| Lian Zhaoyang(连召洋）* ** ***,Duan Lijuan* ** ***,Chen Juncheng*,Qiao Yuanhua****,Miao Jun*****.[J].高技术通讯(英文),2021,27(4):357~364 |
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| Online prediction of EEG based on KRLST algorithm |
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| DOI：10.3772/j.issn.1006-6748.2021.04.003 |
| 中文关键词: |
| 英文关键词: brain computer interface ( BCI), kernel adaptive algorithm, online prediction of electroencephalograph (EEG) |
| 基金项目: |
| Author Name | Affiliation | | Lian Zhaoyang(连召洋）* ** *** | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China)
(**Beijing Key Laboratory of Trusted Computing, Beijing 100124, P.R.China)
(***National Engineering Laboratory for Key Technologies of Information Security Level Protection, Beijing 100124, P.R.China)
(****Faculty of Sciences, Beijing University of Technology, Beijing 100124, P.R.China)
(*****Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, P.R.China) | | Duan Lijuan* ** *** | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China)
(**Beijing Key Laboratory of Trusted Computing, Beijing 100124, P.R.China)
(***National Engineering Laboratory for Key Technologies of Information Security Level Protection, Beijing 100124, P.R.China)
(****Faculty of Sciences, Beijing University of Technology, Beijing 100124, P.R.China)
(*****Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, P.R.China) | | Chen Juncheng* | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China)
(**Beijing Key Laboratory of Trusted Computing, Beijing 100124, P.R.China)
(***National Engineering Laboratory for Key Technologies of Information Security Level Protection, Beijing 100124, P.R.China)
(****Faculty of Sciences, Beijing University of Technology, Beijing 100124, P.R.China)
(*****Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, P.R.China) | | Qiao Yuanhua**** | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China)
(**Beijing Key Laboratory of Trusted Computing, Beijing 100124, P.R.China)
(***National Engineering Laboratory for Key Technologies of Information Security Level Protection, Beijing 100124, P.R.China)
(****Faculty of Sciences, Beijing University of Technology, Beijing 100124, P.R.China)
(*****Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, P.R.China) | | Miao Jun***** | (*Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China)
(**Beijing Key Laboratory of Trusted Computing, Beijing 100124, P.R.China)
(***National Engineering Laboratory for Key Technologies of Information Security Level Protection, Beijing 100124, P.R.China)
(****Faculty of Sciences, Beijing University of Technology, Beijing 100124, P.R.China)
(*****Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| Kernel adaptive algorithm is an extension of adaptive algorithm in nonlinear, and widely used in the field of non-stationary signal processing. But the distribution of classic data sets seems relatively regular and simple in time series. The distribution of the electroencephalograph ( EEG) signal is more randomness and non-stationarity, so online prediction of EEG signal can further verify the ro-bustness and applicability of kernel adaptive algorithms. What’s more, the purpose of modeling and analyzing the time series of EEG signals is to discover and extract valuable information, and to reveal the internal relations of EEG signals. The time series prediction of EEG plays an important role in EEG time series analysis. In this paper, kernel RLS tracker (KRLST) is presented to online predict the EEG signals of motor imagery and compared with other 13 kernel adaptive algorithms. The ex-perimental results show that KRLST algorithm has the best effect on the brain computer interface (BCIK) edyatwasoert.d |
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