ZHANG Zimu, DENG Zhidong. A Two-Stage State Recognition Method for Asynchronous SSVEP-Based Brain-Computer Interface System[J]. 机器人, 2013, 35(1): 45-51. DOI: 10.3724/SP.J.1218.2013.00045
引用本文: ZHANG Zimu, DENG Zhidong. A Two-Stage State Recognition Method for Asynchronous SSVEP-Based Brain-Computer Interface System[J]. 机器人, 2013, 35(1): 45-51. DOI: 10.3724/SP.J.1218.2013.00045
ZHANG Zimu, DENG Zhidong. A Two-Stage State Recognition Method for Asynchronous SSVEP-Based Brain-Computer Interface System[J]. ROBOT, 2013, 35(1): 45-51. DOI: 10.3724/SP.J.1218.2013.00045
Citation: ZHANG Zimu, DENG Zhidong. A Two-Stage State Recognition Method for Asynchronous SSVEP-Based Brain-Computer Interface System[J]. ROBOT, 2013, 35(1): 45-51. DOI: 10.3724/SP.J.1218.2013.00045

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

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

  • 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.

     

    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.

     

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