Volosyak Ivan, Guger Christoph, Graser Axel
Institute of Automation (IAT), University of Bremen, 28359, Germany.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4201-4. doi: 10.1109/IEMBS.2010.5627390.
Modern brain-computer interface (BCI) systems use different types of neural activity for control. Most BCI systems only allow the customization of very few parameters and focus only on one type of BCI approach. Many articles reported that a certain BCI did not work for some users (so called BCI illiteracy). We are introducing the BCI wizard as a system that automatically identifies key parameters to customize the best BCI paradigm for each user. With a BCI wizard it is possible to develop an interface that relies on the best mental strategy for each user and therefore makes the difference between an ineffective system and a working BCI. This work presents a preliminary study that aims to develop a BCI wizard exploring the two most effective BCI approaches (SSVEP and P300). These types of non-invasive BCIs were tested and evaluated in a group of 14 healthy subjects. During online tests all subjects were asked to spell three words with two spelling applications and at the end of the experiment they chose their preferred approach. Results showed that all subjects could communicate with the P300-based BCI with an accuracy above 69% (5 reached 100% accuracy), 10 out of 14 subjects could effectively use the SSVEP-based BCI (2 reached 100% accuracy). These promising results confirm that BCI wizard will enable BCIs customized to each user with considerably greater flexibility and independence than present systems allow.
现代脑机接口(BCI)系统使用不同类型的神经活动进行控制。大多数BCI系统只允许对极少数参数进行定制,并且只专注于一种BCI方法。许多文章报道,某种BCI对一些用户不起作用(即所谓的BCI文盲现象)。我们正在引入BCI向导作为一种系统,它能自动识别关键参数,为每个用户定制最佳的BCI范式。借助BCI向导,可以开发一种依赖于每个用户最佳心理策略的接口,从而在无效系统和可用的BCI之间产生差异。这项工作提出了一项初步研究,旨在开发一个探索两种最有效的BCI方法(稳态视觉诱发电位,SSVEP和P300)的BCI向导。在14名健康受试者组成的一组人群中对这些类型的非侵入性BCI进行了测试和评估。在在线测试期间,要求所有受试者使用两种拼写应用程序拼出三个单词,并且在实验结束时他们选择了自己喜欢的方法。结果表明,所有受试者使用基于P300的BCI进行通信的准确率均高于69%(5人达到100%的准确率),14名受试者中有10人能够有效地使用基于SSVEP的BCI(2人达到100%的准确率)。这些令人鼓舞的结果证实,BCI向导将能够以比现有系统更高的灵活性和独立性为每个用户定制BCI。