• 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

在线脑机接口中信息传递率估计的现有问题研究。

A study of the existing problems of estimating the information transfer rate in online brain-computer interfaces.

机构信息

Department of Biomedical Engineering, Tsinghua University, Beijing, People's Republic of China.

出版信息

J Neural Eng. 2013 Apr;10(2):026014. doi: 10.1088/1741-2560/10/2/026014. Epub 2013 Feb 28.

DOI:10.1088/1741-2560/10/2/026014
PMID:23448963
Abstract

OBJECTIVE

Today, the brain-computer interface (BCI) community lacks a standard method to evaluate an online BCI's performance. Even the most commonly used metric, the information transfer rate (ITR), is often reported differently, even incorrectly, in many papers, which is not conducive to BCI research. This paper aims to point out many of the existing problems and give some suggestions and methods to overcome these problems.

APPROACH

First, the preconditions inherent in ITR calculation based on Wolpaw's definition are summarized and several incorrect ITR calculations, which go against the preconditions, are indicated. Then, the issues affecting ITR estimation during the test of online BCI systems are discussed in detail. Finally, a task-oriented online BCI test platform was proposed, which may help BCI evaluations in real-world applications.

MAIN RESULTS

The guidelines for ITR calculation in online BCIs testing are proposed. The platform executed in the Beijing BCI Competition 2010 shows that it can be used as a common way to compare the online performances (including the ITR) of existing BCI paradigms.

SIGNIFICANCE

The proposed guidelines and task-oriented test platform may reduce the uncertainty and artifacts of online BCI performance evaluation; they provide a relatively objective way to compare different BCI's performances in real-world BCI applications, which is a forward step toward developing standards for BCI performance evaluation.

摘要

目的

目前,脑机接口(BCI)领域缺乏评估在线 BCI 性能的标准方法。即使是最常用的指标,信息传输率(ITR),在许多论文中也经常以不同的方式,甚至不正确地报告,这不利于 BCI 研究。本文旨在指出许多现有的问题,并提出一些建议和方法来克服这些问题。

方法

首先总结了基于 Wolpaw 定义的 ITR 计算所固有的前提条件,并指出了几个违反前提条件的不正确的 ITR 计算。然后,详细讨论了在线 BCI 系统测试中影响 ITR 估计的问题。最后,提出了一个面向任务的在线 BCI 测试平台,这可能有助于真实应用中的 BCI 评估。

主要结果

提出了在线 BCI 测试中 ITR 计算的指南。在北京脑机接口竞赛 2010 中执行的平台表明,它可以作为比较现有 BCI 范式的在线性能(包括 ITR)的常用方法。

意义

提出的指南和面向任务的测试平台可以减少在线 BCI 性能评估的不确定性和伪影;它们为在真实的 BCI 应用中比较不同 BCI 的性能提供了一种相对客观的方法,这是朝着开发 BCI 性能评估标准迈出的一步。

相似文献

1
A study of the existing problems of estimating the information transfer rate in online brain-computer interfaces.在线脑机接口中信息传递率估计的现有问题研究。
J Neural Eng. 2013 Apr;10(2):026014. doi: 10.1088/1741-2560/10/2/026014. Epub 2013 Feb 28.
2
A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm.一种基于 P300 范式中 SSVEP 整合的新型混合 BCI 拼写器。
J Neural Eng. 2013 Apr;10(2):026012. doi: 10.1088/1741-2560/10/2/026012. Epub 2013 Feb 21.
3
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.
4
Brain-computer interfaces using capacitive measurement of visual or auditory steady-state responses.基于视觉或听觉稳态响应的电容测量的脑-机接口。
J Neural Eng. 2013 Apr;10(2):024001. doi: 10.1088/1741-2560/10/2/024001. Epub 2013 Feb 28.
5
Influence of P300 latency jitter on event related potential-based brain-computer interface performance.P300潜伏期抖动对基于事件相关电位的脑机接口性能的影响。
J Neural Eng. 2014 Jun;11(3):035008. doi: 10.1088/1741-2560/11/3/035008. Epub 2014 May 19.
6
A cell-phone-based brain-computer interface for communication in daily life.基于手机的脑机接口,用于日常生活中的交流。
J Neural Eng. 2011 Apr;8(2):025018. doi: 10.1088/1741-2560/8/2/025018. Epub 2011 Mar 24.
7
P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier.采用移动脑电图系统的P300拼写器脑机接口:与传统放大器的比较
J Neural Eng. 2014 Jun;11(3):036008. doi: 10.1088/1741-2560/11/3/036008. Epub 2014 Apr 24.
8
Application of BCI systems in neurorehabilitation: a scoping review.脑机接口系统在神经康复中的应用:一项范围综述。
Disabil Rehabil Assist Technol. 2015;10(5):355-64. doi: 10.3109/17483107.2014.961569. Epub 2015 Jan 5.
9
Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methods.基于事件相关电位的脑机接口优化:动态停止方法的系统评价。
J Neural Eng. 2013 Jun;10(3):036025. doi: 10.1088/1741-2560/10/3/036025. Epub 2013 May 20.
10
The utility metric: a novel method to assess the overall performance of discrete brain-computer interfaces.效用指标:一种评估离散脑机接口整体性能的新方法。
IEEE Trans Neural Syst Rehabil Eng. 2010 Feb;18(1):20-8. doi: 10.1109/TNSRE.2009.2032642. Epub 2009 Sep 22.

