• 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

MP：一个基于多种范式的稳态视觉诱发电位数据集。

MP: A steady-state visual evoked potential dataset based on multiple paradigms.

作者信息

Zhao Xi, Xu Shencheng, Geng Kexing, Zhou Ting, Xu Tianheng, Wang Zhenyu, Feng Shilun, Hu Honglin

机构信息

School of Microelectronics, Shanghai University, Shanghai 200444, China.

Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.

出版信息

iScience. 2024 Sep 25;27(11):111030. doi: 10.1016/j.isci.2024.111030. eCollection 2024 Nov 15.

DOI:10.1016/j.isci.2024.111030
PMID:39759080
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11700636/
Abstract

In the field of steady-state visual evoked potential (SSVEP), stimulus paradigms are regularly arranged or mimic the style of a keyboard with the same size. However, stimulation paradigms have important effects on the performance of SSVEP systems, which correlate with the electroencephalogram (EEG) signal amplitude and recognition accuracy. This paper provides MP dataset that was acquired using a 12-target BCI speller. MP dataset contains 9-channel EEG signals from the occipital region of 24 subjects under 5 stimulation paradigms with different stimulus sizes and arrangements. Stimuli were encoded using joint frequency and phase modulation (JFPM) method. Subjects completed an offline prompted spelling task using a speller under 5 paradigms. Each experiment contains 8 blocks, and each block contains 12 trials. Designers can use this dataset to test the performance of algorithms considering "stimulus size" and "stimulus arrangement". EEG data showed SSVEP features through amplitude-frequency analysis. FBCCA and TRCA confirmed its suitability.

摘要

在稳态视觉诱发电位（SSVEP）领域，刺激范式通常是规则排列的，或者模仿大小相同的键盘样式。然而，刺激范式对SSVEP系统的性能有重要影响，这与脑电图（EEG）信号幅度和识别准确率相关。本文提供了使用12目标脑机接口拼写器采集的MP数据集。MP数据集包含来自24名受试者枕叶区域的9通道EEG信号，这些信号是在5种具有不同刺激大小和排列的刺激范式下采集的。刺激采用联合频率和相位调制（JFPM）方法进行编码。受试者在5种范式下使用拼写器完成了一项离线提示拼写任务。每个实验包含8个块，每个块包含12次试验。设计者可以使用该数据集来测试考虑“刺激大小”和“刺激排列”的算法性能。通过幅度频率分析，EEG数据显示出SSVEP特征。FBCCA和TRCA证实了其适用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/06ce0cd38f5f/gr16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/cfefb2526bd4/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/c4139f0b1cbb/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/36874299132e/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/f1e9c2fff244/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/b9c7eba4320e/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/bf7f306ff745/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/92b00d33ea98/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/09a8309cf334/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/1d4f5b2bca8b/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/e66d696203ce/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/e197b8ccac06/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/7730762ae9f8/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/9b5a33a65768/gr12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/314584d77650/gr13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/3b9b8c556f11/gr14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/66174488b047/gr15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/06ce0cd38f5f/gr16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/cfefb2526bd4/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/c4139f0b1cbb/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/36874299132e/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/f1e9c2fff244/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/b9c7eba4320e/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/bf7f306ff745/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/92b00d33ea98/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/09a8309cf334/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/1d4f5b2bca8b/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/e66d696203ce/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/e197b8ccac06/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/7730762ae9f8/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/9b5a33a65768/gr12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/314584d77650/gr13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/3b9b8c556f11/gr14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/66174488b047/gr15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82a9/11700636/06ce0cd38f5f/gr16.jpg

