Jin Jing, Allison Brendan Z, Zhang Yu, 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 200237, China.
Int J Neural Syst. 2014 Dec;24(8):1450027. doi: 10.1142/S0129065714500270. Epub 2014 Sep 2.
Recent research has shown that a new face paradigm is superior to the conventional "flash only" approach that has dominated P300 brain-computer interfaces (BCIs) for over 20 years. However, these face paradigms did not study the repetition effects and the stability of evoked event related potentials (ERPs), which would decrease the performance of P300 BCI. In this paper, we explored whether a new "multi-faces (MF)" approach would yield more distinct ERPs than the conventional "single face (SF)" approach. To decrease the repetition effects and evoke large ERPs, we introduced a new stimulus approach called the "MF" approach, which shows different familiar faces randomly. Fifteen subjects participated in runs using this new approach and an established "SF" approach. The result showed that the MF pattern enlarged the N200 and N400 components, evoked stable P300 and N400, and yielded better BCI performance than the SF pattern. The MF pattern can evoke larger N200 and N400 components and more stable P300 and N400, which increase the classification accuracy compared to the face pattern.
最近的研究表明，一种新的面部范式优于传统的“仅闪光”方法，这种方法在超过20年的时间里一直主导着P300脑机接口（BCI）。然而，这些面部范式没有研究重复效应和诱发事件相关电位（ERP）的稳定性，这会降低P300 BCI的性能。在本文中，我们探讨了一种新的“多面孔（MF）”方法是否会比传统的“单面孔（SF）”方法产生更明显的ERP。为了减少重复效应并诱发较大的ERP，我们引入了一种新的刺激方法，称为“MF”方法，它随机显示不同的熟悉面孔。15名受试者参与了使用这种新方法和既定的“SF”方法的实验。结果表明，与SF模式相比，MF模式扩大了N200和N400成分，诱发了稳定的P300和N400，并产生了更好的BCI性能。MF模式可以诱发更大的N200和N400成分以及更稳定的P300和N400，与面部模式相比，这提高了分类准确率。
