• 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

基于稳态视觉诱发电位的脑机接口的不同反馈方法

Different Feedback Methods For An SSVEP-Based BCI.

作者信息

Benda Mihaly, Stawicki Piotr, Gembler Felix, Grichnik Roland, Rezeika Aya, Saboor Abdul, Volosyak Ivan

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1939-1943. doi: 10.1109/EMBC.2018.8512622.

DOI:10.1109/EMBC.2018.8512622
PMID:30440778
Abstract

In this paper we examined different ways to inform the user of the classification progress in our online SSVEPbased BCI speller. Different user feedback was given based on the distance from the classification threshold, separately calculated for each stimulus. We focused on the comparison of the accuracies and spelling times associated with each different feedback type. We tested eight different methods, one without feedback for comparison, and the two paradigms each (an increase and a decrease), for three varying parameters, during an online spelling task. The eighth method was a combination of the best performing feedback modalities. A 28 target speller was used for spelling the same word with different feedback methods. The level of comfort was assessed by the seven healthy participants, using a questionnaire. We found substantial decreases in spelling times; they were reduced to 12-77% of the no-feedback condition spelling times, for each of our subjects, with at least one of the parameters. However, this parameter, as expected, was different for each user. According to the personal fastest feedback methods, a combination of them was also used for spelling. These combined feedback methods usually resulted in a slower spelling than the individual best feedback, but still faster than without any feedback. Overall, the average spelling times with the different feedback methods were: no feedback, 95.09 s, increasing size, 62.94 s, decreasing size, 87.73 s, increasing contrast, 77.80 s, decreasing contrast, 124.37 s, increasing duty-cycle, 134.70 s, and decreasing dutycycle, 103.77 s.

摘要

在本文中,我们研究了在基于稳态视觉诱发电位(SSVEP)的在线脑机接口(BCI)拼写器中向用户告知分类进度的不同方式。根据与分类阈值的距离给出不同的用户反馈,针对每个刺激单独计算该距离。我们重点比较了与每种不同反馈类型相关的准确率和拼写时间。在在线拼写任务期间,我们测试了八种不同方法,一种无反馈方法用于比较,针对三个不同参数,每种参数各有两种模式(增加和减少)。第八种方法是表现最佳的反馈模式的组合。使用一个28个目标的拼写器,用不同反馈方法拼写同一个单词。七名健康参与者通过问卷调查评估舒适度。我们发现拼写时间大幅减少;对于我们的每个受试者,在至少一个参数的情况下,拼写时间减少到无反馈条件下拼写时间的12% - 77%。然而,正如预期的那样,每个用户的这个参数各不相同。根据个人最快的反馈方法,也将它们组合起来用于拼写。这些组合反馈方法通常导致拼写速度比单个最佳反馈慢,但仍比无任何反馈时快。总体而言,不同反馈方法的平均拼写时间分别为:无反馈,95.09秒;增大尺寸,62.94秒;减小尺寸,87.73秒;增大对比度,77.80秒;减小对比度,124.37秒;增大占空比,134.70秒;减小占空比,103.77秒。

相似文献

1
Different Feedback Methods For An SSVEP-Based BCI.基于稳态视觉诱发电位的脑机接口的不同反馈方法
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1939-1943. doi: 10.1109/EMBC.2018.8512622.
2
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.
3
A High Performance Spelling System based on EEG-EOG Signals With Visual Feedback.基于 EEG-EOG 信号的具有视觉反馈的高性能拼写系统。
IEEE Trans Neural Syst Rehabil Eng. 2018 Jul;26(7):1443-1459. doi: 10.1109/TNSRE.2018.2839116.
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
A Dynamically Optimized SSVEP Brain-Computer Interface (BCI) Speller.一种动态优化的稳态视觉诱发电位脑机接口(BCI)拼写器。
IEEE Trans Biomed Eng. 2015 Jun;62(6):1447-56. doi: 10.1109/TBME.2014.2320948. Epub 2014 Apr 29.
6
DTU BCI speller: an SSVEP-based spelling system with dictionary support.
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:2212-5. doi: 10.1109/EMBC.2013.6609975.
7
Learning to control an SSVEP-based BCI speller in naïve subjects.让新手学会控制基于稳态视觉诱发电位的脑机接口拼写器。
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:1934-1937. doi: 10.1109/EMBC.2017.8037227.
8
Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface.用于实现基于稳态视觉诱发电位的高速脑机接口的滤波器组典型相关分析。
J Neural Eng. 2015 Aug;12(4):046008. doi: 10.1088/1741-2560/12/4/046008. Epub 2015 Jun 2.
9
A speedy hybrid BCI spelling approach combining P300 and SSVEP.一种结合 P300 和 SSVEP 的快速混合脑机接口拼写方法。
IEEE Trans Biomed Eng. 2014 Feb;61(2):473-83. doi: 10.1109/TBME.2013.2281976.
10
A visual parallel-BCI speller based on the time-frequency coding strategy.一种基于时频编码策略的视觉并行脑机接口拼写器。
J Neural Eng. 2014 Apr;11(2):026014. doi: 10.1088/1741-2560/11/2/026014. Epub 2014 Mar 10.

引用本文的文献

1
An Open Source-Based BCI Application for Virtual World Tour and Its Usability Evaluation.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.
2
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.
3
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.
4
Peak Detection with Online Electroencephalography (EEG) Artifact Removal for Brain-Computer Interface (BCI) Purposes.用于脑机接口(BCI)目的的在线脑电图(EEG)伪迹去除的峰值检测
Brain Sci. 2019 Nov 29;9(12):347. doi: 10.3390/brainsci9120347.