相似文献

1
MP: A steady-state visual evoked potential dataset based on multiple paradigms.MP：一个基于多种范式的稳态视觉诱发电位数据集。
iScience. 2024 Sep 25;27(11):111030. doi: 10.1016/j.isci.2024.111030. eCollection 2024 Nov 15.
2
A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces.基于 SSVEP 的脑-机接口基准数据集。
IEEE Trans Neural Syst Rehabil Eng. 2017 Oct;25(10):1746-1752. doi: 10.1109/TNSRE.2016.2627556. Epub 2016 Nov 10.
3
Spectrum-Enhanced TRCA (SE-TRCA): A novel approach for direction detection in SSVEP-based BCI.谱增强时频响应分析（SE-TRCA）：一种基于 SSVEP 的脑机接口中方向检测的新方法。
Comput Biol Med. 2023 Nov;166:107488. doi: 10.1016/j.compbiomed.2023.107488. Epub 2023 Sep 18.
4
A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature.一种结合 P300 电位和 SSVEP 阻断特征的混合 BCI 拼写范式。
J Neural Eng. 2013 Apr;10(2):026001. doi: 10.1088/1741-2560/10/2/026001. Epub 2013 Jan 31.
5
An open dataset for human SSVEPs in the frequency range of 1-60 Hz.用于 1-60Hz 频率范围内人类 SSVEP 的公开数据集。
Sci Data. 2024 Feb 13;11(1):196. doi: 10.1038/s41597-024-03023-7.
6
Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis.利用任务相关成分分析提高高速脑拼写器 SSVEP 的检测。
IEEE Trans Biomed Eng. 2018 Jan;65(1):104-112. doi: 10.1109/TBME.2017.2694818. Epub 2017 Apr 19.
7
Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance.基于稳态视觉诱发电位的脑机接口拼写器：聚焦刺激范式与性能的综述
Brain Sci. 2021 Apr 1;11(4):450. doi: 10.3390/brainsci11040450.
8
Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis.基于两步任务相关成分分析的 SSVEP 脑-机接口增强。
Sensors (Basel). 2021 Feb 12;21(4):1315. doi: 10.3390/s21041315.
9
A flexible speller based on time-space frequency conversion SSVEP stimulation paradigm under dry electrode.一种基于时空频率转换稳态视觉诱发电位刺激范式的干电极柔性拼写器。
Front Comput Neurosci. 2023 Feb 1;17:1101726. doi: 10.3389/fncom.2023.1101726. eCollection 2023.
10
Implementing a calibration-free SSVEP-based BCI system with 160 targets.实现一个无校准的基于 SSVEP 的具有 160 个目标的脑机接口系统。
J Neural Eng. 2021 Jul 2;18(4). doi: 10.1088/1741-2552/ac0bfa.

本文引用的文献

1
An open dataset for human SSVEPs in the frequency range of 1-60 Hz.用于 1-60Hz 频率范围内人类 SSVEP 的公开数据集。
Sci Data. 2024 Feb 13;11(1):196. doi: 10.1038/s41597-024-03023-7.
2
Multi-frequency steady-state visual evoked potential dataset.多频稳态视觉诱发电位数据集。
Sci Data. 2024 Jan 4;11(1):26. doi: 10.1038/s41597-023-02841-5.
3
Brain-Computer Interface: Advancement and Challenges.脑机接口：进展与挑战。
Sensors (Basel). 2021 Aug 26;21(17):5746. doi: 10.3390/s21175746.
4
Implementing a calibration-free SSVEP-based BCI system with 160 targets.实现一个无校准的基于 SSVEP 的具有 160 个目标的脑机接口系统。
J Neural Eng. 2021 Jul 2;18(4). doi: 10.1088/1741-2552/ac0bfa.
5
Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance.基于稳态视觉诱发电位的脑机接口拼写器：聚焦刺激范式与性能的综述
Brain Sci. 2021 Apr 1;11(4):450. doi: 10.3390/brainsci11040450.
6
An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces.基于可穿戴 SSVEP 的脑机接口的公开数据集。
Sensors (Basel). 2021 Feb 10;21(4):1256. doi: 10.3390/s21041256.
7
BETA: A Large Benchmark Database Toward SSVEP-BCI Application.BETA：一个面向稳态视觉诱发电位脑机接口应用的大型基准数据库。
Front Neurosci. 2020 Jun 23;14:627. doi: 10.3389/fnins.2020.00627. eCollection 2020.
8
Comparison of Different Visual Feedback Methods for SSVEP-Based BCIs.基于稳态视觉诱发电位的脑机接口中不同视觉反馈方法的比较
Brain Sci. 2020 Apr 18;10(4):240. doi: 10.3390/brainsci10040240.
9
A multi-day and multi-band dataset for a steady-state visual-evoked potential-based brain-computer interface.用于稳态视觉诱发电位脑机接口的多天多波段数据集。
Gigascience. 2019 Nov 1;8(11). doi: 10.1093/gigascience/giz133.
10
Facilitating Calibration in High-Speed BCI Spellers via Leveraging Cross-Device Shared Latent Responses.通过利用跨设备共享潜在反应，促进高速脑机接口拼写器的校准。
IEEE Trans Biomed Eng. 2020 Apr;67(4):1105-1113. doi: 10.1109/TBME.2019.2929745. Epub 2019 Jul 18.