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脑-机接口(brain-computer interface,BCI)是在大脑与外部设备间建立一个直接的信息交流通路,它无须依赖外周神经肌肉系统而仅通过脑电信号特征提取与模式识别来实现思维表达或指令操作.变频视觉诱发电位(chirp stimuli visual evoked potential,Chirp-VEP)是最近提出的一种脑电诱发新模式,可作为BCI控制信号,极富应用潜力.然而Chirp-VEP的诱发条件、信号处理、特征提取方法等都缺乏充分研究.本文采用不同起始频率和chirp调频率进行了Chirp-VEP诱发实验,利用Chirplet变换(chirplet transform,CT)等4种时频分析方法提取了ChirpVEP信号特征.研究结果表明,相较于其他时频分析方法,CT可获得更高的VEP信噪比与正确识别率.在8名受试者参加的在线BCI测试中,Chirp-VEP的总平均正确识别率高达97.8%,进一步验证了Chirp-VEP应用于BCI控制的潜力.
The brain-computer interface (BCI) is a direct information exchange path between the brain and external devices. It does not rely on the peripheral neuromuscular system but only through the brain electrical signal feature extraction and pattern recognition to achieve the expression of thinking or Order a copy of this thesis Chirp-VEP is a recently proposed new model of EEG induction that can be used as a BCI control signal, which has great potential for application.However, the Chirp-VEP induced condition, signal Processing, and feature extraction methods, etc. Chirp-VEP experiments were conducted using different initial frequencies and chirp frequencies, and ChirpVEP signal characteristics were extracted using four time-frequency analysis methods, such as chirplet transform (CT) The results show that CT achieves higher VEP signal-to-noise ratio and correct recognition rate than other time-frequency analysis methods.A total average of Chirp-VEP was correctly identified in the online BCI test of 8 subjects Up to 97.8%, further validating the potential of Chirp-VEP for BCI control.