• 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

2020年国际脑机接口竞赛综述

2020 International brain-computer interface competition: A review.

作者信息

Jeong Ji-Hoon, Cho Jeong-Hyun, Lee Young-Eun, Lee Seo-Hyun, Shin Gi-Hwan, Kweon Young-Seok, Millán José Del R, Müller Klaus-Robert, Lee Seong-Whan

机构信息

School of Computer Science, Chungbuk National University, Cheongju, South Korea.

Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea.

出版信息

Front Hum Neurosci. 2022 Jul 22;16:898300. doi: 10.3389/fnhum.2022.898300. eCollection 2022.

DOI:10.3389/fnhum.2022.898300
PMID:35937679
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9354666/
Abstract

The brain-computer interface (BCI) has been investigated as a form of communication tool between the brain and external devices. BCIs have been extended beyond communication and control over the years. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for open access that could be used to evaluate the current degree of technical advances in BCI. Although there are a variety of remaining challenges for future BCI advances, we discuss some of more recent application directions: (i) few-shot EEG learning, (ii) micro-sleep detection (iii) imagined speech decoding, (iv) cross-session classification, and (v) EEG(+ear-EEG) detection in an ambulatory environment. Not only did scientists from the BCI field compete, but scholars with a broad variety of backgrounds and nationalities participated in the competition to address these challenges. Each dataset was prepared and separated into three data that were released to the competitors in the form of training and validation sets followed by a test set. Remarkable BCI advances were identified through the 2020 competition and indicated some trends of interest to BCI researchers.

摘要

脑机接口（BCI）已被作为大脑与外部设备之间的一种通信工具进行研究。多年来，BCI的应用范围已从通信和控制扩展到其他领域。2020年国际BCI竞赛旨在提供可公开获取的高质量神经科学数据，用于评估当前BCI技术的进步程度。尽管未来BCI的发展仍面临诸多挑战，但我们讨论一些最新的应用方向：（i）少样本脑电图学习，（ii）微睡眠检测，（iii）想象语音解码，（iv）跨时段分类，以及（v）动态环境中的脑电图（+耳脑电图）检测。不仅BCI领域的科学家参与了竞赛，来自各种背景和国籍的学者也参与其中，以应对这些挑战。每个数据集都经过准备，并被分成三个数据，以训练集和验证集的形式发布给参赛者，随后是测试集。通过2020年的竞赛发现了BCI的显著进展，并指出了一些BCI研究人员感兴趣的趋势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/a56b0e7c89b7/fnhum-16-898300-g0012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/e1f814aec642/fnhum-16-898300-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/37d705739da2/fnhum-16-898300-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/5de1a041a687/fnhum-16-898300-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/9c6b81e69e84/fnhum-16-898300-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/22c6e4e38bf7/fnhum-16-898300-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/c801cc5b478d/fnhum-16-898300-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/ea6934986314/fnhum-16-898300-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/d57e75f803cd/fnhum-16-898300-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/9637bfe01113/fnhum-16-898300-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/51d80d9f21d0/fnhum-16-898300-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/c20db56e7b95/fnhum-16-898300-g0011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/a56b0e7c89b7/fnhum-16-898300-g0012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/e1f814aec642/fnhum-16-898300-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/37d705739da2/fnhum-16-898300-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/5de1a041a687/fnhum-16-898300-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/9c6b81e69e84/fnhum-16-898300-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/22c6e4e38bf7/fnhum-16-898300-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/c801cc5b478d/fnhum-16-898300-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/ea6934986314/fnhum-16-898300-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/d57e75f803cd/fnhum-16-898300-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/9637bfe01113/fnhum-16-898300-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/51d80d9f21d0/fnhum-16-898300-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/c20db56e7b95/fnhum-16-898300-g0011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/9354666/a56b0e7c89b7/fnhum-16-898300-g0012.jpg

相似文献

1
2020 International brain-computer interface competition: A review.2020年国际脑机接口竞赛综述
Front Hum Neurosci. 2022 Jul 22;16:898300. doi: 10.3389/fnhum.2022.898300. eCollection 2022.
2
Review of the BCI Competition IV.脑机接口竞赛IV综述。
Front Neurosci. 2012 Jul 13;6:55. doi: 10.3389/fnins.2012.00055. eCollection 2012.
3
Adaptive LDA Classifier Enhances Real-Time Control of an EEG Brain-Computer Interface for Decoding Imagined Syllables.自适应线性判别分析分类器增强了用于解码想象音节的脑电图脑机接口的实时控制。
Brain Sci. 2024 Feb 21;14(3):196. doi: 10.3390/brainsci14030196.
4
Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG.机器和深度学习方法在解码想象语音 EEG 中的超参数优化评估。
Sensors (Basel). 2020 Aug 17;20(16):4629. doi: 10.3390/s20164629.
5
Decoding Covert Speech From EEG-A Comprehensive Review.从脑电图中解码隐蔽语音——全面综述
Front Neurosci. 2021 Apr 29;15:642251. doi: 10.3389/fnins.2021.642251. eCollection 2021.
6
Recent Progress in Wearable Brain-Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review.基于脑电图（EEG）的可穿戴脑机接口（BCI）设备在医学应用中的最新进展：综述
Health Data Sci. 2023 Dec 19;3:0096. doi: 10.34133/hds.0096. eCollection 2023.
7
Neural correlates of user learning during long-term BCI training for the Cybathlon competition.神经相关的用户学习在长期的脑机接口竞赛训练。
J Neuroeng Rehabil. 2022 Jul 5;19(1):69. doi: 10.1186/s12984-022-01047-x.
8
Status of deep learning for EEG-based brain-computer interface applications.基于脑电图的脑机接口应用中深度学习的现状。
Front Comput Neurosci. 2023 Jan 16;16:1006763. doi: 10.3389/fncom.2022.1006763. eCollection 2022.
9
A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm.一种使用逻辑回归分类算法对二类运动想象 EEG 信号进行分类的新框架。
PLoS One. 2023 Sep 8;18(9):e0276133. doi: 10.1371/journal.pone.0276133. eCollection 2023.
10
The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.2003年脑机接口竞赛：脑电图单次试验检测与辨别的进展及展望
IEEE Trans Biomed Eng. 2004 Jun;51(6):1044-51. doi: 10.1109/TBME.2004.826692.

