Zhou Sijie, Jin Jing, Daly Ian, 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 Embodiment Lab, School of Systems Engineering, University of Reading Reading, UK.
Front Neurosci. 2016 Oct 7;10:444. doi: 10.3389/fnins.2016.00444. eCollection 2016.
Many recent studies have focused on improving the performance of event-related potential (ERP) based brain computer interfaces (BCIs). The use of a face pattern has been shown to obtain high classification accuracies and information transfer rates (ITRs) by evoking discriminative ERPs (N200 and N400) in addition to P300 potentials. Recently, it has been proved that the performance of traditional P300-based BCIs could be improved through a modification of the mismatch pattern. In this paper, a mismatch inverted face pattern (MIF-pattern) was presented to improve the performance of the inverted face pattern (IF-pattern), one of the state of the art patterns used in visual-based BCI systems. Ten subjects attended in this experiment. The result showed that the mismatch inverted face pattern could evoke significantly larger vertex positive potentials ( < 0.05) and N400s ( < 0.05) compared to the inverted face pattern. The classification accuracy (mean accuracy is 99.58%) and ITRs (mean bit rate is 27.88 bit/min) of the mismatch inverted face pattern was significantly higher than that of the inverted face pattern ( < 0.05).
最近许多研究都集中在提高基于事件相关电位（ERP）的脑机接口（BCI）的性能上。研究表明，除了P300电位外，使用面部图案通过诱发可区分的ERP（N200和N400）能够获得较高的分类准确率和信息传输率（ITR）。最近，已经证明通过修改失配图案可以提高传统基于P300的BCI的性能。在本文中，提出了一种失配倒置面部图案（MIF图案），以提高倒置面部图案（IF图案）的性能，IF图案是基于视觉的BCI系统中使用的最先进图案之一。十名受试者参加了该实验。结果表明，与倒置面部图案相比，失配倒置面部图案能够诱发显著更大的头顶正电位（<0.05）和N400（<0.05）。失配倒置面部图案的分类准确率（平均准确率为99.58%）和ITR（平均比特率为27.88比特/分钟）显著高于倒置面部图案（<0.05）。
