• 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

基于 EEG 的神经生理学实验新型 OpenBCI 框架

A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments.

机构信息

Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia.

Automatics Research Group, Universidad Tecnológica de Pereria, Pereira 660003, Colombia.

出版信息

Sensors (Basel). 2023 Apr 6;23(7):3763. doi: 10.3390/s23073763.

DOI:10.3390/s23073763
PMID:37050823
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10098804/
Abstract

An Open Brain-Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the performance of OpenBCI. These limitations include the need for more effective communication between computers and peripheral devices and more flexibility for fast settings under specific protocols for neurophysiological data. This paper describes a flexible and scalable OpenBCI framework for electroencephalographic (EEG) data experiments using the Cyton acquisition board with updated drivers to maximize the hardware benefits of ADS1299 platforms. The framework handles distributed computing tasks and supports multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. As a result, the OpenBCI system delivers real-time feedback and controlled execution of EEG-based clinical protocols for implementing the steps of neural recording, decoding, stimulation, and real-time analysis. In addition, the system incorporates automatic background configuration and user-friendly widgets for stimuli delivery. Motor imagery tests the closed-loop BCI designed to enable real-time streaming within the required latency and jitter ranges. Therefore, the presented framework offers a promising solution for tailored neurophysiological data processing.

摘要

开源脑-机接口 (OpenBCI) 通过低成本实现的开源硬件和固件提供了无与伦比的自由度和灵活性。它利用强大的硬件平台和强大的软件开发工具包来创建具有高级功能的定制驱动程序。尽管如此，一些限制可能会显著降低 OpenBCI 的性能。这些限制包括需要在计算机和外围设备之间进行更有效的通信，以及在特定神经生理数据协议下需要更灵活的快速设置。本文描述了一种灵活且可扩展的 OpenBCI 框架，用于使用 Cyton 采集板进行脑电图 (EEG) 数据实验，并使用更新的驱动程序来最大程度地发挥 ADS1299 平台的硬件优势。该框架处理分布式计算任务，并支持多种采样率、通信协议、自由电极放置和单个标记同步。结果，OpenBCI 系统提供了实时反馈和基于 EEG 的临床协议的控制执行，以实现神经记录、解码、刺激和实时分析的步骤。此外，该系统还集成了自动后台配置和用户友好的刺激传递小部件。运动想象测试旨在实现实时流媒体的闭环 BCI，以满足所需的延迟和抖动范围。因此，所提出的框架为定制神经生理数据处理提供了一个有前途的解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/767e76fb77fa/sensors-23-03763-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/0b07e0fc33c4/sensors-23-03763-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/524fa23aee60/sensors-23-03763-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/44361a961625/sensors-23-03763-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/50befcb5779d/sensors-23-03763-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/772bf259fc91/sensors-23-03763-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/f06462bd7804/sensors-23-03763-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/b1ed4a72ee22/sensors-23-03763-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/3e69a692f949/sensors-23-03763-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/071d0872126b/sensors-23-03763-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/69c40952ca95/sensors-23-03763-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/30a9c44126c5/sensors-23-03763-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/bbf486ee57ff/sensors-23-03763-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/6da89a780697/sensors-23-03763-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/f00f08edd37a/sensors-23-03763-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/8cbddc0af559/sensors-23-03763-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/b88b20c51386/sensors-23-03763-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/a9dc7e798a14/sensors-23-03763-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/6ed2e2b02230/sensors-23-03763-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/767e76fb77fa/sensors-23-03763-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/0b07e0fc33c4/sensors-23-03763-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/524fa23aee60/sensors-23-03763-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/44361a961625/sensors-23-03763-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/50befcb5779d/sensors-23-03763-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/772bf259fc91/sensors-23-03763-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/f06462bd7804/sensors-23-03763-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/b1ed4a72ee22/sensors-23-03763-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/3e69a692f949/sensors-23-03763-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/071d0872126b/sensors-23-03763-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/69c40952ca95/sensors-23-03763-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/30a9c44126c5/sensors-23-03763-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/bbf486ee57ff/sensors-23-03763-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/6da89a780697/sensors-23-03763-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/f00f08edd37a/sensors-23-03763-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/8cbddc0af559/sensors-23-03763-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/b88b20c51386/sensors-23-03763-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/a9dc7e798a14/sensors-23-03763-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/6ed2e2b02230/sensors-23-03763-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34a5/10098804/767e76fb77fa/sensors-23-03763-g019.jpg