引用本文的文献

1
Effect of natural and synthetic noise data augmentation on physical action classification by brain-computer interface and deep learning.自然和合成噪声数据增强对脑机接口与深度学习进行身体动作分类的影响
Front Neuroinform. 2025 Feb 27;19:1521805. doi: 10.3389/fninf.2025.1521805. eCollection 2025.
2
Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients.基于多范式和纵向训练的中风患者下肢运动想象脑电数据集
Sci Data. 2025 Feb 21;12(1):314. doi: 10.1038/s41597-025-04618-4.
3
Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.

本文引用的文献

1
IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal.IC-U-Net：一种基于 U-Net 的去噪自动编码器，使用独立成分的混合进行自动 EEG 伪影去除。
Neuroimage. 2022 Nov;263:119586. doi: 10.1016/j.neuroimage.2022.119586. Epub 2022 Aug 27.
2
Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running.带 ERP 和 SSVEP 范式的头皮和耳 EEG 的移动脑机接口数据集，用于站立、行走和跑步。
Sci Data. 2021 Dec 20;8(1):315. doi: 10.1038/s41597-021-01094-4.
3
An Open Source-Based BCI Application for Virtual World Tour and Its Usability Evaluation.
非侵入式脑机接口：现状与趋势
IEEE Rev Biomed Eng. 2025;18:26-49. doi: 10.1109/RBME.2024.3449790. Epub 2025 Jan 28.
4
Bridging the BCI illiteracy gap: a subject-to-subject semantic style transfer for EEG-based motor imagery classification.弥合脑机接口知识差距：基于脑电图的运动想象分类的受试者间语义风格迁移
Front Hum Neurosci. 2023 May 15;17:1194751. doi: 10.3389/fnhum.2023.1194751. eCollection 2023.
5
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.
6
Identifying Thematics in a Brain-Computer Interface Research.识别脑机接口研究中的主题。
Comput Intell Neurosci. 2023 Jan 4;2023:2793211. doi: 10.1155/2023/2793211. eCollection 2023.
7
An Adaptive Task-Related Component Analysis Method for SSVEP Recognition.一种用于 SSVEP 识别的自适应任务相关成分分析方法。
Sensors (Basel). 2022 Oct 11;22(20):7715. doi: 10.3390/s22207715.
一种基于开源的用于虚拟世界游览的脑机接口应用及其可用性评估。
Front Hum Neurosci. 2021 Jul 19;15:647839. doi: 10.3389/fnhum.2021.647839. eCollection 2021.
4
Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria.神经假体用于解码无言语症瘫痪患者的言语。
N Engl J Med. 2021 Jul 15;385(3):217-227. doi: 10.1056/NEJMoa2027540.
5
BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data.BENDR：使用Transformer和对比自监督学习任务从大量脑电图数据中学习。
Front Hum Neurosci. 2021 Jun 23;15:653659. doi: 10.3389/fnhum.2021.653659. eCollection 2021.
6
Improving motor imagery classification during induced motor perturbations.在诱导运动干扰下提高运动想象分类。
J Neural Eng. 2021 Jul 21;18(4). doi: 10.1088/1741-2552/ac123f.
7
A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain-Computer Interfaces.基于深度学习的脑电图脑机接口短/零校准方法综述。
Front Hum Neurosci. 2021 May 28;15:643386. doi: 10.3389/fnhum.2021.643386. eCollection 2021.
8
Benefits of deep learning classification of continuous noninvasive brain-computer interface control.深度学习分类连续非侵入式脑机接口控制的优势。
J Neural Eng. 2021 Jun 9;18(4). doi: 10.1088/1741-2552/ac0584.
9
Meta-Learning in Neural Networks: A Survey.元学习在神经网络中的研究进展综述
IEEE Trans Pattern Anal Mach Intell. 2022 Sep;44(9):5149-5169. doi: 10.1109/TPAMI.2021.3079209. Epub 2022 Aug 4.
10
Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces.视觉表象的生动性和个性对运动想象脑机接口的影响。
Front Hum Neurosci. 2021 Apr 6;15:634748. doi: 10.3389/fnhum.2021.634748. eCollection 2021.