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.
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.
出处
《机器人》
EI
CSCD
北大核心
2013年第1期45-51,共7页
Robot
基金
National Natural Science Foundation of China(90820305,60775040)
关键词
检测模块
机器人
连续小波变换技术
视觉诱发
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）
