文献类型：期刊文献

英文题名：The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain-Computer Interface

作者：Jin, Jing[1];Li, Shurui[1];Daly, Ian[2];Miao, Yangyang[1];Liu, Chang[1];Wang, Xingyu[1];Cichocki, Andrzej[3,4,5]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Univ Essex, Sch Comp Sci & Elect Engn, Brain Comp Interfacing & Neural Engn Lab, Colchester CO4 3SQ, Essex, England;[3]Skolkovo Inst Sci & Technol SKOLTECH, Moscow 143026, Russia;[4]RIKEN, Brain Sci Inst, Wako, Saitama 3510198, Japan;[5]Nicolaus Copernicus Univ UMK, PL-87100 Torun, Poland

年份：2020

卷号：28

期号：1

起止页码：3

外文期刊名：IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING

收录：;EI(收录号：20200508091795);WOS:【SCI-EXPANDED(收录号:WOS:000508375400001)】；

基金：This work was supported in part by the National Key Research and Development Program under Grant 2017YFB13003002, in part by the Grant National Natural Science Foundation of China under Grant 61573142, Grant 61773164, and Grant 91420302, in part by the Programme of Introducing Talents of Discipline to Universities through the 111 Project under Grant B17017, and in part by the ShuGuang Project by Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant 19SG25.

语种：英文

外文关键词：P300 speller; brain computer interface; WLDA; generic model set; matching method; online training strategy

摘要：P300-based brain-computer interfaces (BCIs) provide an additional communication channel for individuals with communication disabilities. In general, P300-based BCIs need to be trained, offline, for a considerable period of time, which causes users to become fatigued. This reduces the efficiency and performance of the system. In order to shorten calibration time and improve system performance, we introduce the concept of a generic model set. We used ERP data from 116 participants to train the generic model set. The resulting set consists of ten models, which are trained by weighted linear discriminant analysis (WLDA). Twelve new participants were then invited to test the validity of the generic model set. The results demonstrated that all new participants matched the best generic model. The resulting mean classification accuracy equaled 80% after online training, an accuracy that was broadly equivalent to the typical training model method. Moreover, the calibration time was shortened by 70.7% of the calibration time of the typical model method. In other words, the best matching model method only took 81s to calibrate, while the typical model method took 276s. There were also significant differences in both accuracy and raw bit rate between the best and the worst matching model methods. We conclude that the strategy of combining the generic models with online training is easily accepted and achieves higher levels of user satisfaction (as measured by subjective reports). Thus, we provide a valuable new strategy for improving the performance of P300-based BCI.