Online adaptation of a c-VEP Brain-computer Interface(BCI) based on error-related potentials and unsupervised learning.
Online adaptation of a c-VEP Brain-computer Interface(BCI) based on error-related potentials and unsupervised learning.
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DOI：
10.1371/journal.pone.0051077
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Bogdan M
中科院分区：
文献类型：
--
作者：
Spüler M;Rosenstiel W;Bogdan M
脑机接口(BCI)的目标是通过纯粹的大脑活动来控制计算机。近年来，基于编码调制视觉诱发电位(c-VEP)的脑机接口在建立高性能通信方面显示出巨大的潜力。在本文中，我们提出了一种c-VEP脑机接口，它使用分类器的在线自适应来减少校准时间并提高性能。我们比较了两种不同的系统在线自适应方法：无监督方法和使用误差相关电位检测的方法。这两种方法都在一项在线研究中进行了测试，其中基于误差相关电位的自适应实现了96%的平均准确率。该精度对应于144比特/分钟的平均信息传输速率，这是迄今为止报告的用于非侵入性BCI的最高比特率。在自由拼写模式下，受试者平均每分钟能够写出21.3个没有错误的字母，这表明BCI系统在正常使用情景下的可行性。此外，我们还表明，在不知道真实类别标签的情况下，仅基于误差相关电位的检测来校准BCI系统是可能的。
The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and increase performance. We compare two different approaches for online adaptation of the system: an unsupervised method and a method that uses the detection of error-related potentials. Both approaches were tested in an online study, in which an average accuracy of 96% was achieved with adaptation based on error-related potentials. This accuracy corresponds to an average information transfer rate of 144 bit/min, which is the highest bitrate reported so far for a non-invasive BCI. In a free-spelling mode, the subjects were able to write with an average of 21.3 error-free letters per minute, which shows the feasibility of the BCI system in a normal-use scenario. In addition we show that a calibration of the BCI system solely based on the detection of error-related potentials is possible, without knowing the true class labels.
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