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Code-modulated evoked potentials for control of Brain-Computer Interfaces

Code-modulated evoked potentials for control of Brain-Computer Interfaces
用于控制脑机接口的代码调制诱发电位
批准号:
274840190
负责人:
Dr. Martin Spüler
金额:
$0.0万
依托单位:
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
2015
资助国家:
德国
项目状态:
已结题
起止时间:
2014-12-31 至 2018-12-31

项目摘要

项目成果

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中文摘要
翻译
脑机接口(BCI)允许仅通过大脑活动进行通信或控制计算机。这项技术的目的是使完全瘫痪的人能够与他们的环境进行交流和互动。前人的工作表明,利用伪随机码调制的视觉诱发电位可以用于脑-机接口控制。基于这些编码调制的视觉诱发电位(c-VEP),申请人开发了一种BCI系统,该系统目前在非侵入性BCI系统领域提供最高的通信速度。然而,该系统在完全瘫痪患者中的应用尚不可能,本项目的目标是对现有的c-VEP脑机接口系统进行扩展,以增加准确性和改善易用性。此外,申请人希望使c-VEP可供完全瘫痪的患者使用。由于后者特别重要,应采用不同的方法。首先,视觉刺激应适应于不需要眼球运动的情况下使用,利用编码调制刺激诱发电位的方法应转变为听觉刺激,使BCI系统适用于盲人或视力受损的人。因此,用于信号处理的方法也需要改进。此外,应该使用无监督的机器学习方法来校准BCI。这是一种全新的方法,应该也能使BCI应用于完全闭锁综合征患者,现有的所有BCI系统对他们都不起作用。在项目结束时,应与外部合作伙伴合作,对用户友好的BCI系统进行测试,使其完全瘫痪。
英文摘要
A Brain-Computer Interface (BCI) allows to communicate or to control a computer by means of brain activity only. The aim of this technology is to enable completely paralyzed persons to communicate and interact with their environment. In previous works, it was shown that visual evoked potentials that are modulated by a pseudorandom code can be utilized for BCI control. Based on these code-modulated visual evoked potentials (c-VEPs), the applicant has developed a BCI system, which currently provides the highest communication speeds in the area of non-invasive BCI systems. However, the application of this system for completely paralyzed patients is not possible yet.The goal of this project is the extension of the current c-VEP BCI system to increase the accuracy and improve the usability. Further, the applicant wants to make the c-VEP useable by patients who are completely paralyzed. Since the latter is of special importance, different approaches shall be used. First, the visual stimulation should be adapted to be useable without eye movement and the method using code-modulated Stimuli to evoked potentials shall be transferred to auditory stimulation to make the BCI system useable by people who are blind or have impaired vision. Therefore, also the methods used for signal processing need to be improved. Further, unsupervised machine learning methods should be used for calibration of the BCI. This is a completely novel approach and should enable the use of BCIs also for patients with complete locked-in syndrome, for whom all existing BCI systems does not work. At the end of the project, the user-friendly BCI system should be tested with completely paralyzed in cooperation with external partners.
期刊论文(7)
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科研奖励(0)
会议论文
DOI: 10.1080/2326263x.2017.1415496
发表时间: 2018-01
期刊:
影响因子: --
作者: [M. Spüler;Simone Kurek]
通讯作者: M. Spüler;Simone Kurek
DOI: 10.1101/546986
发表时间: 2019-02
期刊: PLoS ONE
影响因子: 3.7
作者: [S. Nagel;M. Spüler]
通讯作者: S. Nagel;M. Spüler
DOI: 10.1038/s41598-019-44645-x
发表时间: 2019-06-04
期刊: SCIENTIFIC REPORTS
影响因子: 4.6
作者: [Nagel, Sebastian, Spueler, Martin]
通讯作者: Spueler, Martin
海外基金