文献类型：期刊文献

英文题名：AN ERP-BASED BCI USING AN ODDBALL PARADIGM WITH DIFFERENT FACES AND REDUCED ERRORS IN CRITICAL FUNCTIONS

作者：Jin, Jing[1];Allison, Brendan Z.[2];Zhang, Yu[1];Wang, Xingyu[1];Cichocki, Andrzej[3,4]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Univ Calif San Diego, Dept Cognit Sci, Cognit Neurosci Lab, La Jolla, CA 92093 USA;[3]RIKEN, Brain Sci Inst, Lab Adv Brain Signal Proc, Wako, Saitama 3510198, Japan;[4]Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland

年份：2014

卷号：24

期号：8

外文期刊名：INTERNATIONAL JOURNAL OF NEURAL SYSTEMS

收录：;EI(收录号：20144600180158);WOS:【SCI-EXPANDED(收录号:WOS:000345514800002)】；

基金：This work was supported in part by the Grant National Natural Science Foundation of China, under Grant Numbers 61203127, 61105122, 61201124 and 61305028 and supported in part by Shanghai Leading Academic Discipline Project, Project Number: B504. This work was also supported by the Fundamental Research Funds for the Central Universities (WG1414005, WH1314023). We wish to thank anonymous reviewers for their helpful suggestions.

语种：英文

外文关键词：Brain computer interface; event-related potentials; multi-faces

摘要：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.