引用本文的文献

1
A preliminary study of steady-state visually-evoked potential-based non-invasive brain-computer interface technology as a communication aid for patients with amyotrophic lateral sclerosis.基于稳态视觉诱发电位的无创脑机接口技术作为肌萎缩侧索硬化症患者交流辅助手段的初步研究。
Quant Imaging Med Surg. 2025 Apr 1;15(4):3469-3479. doi: 10.21037/qims-24-1643. Epub 2025 Mar 28.
2
Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.四肢瘫痪患者通过神经网络介导的特征提取增强对脑机接口的控制。
Nat Biomed Eng. 2024 Dec 6. doi: 10.1038/s41551-024-01297-1.
3
Comprehensive evaluation methods for translating BCI into practical applications: usability, user satisfaction and usage of online BCI systems.
将脑机接口转化为实际应用的综合评估方法:在线脑机接口系统的可用性、用户满意度和使用情况。
Front Hum Neurosci. 2024 Jun 5;18:1429130. doi: 10.3389/fnhum.2024.1429130. eCollection 2024.
4
BCI-Utility Metric for Asynchronous P300 Brain-Computer Interface Systems.基于 P300 的脑-机接口系统的异步 BCI 效用度量
IEEE Trans Neural Syst Rehabil Eng. 2023;31:3968-3977. doi: 10.1109/TNSRE.2023.3322125. Epub 2023 Oct 16.
5
Combining brain-computer interfaces and multiplayer video games: an application based on c-VEPs.结合脑机接口与多人视频游戏:基于c-VEP的应用
Front Hum Neurosci. 2023 Aug 3;17:1227727. doi: 10.3389/fnhum.2023.1227727. eCollection 2023.
6
Improving user experience of SSVEP BCI through low amplitude depth and high frequency stimuli design.通过低幅度深度和高频刺激设计提高 SSVEP-BCI 的用户体验。
Sci Rep. 2022 May 25;12(1):8865. doi: 10.1038/s41598-022-12733-0.
7
Instant classification for the spatially-coded BCI.空间编码脑机接口的即时分类。
PLoS One. 2022 Apr 28;17(4):e0267548. doi: 10.1371/journal.pone.0267548. eCollection 2022.
8
A Multi-Branch Convolutional Neural Network with Squeeze-and-Excitation Attention Blocks for EEG-Based Motor Imagery Signals Classification.一种具有挤压与激励注意力模块的多分支卷积神经网络用于基于脑电图的运动想象信号分类
Diagnostics (Basel). 2022 Apr 15;12(4):995. doi: 10.3390/diagnostics12040995.
9
Information Bottleneck as Optimisation Method for SSVEP-Based BCI.基于信息瓶颈的稳态视觉诱发电位脑机接口优化方法
Front Hum Neurosci. 2021 Sep 7;15:675091. doi: 10.3389/fnhum.2021.675091. eCollection 2021.
10
Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms.基于感觉运动节律的无创脑机接口
Proc IEEE Inst Electr Electron Eng. 2015 Jun;103(6):907-925. doi: 10.1109/jproc.2015.2407272. Epub 2015 May 20.