文献类型：期刊文献

英文题名：LASSO based stimulus frequency recognition model for SSVEP BCIs

作者：Zhang, Yu[1];Jin, Jing[1];Qing, Xiangyun[1];Wang, Bei[1];Wang, Xingyu[1]

机构：[1]E China Univ Sci & Technol, Sch Informat Sci & Engn, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2012

卷号：7

期号：2

起止页码：104

外文期刊名：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

收录：;EI(收录号：20120914810289);WOS:【SCI-EXPANDED(收录号:WOS:000301759300002)】；

基金：The authors would like to thank the editor and anonymous referees for their valuable comments. They would also like to thank Dr. F.Y. Cong (University of Jyvaskyla, Finland) and Dr. B.Z. Allison (Graz University of Technology, Austria) for their advice and English checking. This study is supported by Nation Nature Science Foundation of China 61074113, Shanghai Leading Academic Discipline Project B504, and Fundamental Research Funds for the Central Universities WH0914028.

语种：英文

外文关键词：Brain-computer interfaces (BCIs); Electroencephalogram (EEG); Least absolute shrinkage and selection operator (LASSO); Steady-state visual evoked potential (SSVEP); Time window (TW)

摘要：Steady-state visual evoked potential (SSVEP) has been increasingly used for the study of brain-computer interface (BC!). How to recognize SSVEP with shorter time and lower error rate is one of the key points to develop a more efficient SSVEP-based BCI. To achieve this goal, we make use of the sparsity constraint of the least absolute shrinkage and selection operator (LASSO) for the extraction of more discriminative features of SSVEP, and then we propose a LASSO model using the linear regression between electroencephalogram (EEG) recordings and the standard square-wave signals of different frequencies to recognize SSVEP without the training stage. In this study, we verified the proposed LASSO model offline with the EEG data of nine healthy subjects in contrast to canonical correlation analysis (CCA). In the experiment, when a shorter time window was used, we found that the LASSO model yielded better performance in extracting robust and detectable features of SSVEP, and the information transfer rate obtained by the LASSO model was significantly higher than that of the CCA. Our proposed method can assist to reduce the recording time without sacrificing the classification accuracy and is promising for a high-speed SSVEP-based BCI. (C) 2011 Elsevier Ltd. All rights reserved.