• 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

用于被动脑机接口应用的开放式多会话和多任务 EEG 认知数据集。

Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications.

机构信息

ISAE-SUPAERO, Université de Toulouse, Toulouse, France.

DTIS, ONERA, F-13661 Salon Cedex Air, France.

出版信息

Sci Data. 2023 Feb 10;10(1):85. doi: 10.1038/s41597-022-01898-y.

DOI:10.1038/s41597-022-01898-y
PMID:36765121
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9918545/
Abstract

Brain-Computer Interfaces and especially passive Brain-Computer interfaces (pBCI), with their ability to estimate and monitor user mental states, are receiving increasing attention from both the fundamental research and the applied research and development communities. Testing new pipelines and benchmarking classifiers and feature extraction algorithms is central to further research within this domain. Unfortunately, data sharing in pBCI research is still scarce. The COG-BCI database encompasses the recordings of 29 participants over 3 separate sessions with 4 different tasks (MATB, N-Back, PVT, Flanker) designed to elicit different mental states, for a total of over 100 hours of open EEG data. This dataset was validated on a subjective, behavioral and physiological level, to ensure its usefulness to the pBCI community. Furthermore, a proof of concept is given with an example of mental workload estimation pipeline and results, to ensure that the data can be used for the design and evaluation of pBCI pipelines. This body of work presents a large effort to promote the use of pBCIs in an open science framework.

摘要

脑机接口，特别是被动脑机接口（pBCI），因其能够估计和监测用户的精神状态，正受到基础研究和应用研究与开发界越来越多的关注。在该领域内的进一步研究中，测试新的管道和基准分类器以及特征提取算法是核心内容。不幸的是，pBCI 研究中的数据共享仍然很少。COG-BCI 数据库包含了 29 名参与者在 3 次不同的会议中的记录，这些会议共包含 4 种不同的任务（MATB、N-Back、PVT、Flanker），旨在引出不同的精神状态，总共有超过 100 小时的公开 EEG 数据。该数据集在主观、行为和生理层面上进行了验证，以确保其对 pBCI 社区的有用性。此外，还提供了一个心理工作量估计管道和结果的概念验证示例，以确保数据可用于 pBCI 管道的设计和评估。这项工作旨在大力推动 pBCI 在开放科学框架中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/7b0c5b3c4097/41597_2022_1898_Fig22_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/55e4ced1b5b2/41597_2022_1898_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/4b940f5d4188/41597_2022_1898_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/a2c5c38d78eb/41597_2022_1898_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/50e7674f0d88/41597_2022_1898_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/a78790178793/41597_2022_1898_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/9ed32bd9a64a/41597_2022_1898_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/717a04b02b03/41597_2022_1898_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/9d9c7943a369/41597_2022_1898_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/6a4726b6534b/41597_2022_1898_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/06f94fe574d1/41597_2022_1898_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/b227e6b5f786/41597_2022_1898_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/2d0f99ab7c70/41597_2022_1898_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/2875b00c0c87/41597_2022_1898_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/b64b9b928b6e/41597_2022_1898_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/752c1855d4a4/41597_2022_1898_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/e2080ba1adbf/41597_2022_1898_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/7e01c3368596/41597_2022_1898_Fig17_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/aa45baa492f1/41597_2022_1898_Fig18_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/4211f20841ac/41597_2022_1898_Fig19_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/864c764d943c/41597_2022_1898_Fig20_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/3ebd00a0c8f7/41597_2022_1898_Fig21_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/7b0c5b3c4097/41597_2022_1898_Fig22_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/55e4ced1b5b2/41597_2022_1898_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/4b940f5d4188/41597_2022_1898_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/a2c5c38d78eb/41597_2022_1898_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/50e7674f0d88/41597_2022_1898_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/a78790178793/41597_2022_1898_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/9ed32bd9a64a/41597_2022_1898_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/717a04b02b03/41597_2022_1898_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/9d9c7943a369/41597_2022_1898_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/6a4726b6534b/41597_2022_1898_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/06f94fe574d1/41597_2022_1898_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/b227e6b5f786/41597_2022_1898_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/2d0f99ab7c70/41597_2022_1898_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/2875b00c0c87/41597_2022_1898_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/b64b9b928b6e/41597_2022_1898_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/752c1855d4a4/41597_2022_1898_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/e2080ba1adbf/41597_2022_1898_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/7e01c3368596/41597_2022_1898_Fig17_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/aa45baa492f1/41597_2022_1898_Fig18_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/4211f20841ac/41597_2022_1898_Fig19_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/864c764d943c/41597_2022_1898_Fig20_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/3ebd00a0c8f7/41597_2022_1898_Fig21_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be0/9918545/7b0c5b3c4097/41597_2022_1898_Fig22_HTML.jpg

