• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

立体定向脑电图在脑机接口中的潜力:当前进展与未来方向。

The Potential of Stereotactic-EEG for Brain-Computer Interfaces: Current Progress and Future Directions.

作者信息

Herff Christian, Krusienski Dean J, Kubben Pieter

机构信息

Department of Neurosurgery, School of Mental Health and Neurosciences, Maastricht University, Maastricht, Netherlands.

ASPEN Lab, Biomedical Engineering Department, Virginia Commonwealth University, Richmond, VA, United States.

出版信息

Front Neurosci. 2020 Feb 27;14:123. doi: 10.3389/fnins.2020.00123. eCollection 2020.

DOI:10.3389/fnins.2020.00123
PMID:32174810
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7056827/
Abstract

Stereotactic electroencephalogaphy (sEEG) utilizes localized, penetrating depth electrodes to measure electrophysiological brain activity. It is most commonly used in the identification of epileptogenic zones in cases of refractory epilepsy. The implanted electrodes generally provide a sparse sampling of a unique set of brain regions including deeper brain structures such as hippocampus, amygdala and insula that cannot be captured by superficial measurement modalities such as electrocorticography (ECoG). Despite the overlapping clinical application and recent progress in decoding of ECoG for Brain-Computer Interfaces (BCIs), sEEG has thus far received comparatively little attention for BCI decoding. Additionally, the success of the related deep-brain stimulation (DBS) implants bodes well for the potential for chronic sEEG applications. This article provides an overview of sEEG technology, BCI-related research, and prospective future directions of sEEG for long-term BCI applications.

摘要

立体定向脑电图(sEEG)利用局部穿透深度电极来测量脑电生理活动。它最常用于难治性癫痫病例中致痫区的识别。植入电极通常对一组独特的脑区进行稀疏采样,包括海马体、杏仁核和脑岛等深部脑结构,这些结构无法通过诸如皮层脑电图(ECoG)等表面测量方式捕获。尽管在脑机接口(BCI)的ECoG解码方面有重叠的临床应用和近期进展,但迄今为止,sEEG在BCI解码方面受到的关注相对较少。此外,相关深部脑刺激(DBS)植入的成功预示着慢性sEEG应用的潜力。本文概述了sEEG技术、与BCI相关的研究以及sEEG在长期BCI应用方面未来的潜在发展方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac36/7056827/f8d7580f1c0f/fnins-14-00123-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac36/7056827/2fc6a035b9f8/fnins-14-00123-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac36/7056827/f8d7580f1c0f/fnins-14-00123-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac36/7056827/2fc6a035b9f8/fnins-14-00123-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac36/7056827/f8d7580f1c0f/fnins-14-00123-g0002.jpg

相似文献

1
The Potential of Stereotactic-EEG for Brain-Computer Interfaces: Current Progress and Future Directions.立体定向脑电图在脑机接口中的潜力:当前进展与未来方向。
Front Neurosci. 2020 Feb 27;14:123. doi: 10.3389/fnins.2020.00123. eCollection 2020.
2
Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings.使用立体脑电图记录评估多个领域中手部运动的差异表征。
Neuroimage. 2022 Apr 15;250:118969. doi: 10.1016/j.neuroimage.2022.118969. Epub 2022 Feb 4.
3
A Review of Motor Brain-Computer Interfaces Using Intracranial Electroencephalography Based on Surface Electrodes and Depth Electrodes.基于表面电极和深部电极的颅内脑电图的运动脑-机接口综述
IEEE Trans Neural Syst Rehabil Eng. 2024;32:2408-2431. doi: 10.1109/TNSRE.2024.3421551. Epub 2024 Jul 4.
4
Investigating Data Cleaning Methods to Improve Performance of Brain-Computer Interfaces Based on Stereo-Electroencephalography.研究数据清理方法以提高基于立体脑电图的脑机接口性能。
Front Neurosci. 2021 Oct 6;15:725384. doi: 10.3389/fnins.2021.725384. eCollection 2021.
5
A P300-Based BCI System Using Stereoelectroencephalography and Its Application in a Brain Mechanistic Study.基于立体脑电图的 P300 脑-机接口系统及其在脑机制研究中的应用。
IEEE Trans Biomed Eng. 2021 Aug;68(8):2509-2519. doi: 10.1109/TBME.2020.3047812. Epub 2021 Jul 16.
6
Direct Electrical Stimulation in Electrocorticographic Brain-Computer Interfaces: Enabling Technologies for Input to Cortex.皮层脑电图脑机接口中的直接电刺激:用于向皮层输入的使能技术。
Front Neurosci. 2019 Aug 7;13:804. doi: 10.3389/fnins.2019.00804. eCollection 2019.
7
Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand.解码大脑脑沟和白质区域的神经活动以准确预测人类手部的个体手指运动和触觉刺激。
Front Neurosci. 2021 Aug 17;15:699631. doi: 10.3389/fnins.2021.699631. eCollection 2021.
8
[Intracranial EEG monitoring methods].[颅内脑电图监测方法]
Ideggyogy Sz. 2020 Mar 30;73(3-4):79-83. doi: 10.18071/isz.73.0079.
9
The Potential for a Speech Brain-Computer Interface Using Chronic Electrocorticography.利用慢性皮层脑电图实现语音脑-机接口的潜力
Neurotherapeutics. 2019 Jan;16(1):144-165. doi: 10.1007/s13311-018-00692-2.
10
Speech Synthesis from Stereotactic EEG using an Electrode Shaft Dependent Multi-Input Convolutional Neural Network Approach.基于电极轴相关多输入卷积神经网络的立体脑电图语音合成。
Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:6045-6048. doi: 10.1109/EMBC46164.2021.9629711.

