基于ABC-SVM的运动想象脑电信号模式分类

为了提高运动想象脑电信号分类的准确率，针对传统支持向量机（SVM）分类方法在脑电信号处理中存在寻优繁 琐、工作量大和分类正确率低等问题，本研究提出一种基于人工蜂群（ABC）算法优化SVM的分类识别方法。首先利用正 则化共空间模式对脑电信号进行特征提取，然后利用ABC算法优化SVM的惩罚因子和核参数，最后利用提取的右手和 右脚两类脑电信号样本特征对优化后的SVM进行训练和分类测试。实验结果表明ABC-SVM分类器提高了脑电信号分 类的准确率，比传统的SVM分类器准确率高出2.5%，证明该算法的可行性和较高准确性。

Pattern classification of motor imagery EEG signals based on ABC-SVM algorithm

Due to the problems of traditional support vector machine (SVM) classification method in electroencephalogram (EEG) signal processing, such as high complexity of searching the optimal parameters, heavy workload and low classification accuracy, a new SVMclassification method based on artificial bee colony (ABC) algorithm is proposed in this study to improve the accuracy of motor imagery EEG recognition. Firstly, the regularization common spatial pattern is used for EEG feature extraction. Then penalty factor and kernel function of SVM are optimized by ABC algorithm. Finally, the optimized SVM classifiers is trained and tested by two kinds of EEG data of right foot and right hand movements. The final results show that the accuracy of ABCSVM classifier for EEG classification is averagely 2.5% higher than that of non-parameter-optimized SVM classifier, which proved that the proposed algorithm is feasibility and achieves a high accuracy in motor imagery EEG recognition.