相似文献

1
Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications.用于被动脑机接口应用的开放式多会话和多任务 EEG 认知数据集。
Sci Data. 2023 Feb 10;10(1):85. doi: 10.1038/s41597-022-01898-y.
2
Passive BCI in Operational Environments: Insights, Recent Advances, and Future Trends.运行环境中的被动脑机接口：见解、最新进展与未来趋势
IEEE Trans Biomed Eng. 2017 Jul;64(7):1431-1436. doi: 10.1109/TBME.2017.2694856. Epub 2017 Apr 17.
3
Prediction of Individual User's Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain-Computer Interface Applications.基于静息态 EEG 数据预测个体用户的 EEG 特征动态范围，用于评估其在被动脑机接口应用中的适用性。
Sensors (Basel). 2020 Feb 12;20(4):988. doi: 10.3390/s20040988.
4
Retrospective on the First Passive Brain-Computer Interface Competition on Cross-Session Workload Estimation.关于首次跨会话工作负荷估计被动式脑机接口竞赛的回顾
Front Neuroergon. 2022 Apr 4;3:838342. doi: 10.3389/fnrgo.2022.838342. eCollection 2022.
5
Enhancing EEG-based cross-day mental workload classification using periodic component of power spectrum.利用功率谱的周期性成分增强基于脑电图的跨日心理负荷分类
J Neural Eng. 2023 Dec 12;20(6). doi: 10.1088/1741-2552/ad0f3d.
6
EEG-based hierarchical classification of level of demand and modality of auditory and visual sensory processing.基于 EEG 的听觉和视觉感觉处理需求水平和模态的层次分类。
J Neural Eng. 2024 Jan 17;21(1). doi: 10.1088/1741-2552/ad1ac1.
7
EEG-based detection of mental workload level and stress: the effect of variation in each state on classification of the other.基于 EEG 的脑力负荷和压力水平检测：每种状态变化对另一种状态分类的影响。
J Neural Eng. 2020 Oct 15;17(5):056015. doi: 10.1088/1741-2552/abbc27.
8
EEG-based detection of modality-specific visual and auditory sensory processing.基于脑电图的特定模态视觉和听觉感觉处理检测
J Neural Eng. 2023 Feb 20;20(1). doi: 10.1088/1741-2552/acb9be.
9
Design of Wearable EEG Devices Specialized for Passive Brain-Computer Interface Applications.用于被动脑机接口应用的可穿戴 EEG 设备设计。
Sensors (Basel). 2020 Aug 14;20(16):4572. doi: 10.3390/s20164572.
10
A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces.大型脑电运动想象数据集用于脑电脑机接口。
Sci Data. 2018 Oct 16;5:180211. doi: 10.1038/sdata.2018.211.

引用本文的文献

1
The effects of cues on task interruption recovery in a concurrent multitasking environment.在并发多任务处理环境中，线索对任务中断恢复的影响。
Sci Rep. 2025 Jul 17;15(1):25992. doi: 10.1038/s41598-025-09358-4.
2
The impact of cross-validation choices on pBCI classification metrics: lessons for transparent reporting.交叉验证选择对脑机接口分类指标的影响：透明报告的经验教训。
Front Neuroergon. 2025 Jul 1;6:1582724. doi: 10.3389/fnrgo.2025.1582724. eCollection 2025.
3
Evaluation of EEG pre-processing and source localization in ecological research.