引用本文的文献

1
Directional hand movement can be classified from insular cortex SEEG signals using recurrent neural networks and high-gamma band features.使用递归神经网络和高伽马波段特征,可以从岛叶皮质的立体定向脑电图信号中对定向手部运动进行分类。
Sci Rep. 2025 Aug 16;15(1):29993. doi: 10.1038/s41598-025-14805-3.
2
A Comprehensive Survey of Brain-Computer Interface Technology in Health care: Research Perspectives.医疗保健领域脑机接口技术综合调查:研究视角
J Med Signals Sens. 2025 Jun 9;15:16. doi: 10.4103/jmss.jmss_49_24. eCollection 2025.
3
Invasive Brain-Computer Interface for Communication: A Scoping Review.

本文引用的文献

1
Deep brain stimulation: An overview of history, methods, and future developments.深部脑刺激:历史、方法及未来发展概述
Brain Neurosci Adv. 2018 Dec 12;2:2398212818816017. doi: 10.1177/2398212818816017. eCollection 2018 Jan-Dec.
2
Stability of a chronic implanted brain-computer interface in late-stage amyotrophic lateral sclerosis.晚期肌萎缩侧索硬化症慢性植入式脑机接口的稳定性。
Clin Neurophysiol. 2019 Oct;130(10):1798-1803. doi: 10.1016/j.clinph.2019.07.020. Epub 2019 Jul 27.
3
Real-time decoding of question-and-answer speech dialogue using human cortical activity.
用于交流的侵入性脑机接口:一项范围综述
Brain Sci. 2025 Mar 24;15(4):336. doi: 10.3390/brainsci15040336.
4
VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language.VocalMind:一个用于有声、哑剧和想象中的声调语言语音的立体定向脑电图数据集。
Sci Data. 2025 Apr 19;12(1):657. doi: 10.1038/s41597-025-04741-2.
5
Materials and devices for high-density, high-throughput micro-electrocorticography arrays.用于高密度、高通量微皮层脑电图阵列的材料和设备。
Fundam Res. 2024 Feb 28;5(1):17-28. doi: 10.1016/j.fmre.2024.01.016. eCollection 2025 Jan.
6
Transformer-based neural speech decoding from surface and depth electrode signals.基于Transformer的从表面和深度电极信号进行神经语音解码
J Neural Eng. 2025 Jan 28;22(1):016017. doi: 10.1088/1741-2552/adab21.
7
A review of ethical considerations for the medical applications of brain-computer interfaces.脑机接口医学应用的伦理考量综述。
Cogn Neurodyn. 2024 Dec;18(6):3603-3614. doi: 10.1007/s11571-024-10144-7. Epub 2024 Sep 24.
8
Neural Decoding of Spontaneous Overt and Intended Speech.自发性言语的神经解码:显性和意图性言语。
J Speech Lang Hear Res. 2024 Nov 7;67(11):4216-4225. doi: 10.1044/2024_JSLHR-24-00046. Epub 2024 Aug 6.
9
Representation of internal speech by single neurons in human supramarginal gyrus.人类缘上回听皮层中单个神经元对内部言语的表征。
Nat Hum Behav. 2024 Jun;8(6):1136-1149. doi: 10.1038/s41562-024-01867-y. Epub 2024 May 13.
10
Subject-Agnostic Transformer-Based Neural Speech Decoding from Surface and Depth Electrode Signals.基于表面和深度电极信号的与主题无关的基于Transformer的神经语音解码
bioRxiv. 2024 Sep 25:2024.03.11.584533. doi: 10.1101/2024.03.11.584533.
使用人类大脑皮层活动实时解码问答式语音对话。
Nat Commun. 2019 Jul 30;10(1):3096. doi: 10.1038/s41467-019-10994-4.
4
Speaker-independent auditory attention decoding without access to clean speech sources.无需访问干净语音源的说话人无关听觉注意力解码。
Sci Adv. 2019 May 15;5(5):eaav6134. doi: 10.1126/sciadv.aav6134. eCollection 2019 May.
5
Speech synthesis from neural decoding of spoken sentences.基于语音解码的语音合成
Nature. 2019 Apr;568(7753):493-498. doi: 10.1038/s41586-019-1119-1. Epub 2019 Apr 24.
6
Human motor cortex relies on sparse and action-specific activation during laughing, smiling and speech production.人类运动皮层在发笑、微笑和言语产生过程中依赖于稀疏且特定于动作的激活。
Commun Biol. 2019 Mar 26;2:118. doi: 10.1038/s42003-019-0360-3. eCollection 2019.
7
Speech synthesis from ECoG using densely connected 3D convolutional neural networks.使用密集连接的 3D 卷积神经网络进行脑电信号合成。
J Neural Eng. 2019 Jun;16(3):036019. doi: 10.1088/1741-2552/ab0c59. Epub 2019 Mar 4.
8
Subthalamic Nucleus and Sensorimotor Cortex Activity During Speech Production.丘脑底核和感觉运动皮层在言语产生过程中的活动。
J Neurosci. 2019 Apr 3;39(14):2698-2708. doi: 10.1523/JNEUROSCI.2842-18.2019. Epub 2019 Jan 30.
9
Towards reconstructing intelligible speech from the human auditory cortex.从人类听觉皮层重建可理解的语音。
Sci Rep. 2019 Jan 29;9(1):874. doi: 10.1038/s41598-018-37359-z.
10
Coarse behavioral context decoding.粗糙行为上下文解码。
J Neural Eng. 2019 Feb;16(1):016021. doi: 10.1088/1741-2552/aaee9c. Epub 2018 Nov 6.