旦大学附属华山医院康复医学科，上海　200040

通讯作者:

陈树耿，博士研究生，主管治疗师，研究方向：脑机接口与神经康复

参考文献

分享链接

https://castjournals.cast.org.cn/joweb/xdyqyyl/CN/10.11876/mimt202505002

分享至

全文二维码

扫描看全文

引用本文

BibTeX

本文的引用情况

2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

关闭全屏

BibTeX
EndNote
RefWorks
TxT