本文引用的文献

1
Dual Passive Reactive Brain-Computer Interface: A Novel Approach to Human-Machine Symbiosis.双被动反应式脑机接口：人机共生的新方法。
Front Neuroergon. 2022 Apr 11;3:824780. doi: 10.3389/fnrgo.2022.824780. eCollection 2022.
2
Grand Challenges in Neurotechnology and System Neuroergonomics.神经技术与系统神经工效学的重大挑战。
Front Neuroergon. 2020 Nov 30;1:602504. doi: 10.3389/fnrgo.2020.602504. eCollection 2020.
3
Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training.一名四肢瘫痪用户的长期脑机接口训练：自适应黎曼分类器与用户训练
生态研究中脑电图预处理与源定位的评估
Front Neuroimaging. 2025 Mar 31;4:1479569. doi: 10.3389/fnimg.2025.1479569. eCollection 2025.
4
A cross-attention swin transformer network for EEG-based subject-independent cognitive load assessment.一种用于基于脑电图的独立于个体的认知负荷评估的交叉注意力窗口变压器网络。
Cogn Neurodyn. 2024 Dec;18(6):3805-3819. doi: 10.1007/s11571-024-10160-7. Epub 2024 Aug 20.
5
Unobtrusive measurement of cognitive load and physiological signals in uncontrolled environments.在非控制环境下对认知负荷和生理信号进行非侵入式测量。
Sci Data. 2024 Sep 13;11(1):1000. doi: 10.1038/s41597-024-03738-7.
6
Reproducible machine learning research in mental workload classification using EEG.使用脑电图进行心理负荷分类的可重复机器学习研究。
Front Neuroergon. 2024 Apr 10;5:1346794. doi: 10.3389/fnrgo.2024.1346794. eCollection 2024.
7
An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior.鼓励欺骗行为的竞争性二人游戏中的神经特征 EEG 数据集。
Sci Data. 2024 Apr 16;11(1):389. doi: 10.1038/s41597-024-03234-y.
8
Application and Development of EEG Acquisition and Feedback Technology: A Review.脑电采集与反馈技术的应用与发展：综述。
Biosensors (Basel). 2023 Oct 17;13(10):930. doi: 10.3390/bios13100930.
Front Hum Neurosci. 2021 Mar 18;15:635653. doi: 10.3389/fnhum.2021.635653. eCollection 2021.
4
An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction.基于 EEG 的跨被试疲劳心理状态预测迁移学习方法。
Sensors (Basel). 2021 Mar 29;21(7):2369. doi: 10.3390/s21072369.
5
A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance.一种针对心理负荷、参与度和人类绩效的神经工效学方法。
Front Neurosci. 2020 Apr 7;14:268. doi: 10.3389/fnins.2020.00268. eCollection 2020.
6
Using a structured-light 3D scanner to improve EEG source modeling with more accurate electrode positions.使用结构光 3D 扫描仪改善 EEG 源建模，以获得更精确的电极位置。
J Neurosci Methods. 2019 Oct 1;326:108378. doi: 10.1016/j.jneumeth.2019.108378. Epub 2019 Jul 31.
7
Comparing the Effects of Three Cognitive Tasks on Indicators of Mental Fatigue.比较三种认知任务对心理疲劳指标的影响。
J Psychol. 2019;153(8):759-783. doi: 10.1080/00223980.2019.1611530. Epub 2019 Jun 12.
8
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website.ICLabel：一种自动化的脑电图独立成分分类器、数据集和网站。
Neuroimage. 2019 Sep;198:181-197. doi: 10.1016/j.neuroimage.2019.05.026. Epub 2019 May 16.
9
PC-PVT 2.0: An updated platform for psychomotor vigilance task testing, analysis, prediction, and visualization.PC-PVT 2.0：一款用于精神运动警戒任务测试、分析、预测和可视化的更新平台。
J Neurosci Methods. 2018 Jul 1;304:39-45. doi: 10.1016/j.jneumeth.2018.04.007. Epub 2018 Apr 19.
10
Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset.在认知任务中同时采集 EEG 和 NIRS 的开放获取数据集。
Sci Data. 2018 Feb 13;5:180003. doi: 10.1038/sdata.2018.3.