Cheng Jiao, Jin Jing, Daly Ian, Zhang Yu, Wang Bei, Wang Xingyu, Cichocki Andrzej
Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China.
Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UK.
Biomed Tech (Berl). 2019 Feb 25;64(1):29-38. doi: 10.1515/bmt-2017-0082.
Brain-computer interface (BCI) systems can allow their users to communicate with the external world by recognizing intention directly from their brain activity without the assistance of the peripheral motor nervous system. The P300-speller is one of the most widely used visual BCI applications. In previous studies, a flip stimulus (rotating the background area of the character) that was based on apparent motion, suffered from less refractory effects. However, its performance was not improved significantly. In addition, a presentation paradigm that used a "zooming" action (changing the size of the symbol) has been shown to evoke relatively higher P300 amplitudes and obtain a better BCI performance. To extend this method of stimuli presentation within a BCI and, consequently, to improve BCI performance, we present a new paradigm combining both the flip stimulus with a zooming action. This new presentation modality allowed BCI users to focus their attention more easily. We investigated whether such an action could combine the advantages of both types of stimuli presentation to bring a significant improvement in performance compared to the conventional flip stimulus. The experimental results showed that the proposed paradigm could obtain significantly higher classification accuracies and bit rates than the conventional flip paradigm (p<0.01).
脑机接口(BCI)系统能够让用户在不借助外周运动神经系统的情况下,直接通过识别大脑活动意图与外界进行交流。P300 拼字器是应用最为广泛的视觉 BCI 之一。在以往研究中,基于表观运动的翻转刺激(旋转字符的背景区域)产生的不应期效应较小。然而,其性能并未得到显著提升。此外,一种采用“缩放”动作(改变符号大小)的呈现范式已被证明能诱发相对较高的 P300 波幅,并获得更好的 BCI 性能。为了在 BCI 中扩展这种刺激呈现方法,进而提高 BCI 性能,我们提出了一种将翻转刺激与缩放动作相结合的新范式。这种新的呈现方式使 BCI 用户能够更轻松地集中注意力。我们研究了这种动作是否能结合两种刺激呈现方式的优点,从而相比传统翻转刺激在性能上带来显著提升。实验结果表明,与传统翻转范式相比,所提出的范式能够获得显著更高的分类准确率和比特率(p<0.01)。