基于自适应遗传算法的脑电信号特征选择

针对脑机接口(BCI)研究中脑电信号的特征选择问题,本文提出了一种自适应的遗传算法(AGA).它与标准遗传算法(SGA)的区别在于对交叉和变异概率进行自适应选择.在SGA中,采用固定的交叉和变异概率,因而容易造成早熟和局部收敛; 而AGA对两种概率的自适应选择保留了种群的多样性,并且有利于全局收敛.为检验提出方法的有效性,将其与基于SGA的特征选择方法以及基于Fisher距离的滤波选择方法进行了比较,实验结果表明AGA的分类精度明显高于其它方法,获得了最好的模式识别性能.

Automated Feature Selection Based on Adaptive Genetic Algorithm for Brain-Computer Interfaces

In brain-computer interfaces(BCIs),a feature selection approach using an adaptive genetic algorithm(AGA) was described.In the AGA,each individual among the population has its own crossover probability and mutation probability.The probabilities of crossover and mutation are varied depending on the fitness values of the individuals.The adaptive probabilities of crossover and mutation are propitious to maintain diversity in the population and sustain the convergence capacity of the genetic algorithms(GAs).The performance of the AGA was compared with those of the Standard GA(SGA) and the Filter method in selecting feature subset for BCIs.The results show that the classification accuracy obtained by the AGA is significantly higher than those obtained by other methods.Furthermore,the AGA has a higher convergence rate than the SGA.