基于遗传算法和概率神经网络提高脑机接口中脑电信号识别率

针对脑机接口（BCI）研究中存在脑电信号（EEG）识别率低的问题，提出一种基于遗传算法（GA）和概率神经网络（PNN）的GA—PNN识别方法．用该方法对EEG提取时频特征，构成模式识别的初始特征．以训练样本识别正确率为适应度函数，采用GA对初始特征进行组合优化．基于优选后的特征，用PNN对测试样本进行分类．该方法使EEG识别正确率达到92．499／5，与2003年BCI国际竞赛最好的处理结果（88．7％）相比，提高近4％，为BCI中EEG的识别提供了有效的手段．

Improving the Recognition Rate of EEG in BCI Based on Genetic Algorithm and Probabilistic Neural Network

Aimed at the problem that the recognition rate of electroencephalogram(EEG) is low in brain-computer interfaces(BCIs),a GA-PNN recognition method based on genetic algorithm(GA) and probabilistic neural network(PNN) was presented.EEG features from time domain and frequency domain are extracted.These features form the initial features for pattern recognition.Then,a GA is used to combine and optimize initial features.The fitness function of the GA is the recognition rate of training samples.(Finally,) a PNN is used to classify testing samples based on these optimized features.The recognition rate can obtain 92.49%,which improves about 4% compared with the best result(88.7 %) of 2003 BCI competition.The method provides an effective way to EEG recognition in BCIs.