A Two-Stage State Recognition Method for Asynchronous SSVEP-Based Brain-Computer Interface System

ZHANG Zimu; DENG Zhidong

doi:10.3724/SP.J.1218.2013.00045

A Two-Stage State Recognition Method for Asynchronous SSVEP-Based Brain-Computer Interface System

ZHANG Zimu,
DENG Zhidong^,

State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

详细信息

作者简介:
ZHANG Zimu (1984—), male, Ph.D. candidate. His research interests include neural science, signal processing, BCI, and robot control.
DENG Zhidong (1966—), male, Professor. His research interests include computational intelligence, BCI, complex network theory, computational biology, virtual reality, wireless sensor network, and robotics.

通信作者:
DENG Zhidong, michael@tsinghua.edu.cn

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- 文章访问数: 35
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- PDF下载量: 457
出版历程
- 收稿日期: 2012-03-04
- 修回日期: 2012-11-05
- 发布日期: 2013-01-14

A Two-Stage State Recognition Method for Asynchronous SSVEP-Based Brain-Computer Interface System

ZHANG Zimu,
DENG Zhidong^,

State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

摘要

摘要: A two-stage state recognition method is proposed for asynchronous SSVEP (steady-state visual evoked potential) based brain-computer interface (SBCI) system. The two-stage method is composed of the idle state (IS) detection and control state (CS) discrimination modules. Based on blind source separation and continuous wavelet transform techniques, the proposed method integrates functions of multi-electrode spatial filtering and feature extraction. In IS detection module, a method using the ensemble IS feature is proposed. In CS discrimination module, the ensemble CS feature is designed as feature vector for control intent classification. Further, performance comparisons are investigated among our IS detection module and other existing ones. Also the experimental results validate the satisfactory performance of our CS discrimination module.
- state recognition /
- ensemble feature model /
- blind source separation (BSS) /
- continuous wavelet transform(CWT) /
- steady-state visual evoked potential (SSVEP) /
- brain-computer interface (BCI) /
- electroencephalogram(EEG)
Abstract: A two-stage state recognition method is proposed for asynchronous SSVEP (steady-state visual evoked potential) based brain-computer interface (SBCI) system. The two-stage method is composed of the idle state (IS) detection and control state (CS) discrimination modules. Based on blind source separation and continuous wavelet transform techniques, the proposed method integrates functions of multi-electrode spatial filtering and feature extraction. In IS detection module, a method using the ensemble IS feature is proposed. In CS discrimination module, the ensemble CS feature is designed as feature vector for control intent classification. Further, performance comparisons are investigated among our IS detection module and other existing ones. Also the experimental results validate the satisfactory performance of our CS discrimination module.
- state recognition /
- ensemble feature model /
- blind source separation (BSS) /
- continuous wavelet transform(CWT) /
- steady-state visual evoked potential (SSVEP) /
- brain-computer interface (BCI) /
- electroencephalogram(EEG)

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参考文献(21)

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文章访问数: 35
HTML全文浏览量: 1301
PDF下载量: 457
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A Two-Stage State Recognition Method for Asynchronous SSVEP-Based Brain-Computer Interface System

通信作者: DENG Zhidong, michael@tsinghua.edu.cn

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出版历程

A Two-Stage State Recognition Method for Asynchronous SSVEP-Based Brain-Computer Interface System

计量

出版历程

目录

通信作者:
DENG Zhidong, michael@tsinghua.edu.cn