感知器算法在运动想象脑电模式识别中的应用

基于脑电(EEG)的脑机接口(BCI)是在人脑和计算机或其它电子设备之间建立不依赖于常规大脑信息输出通路(外周神经和肌肉组织)的全新对外信息交流和控制技术。及时有效地提取和识别与运动想象相关的脑电模式可以帮助严重瘫痪病人控制光标或辅助运动设备以替代其受损的运动功能,建立一种与外界交流沟通的新途径。论文将以EEG(C3,C4)两个通道的mu节律能量作为特征向量,用感知器算法对左右手运动想象脑电模式进行识别,实验结果表明,正确识别率可达87.86%。由于感知器算法计算简单,故可以认为,感知器算法在脑机接口的应用中有较高的实用价值。

Application of Perceptron Algorithm in Discrimination of Motor Imagery Event-related EEG Pattern

The EEG-based brain-computer interface(BCI) is a novel technology,which provides a wholly new channelbetween the computer and electronic equipments instead of the normal output pathways of peripheral nerves and muscles.Quick and correct classification of these event-related EEG pattern can be used to help patients with severe paralysis to move a cursor or to replace impaired motor function and provide a new communication channel to environment.In this paper,we will extract the energy of mu rhythm of two channels(C3,C4) as feature vector,and classify the event-related EEG pattern during left and right hand motor imagery with the perceptron algorithm.According to the analysis and experiment results,the correct rate of classification can achieve 87.86%.Since the computation of perceptron algorithm is very simple,the new method has the practicability in the application of brain-computer interface technique.