文献类型：期刊文献

英文题名：Enhancing P300 based character recognition performance using a combination of ensemble classifiers and a fuzzy fusion method

作者：Li, Shurui[1];Jin, Jing[1];Daly, Ian[2];Wang, Xingyu[1];Lam, Hak-Keung[3];Cichocki, Andrzej[4,5,6]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai, Peoples R China;[2]Univ Essex, Sch Comp Sci & Elect Engn, Brain Comp Interfacing & Neural Engn Lab, Colchester CO4 3SQ, Essex, England;[3]Kings Coll London, Dept Engn, London WC2R 2LS, England;[4]Skolkovo Inst Sci & Technol SKOLTECH, Moscow 143026, Russia;[5]Syst Res Inst PAS, Warsaw, Poland;[6]Nicolaus Copernicus Univ UMK, Torun, Poland

年份：2021

卷号：362

外文期刊名：JOURNAL OF NEUROSCIENCE METHODS

收录：;WOS:【SSCI(收录号:WOS:000688451700004)，SCI-EXPANDED(收录号:WOS:000688451700004)】；

基金：This work was supported by the National Key Research and Devel-opment Program 2017YFB13003002. This work was also supported in part by the Grant National Natural Science Foundation of China, under Grant Nos. 61573142, 61773164, the Programme of Introducing Talentsof Discipline to Universities (the 111 Project) under Grant B17017, and the "ShuGuang" project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant 19SG25.

语种：英文

外文关键词：Brain-computer interface; P300 speller; Ensemble classifiers; Fuzzy fusion

摘要：Background: P300-based brain-computer interfaces provide communication pathways without the need for muscle activity by recognizing electrical signals from the brain. The P300 speller is one of the most commonly used BCI applications, as it is very simple and reliable, and it is capable of reaching satisfactory communication performance. However, as with other BCIs, it remains a challenge to improve the P300 speller's performance to increase its practical usability. New methods: In this study, we propose a novel multi-feature subset fuzzy fusion (MSFF) framework for the P300 speller to recognize the users' spelling intention. This method includes two parts: 1) feature selection by the Lasso algorithm and feature division; 2) the construction of ensemble LDA classifiers and the fuzzy fusion of those classifiers to recognize user intention. Results: The proposed framework is evaluated in three public datasets and achieves an average accuracy of 100% after 4 epochs for BCI Competition II Dataset IIb, 96% for BCI Competition III dataset II and 98.3% for the BNCI Horizon Dataset. It indicates that the proposed MSFF method can make use of temporal information of signals and helps to enhance classification performance. Comparison with existing methods: The proposed MSFF method yields better or comparable performance than previously reported machine learning algorithms. Conclusions: The proposed MSFF method is able to improve the performance of P300-based BCIs.