文献类型：期刊文献

英文题名：Whether generic model works for rapid ERP-based BCI calibration

作者：Jin, Jing[1];Sellers, Eric W.[2];Zhang, Yu[1,3];Daly, Ian[4];Wang, Xingyu[1];Cichocki, Andrzej[3]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]E Tennessee State Univ, Dept Psychol, Brain Comp Interface Lab, Johnson City, TN 37614 USA;[3]RIKEN, Brain Sci Inst, Lab Adv Brain Signal Proc, Wako, Saitama 3510198, Japan;[4]Graz Univ Technol, Inst Knowledge Discovery, Lab Brain Comp Interfaces, A-8010 Graz, Austria

年份：2013

卷号：212

期号：1

起止页码：94

外文期刊名：JOURNAL OF NEUROSCIENCE METHODS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000313390600010)】；

基金：This work was supported in part by the Grant National Natural Science Foundation of China, under Grant Nos. 61074113 and 61203127 and supported part by Shanghai Leading Academic Discipline Project, Project Number: B504, NIBIB & NINDS, NIH (EB00856), NIDCD, NIH (1 R21 DC010470-01), NIDCD, NIH (1 R15 DC011002-01), and Fundamental Research Funds for the Central Universities (WH1114038).

语种：英文

外文关键词：Brain computer interface; P300; Online training; Generic model

摘要：Event-related potential (ERP)-based brain-computer interfacing (BCI) is an effective method of basic communication. However, collecting calibration data, and classifier training, detracts from the amount of time allocated for online communication. Decreasing calibration time can reduce preparation time thereby allowing for additional online use, potentially lower fatigue, and improved performance. Previous studies, using generic online training models which avoid offline calibration, afford more time for online spelling. Such studies have not examined the direct effects of the model on individual performance, and the training sequence exceeded the time reported here. The first goal of this work is to survey whether one generic model works for all subjects and the second goal is to show the performance of a generic model using an online training strategy when participants could use the generic model. The generic model was derived from 10 participant's data. An additional 11 participants were recruited for the current study. Seven of the participants were able to use the generic model during online training. Moreover, the generic model performed as well as models obtained from participant specific offline data with a mean training time of less than 2 min. However, four of the participants could not use this generic model, which shows that one generic mode is not generic for all subjects. More research on ERPs of subjects with different characteristics should be done, which would tie helpful to build generic models for subject groups. This result shows a potential valuable direction for improving the BCI system. (C) 2012 Elsevier B.V. All rights reserved.