Lu Zhaohua, Li Qi, Gao Ning, Yang Jingjing
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China.
Front Comput Neurosci. 2020 Jan 15;13:93. doi: 10.3389/fncom.2019.00093. eCollection 2019.
Previous studies have shown that the performance of the famous face P300-speller was better than that of the classical row/column flashing P300-speller. Furthermore, in some studies, the brain was more active when responding to one's own face than to a famous face, and a self-face stimulus elicited larger amplitude event-related potentials (ERPs) than did a famous face. Thus, we aimed to study the role of the self-face paradigm on further improving the performance of the P300-speller system with the famous face P300-speller paradigm as the control paradigm. We designed two facial P300-speller paradigms based on the self-face and a famous face (Ming Yao, a sports star; the famous face spelling paradigm) with a neutral expression. ERP amplitudes were significantly greater in the self-face than in the famous face spelling paradigm at the parietal area from 340 to 480 ms (P300), from 480 to 600 ms (P600f), and at the fronto-central area from 700 to 800 ms. Offline and online classification results showed that the self-face spelling paradigm accuracies were significantly higher than those of the famous face spelling paradigm at superposing first two times ( < 0.05). Similar results were found for information transfer rates ( < 0.05). The self-face spelling paradigm significantly improved the performance of the P300-speller system. This has significant practical applications for brain-computer interfaces (BCIs) and could avoid infringement issues caused by using images of other people's faces.
先前的研究表明，著名面孔P300拼写器的性能优于经典的行/列闪烁P300拼写器。此外，在一些研究中，大脑对自己的面孔做出反应时比面对著名面孔时更活跃，并且自我面孔刺激引发的事件相关电位（ERP）幅度比著名面孔更大。因此，我们旨在以著名面孔P300拼写器范式作为对照范式，研究自我面孔范式对进一步提高P300拼写器系统性能的作用。我们基于自我面孔和一张著名面孔（体育明星姚明；著名面孔拼写范式）设计了两种面部P300拼写器范式，面部表情均为中性。在顶叶区域，从340至480毫秒（P300）、从480至600毫秒（P600f）以及额中央区域从700至800毫秒时，自我面孔条件下的ERP波幅显著大于著名面孔拼写范式。离线和在线分类结果显示，在前两次叠加时，自我面孔拼写范式的准确率显著高于著名面孔拼写范式（<0.05）。信息传输率也有类似结果（<0.05）。自我面孔拼写范式显著提高了P300拼写器系统的性能。这对脑机接口（BCI）具有重要的实际应用价值，并且可以避免因使用他人面部图像而引发的侵权问题。