相似文献

1
A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments.基于 EEG 的神经生理学实验新型 OpenBCI 框架
Sensors (Basel). 2023 Apr 6;23(7):3763. doi: 10.3390/s23073763.
2
Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping.记录用于神经科学研究和实时功能性皮层图谱绘制的人类皮层脑电图（ECoG）信号。
J Vis Exp. 2012 Jun 26(64):3993. doi: 10.3791/3993.
3
A Systematic Comparison of High-End and Low-Cost EEG Amplifiers for Concealed, Around-the-Ear EEG Recordings.高端和低成本 EEG 放大器在隐蔽、环绕式 EEG 记录中的系统比较。
Sensors (Basel). 2023 May 8;23(9):4559. doi: 10.3390/s23094559.
4
A Robust Low-Cost EEG Motor Imagery-Based Brain-Computer Interface.一种基于稳健低成本脑电图运动想象的脑机接口。
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:5089-5092. doi: 10.1109/EMBC.2018.8513429.
5
A Combined Virtual Electrode-Based ESA and CNN Method for MI-EEG Signal Feature Extraction and Classification.基于虚拟电极的组合 ESA 和 CNN 方法在 MI-EEG 信号特征提取和分类中的应用。
Sensors (Basel). 2023 Nov 1;23(21):8893. doi: 10.3390/s23218893.
6
Design-development of an at-home modular brain-computer interface (BCI) platform in a case study of cervical spinal cord injury.设计开发一种家用模块化脑机接口（BCI）平台，以颈髓损伤为例的研究。
J Neuroeng Rehabil. 2022 Jun 3;19(1):53. doi: 10.1186/s12984-022-01026-2.
7
Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets.利用黎曼几何算法对大型 EEG 数据集进行多类运动想象和运动执行任务的解码。
Sensors (Basel). 2023 May 25;23(11):5051. doi: 10.3390/s23115051.
8
Fully Open-Access Passive Dry Electrodes BIOADC: Open-Electroencephalography (EEG) Re-Invented.全开放访问被动干电极生物模拟前端：重新发明开放式脑电图（EEG）。
Sensors (Basel). 2019 Feb 13;19(4):772. doi: 10.3390/s19040772.
9
Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity.基于 CNN 连通性的分组对主体运动想象技能的神经反应进行事后可解释性分析。
Sensors (Basel). 2023 Mar 2;23(5):2750. doi: 10.3390/s23052750.
10
SimBSI: An open-source Simulink library for developing closed-loop brain signal interfaces in animals and humans.SimBSI：一个用于在动物和人类中开发闭环脑信号接口的开源 Simulink 库。
Biomed Phys Eng Express. 2020 Apr 15;6(3):035023. doi: 10.1088/2057-1976/ab6e20.

引用本文的文献

1
The safety and efficacy of applying a high-current temporal interference electrical stimulation in humans.在人体中应用高电流经皮神经电刺激的安全性和有效性。
Front Hum Neurosci. 2024 Nov 29;18:1484593. doi: 10.3389/fnhum.2024.1484593. eCollection 2024.
2
Evaluation of an English language phoneme-based imagined speech brain computer interface with low-cost electroencephalography.基于英语音素的想象语音脑机接口与低成本脑电图的评估。
Front Neuroinform. 2023 Dec 18;17:1306277. doi: 10.3389/fninf.2023.1306277. eCollection 2023.
3
State-of-the-Art on Brain-Computer Interface Technology.

本文引用的文献

1
A scoping review on the use of consumer-grade EEG devices for research.消费者级 EEG 设备在研究中的应用：范围综述
PLoS One. 2024 Mar 6;19(3):e0291186. doi: 10.1371/journal.pone.0291186. eCollection 2024.
2
Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG Systems Review.最先进的可穿戴和无线 EEG 系统综述：多样性和适用性。
IEEE J Biomed Health Inform. 2023 Aug;27(8):3830-3843. doi: 10.1109/JBHI.2023.3239053. Epub 2023 Aug 8.
3
A Simplified Electroencephalography Montage and Interpretation for Evaluation of Comatose Patients in the ICU.
脑机接口技术的最新进展。
Sensors (Basel). 2023 Jun 28;23(13):6001. doi: 10.3390/s23136001.
一种用于评估重症监护病房昏迷患者的简化脑电图组合及解读方法。
Crit Care Explor. 2022 Nov 3;4(11):e0781. doi: 10.1097/CCE.0000000000000781. eCollection 2022 Nov.
4
Electroencephalogram in the intensive care unit: a focused look at acute brain injury.重症监护病房中的脑电图：急性脑损伤的重点观察。
Intensive Care Med. 2022 Oct;48(10):1443-1462. doi: 10.1007/s00134-022-06854-3. Epub 2022 Aug 23.
5
Good scientific practice in EEG and MEG research: Progress and perspectives.脑电图和脑磁图研究中的良好科学实践：进展与展望。
Neuroimage. 2022 Aug 15;257:119056. doi: 10.1016/j.neuroimage.2022.119056. Epub 2022 Mar 10.
6
Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain-Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate.用于虚拟现实应用的新型混合脑机接口，采用基于稳态视觉诱发电位的脑机接口和基于眼电图的眼动追踪技术以提高信息传输速率。
Front Neuroinform. 2022 Feb 24;16:758537. doi: 10.3389/fninf.2022.758537. eCollection 2022.
7
"Tricking the Brain" Using Immersive Virtual Reality: Modifying the Self-Perception Over Embodied Avatar Influences Motor Cortical Excitability and Action Initiation.利用沉浸式虚拟现实“欺骗大脑”：改变对具身化虚拟形象的自我认知会影响运动皮层兴奋性和动作发起。
Front Hum Neurosci. 2022 Feb 9;15:787487. doi: 10.3389/fnhum.2021.787487. eCollection 2021.
8
Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks.基于单试核的功能连接在运动相关任务中的特征提取增强。
Sensors (Basel). 2021 Apr 13;21(8):2750. doi: 10.3390/s21082750.
9
Summary of over Fifty Years with Brain-Computer Interfaces-A Review.脑机接口五十多年综述
Brain Sci. 2021 Jan 3;11(1):43. doi: 10.3390/brainsci11010043.
10
A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection.用于困倦检测的现有低成本脑电图耳机的系统评价。
Front Neuroinform. 2020 Oct 15;14:553352. doi: 10.3389/fninf.2020.553352. eCollection 2020.