【摘要】 想象动作提取是脑—机接口(BC I)技术的关键和难点之一.本文采用连续小波变换结合贝叶斯神经网络组成新的分类方法,利用想象动作思维引起的事件相关去同步(ERD)现象进行特征脑电信息检测与模式识别.研究表明,该方法较常用的线性分类器具有更高的识别准确率和较强的抗干扰能力及较快的识别速度,基本可以满足实时BC I系统模式识别的需求.yh更多还原

【Abstract】 Imaginary movement extraction is one of the key and difficult tasks in brain-computer interface(BCI).In this paper,we utilize a novel classification method synthesized with continuous wavelet transform(CWT) and Bayesian neural network(BNN) to detect and recognize the characteristic EEG(electroencephalogram) information by using the event related desynchronization(ERD) caused by imaginary movement thinking.Research results show that the presented method has a higher recognition accuracy,better anti-noise ability and higher recognition speed than the generally used linear classifiers.It can meet the basic requirments of pattern recognition for realtime BCI system.更多还原