Wilhelm-Schickard-Institute for Computer Science, University of Tübingen, Tübingen, Germany.
PLoS One. 2012;7(12):e51077. doi: 10.1371/journal.pone.0051077. Epub 2012 Dec 7.
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.
脑机接口（BCI）的目标是以纯粹的大脑活动来控制计算机。最近，基于编码调制视觉诱发电位（c-VEPs）的 BCI 已经显示出建立高性能通信的巨大潜力。在本文中，我们提出了一种使用在线分类器自适应来减少校准时间并提高性能的 c-VEP BCI。我们比较了两种不同的在线系统自适应方法：一种是无监督的方法，另一种是使用错误相关电位检测的方法。这两种方法都在在线研究中进行了测试，其中基于错误相关电位的自适应方法达到了 96%的平均准确率。这一准确率对应于 144 位/分钟的平均信息传输率，这是迄今为止报道的最高非侵入式 BCI 比特率。在自由拼写模式下，受试者平均每分钟能无错误地书写 21.3 个字母，这表明 BCI 系统在正常使用场景中的可行性。此外，我们还表明，仅基于错误相关电位的检测就可以校准 BCI 系统，而无需知道真实的类别标签。
