基于人工蜂群优化高斯过程的运动想象脑电信号分类

Classification of Motor Imagery EEG Based on Gaussian Process Optimized with Artificial Bee Colony

针对传统的高斯过程采用共轭梯度法确定超参数时对初值有较强依赖性且易陷入局部最优的问题,提出了一种基于人工蜂群优化的高斯过程分类方法,用于脑电信号的模式识别。首先,构建高斯过程模型,选择合适的核函数且确定待优化的参数。然后,选取识别错误率的倒数为适应度函数,使用人工蜂群算法搜索寻找出限定范围内可以取得最优准确率的超参数。最后,采用参数优化后的高斯过程分类器对样本分类。分别采用2008年竞赛数据集BCI CompetitionⅣData Set 1和2005年数据集BCI CompetitionⅢData SetⅣa对所提方法进行验证,并与支持向量机(SVM)、人工蜂群优化的支持向量机(ABC-SVM)、高斯过程分类(GPC)方法进行比较,实验结果表明了所提方法的有效性。

The conjugate gradient method is used to determine the parameters in the traditional Gaussian process. However, the conjugate gradient method has a strong dependence on the initial value and is easy to fall into local op- timum. In order to solve the problem, a Gaussian process classification（GPC） method is proposed based on artificial bee colony（ ABC ）optimization and applied for pattern recognition of EEG signals. Firstly, Gaussian process model is constructed, and suitable kemel function is chosen and the parameters to be optimized are specified. Then the recip- rocal of the recognition error rate is selected as fitness function, and the parameters which are used to obtain optimal accuracy in a limited range are found out by employing the ABC algorithm. Finally, the Gaussian process classifier with optimized parameters is used to classify the samples. The efficiency of the propose method has been demonstra- ted by comparison with support vector machine （SVM）, support vector machine optimized with Artificial bee colony （ABC-SVM） and GPC algorithms on both BCI Competition 1V Data Set 1 in 2008 and BCI Competition HI Data Set IV a in 2005.