Motor Imagery EEG Classification Based on Immune Multi-domain-feature Fusion and Multiple Kernel Learning SVM
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摘要: 针对多通道四类运动想象(Motor imagery, MI)脑电信号(Electroencephalography, EEG)的分类问题, 提出免疫多域特征融合的多核学习SVM (Support vector machine)运动想象脑电信号分类算法.首先, 通过离散小波变换(Discrete wavelet transform, DWT)提取脑电信号的时频域特征, 并利用一对多公共空间模式(One versus the rest common spatial patterns, OVR-CSP)提取脑电信号的空域特征, 融合时频空域特征形成特征向量.其次, 利用多核学习支持向量机(Multiple kernel learning support vector machine, MKL-SVM)对提取的特征向量进行分类.最后, 利用免疫遗传算法(Immune genetic algorithm, IGA)对模型的相关参数进行优化, 得到识别率更高的脑电信号分类模型.采用BCI2005desc-Ⅲa数据集进行实验验证, 对比结果表明, 本文所提出的分类模型有效地解决了传统单域特征提取算法特征单一、信息描述不足的问题, 更准确地表达了不同受试者个性化的多域特征, 取得了94.21%的识别率, 优于使用相同数据集的其他方法.Abstract: For the classification problem of four kinds of multi-channel motor imagery (MI) EEG, a multi-class motor imagery EEG classification algorithm based on immune multi-domain-feature fusion and multiple kernel learning SVM is proposed. Firstly, discrete wavelet transform (DWT) is used to extract the time-frequency features of EEG signals. The spatial features of EEG are extracted by using one versus the rest common spatial patterns (OVR-CSP). And then time-frequency and spatial features are fused to form one-dimensional feature vector. Secondly, multiple kernel learning support vector machine (MKL-SVM) is used as a classifier to classify the feature vectors. Finally, immune genetic algorithm (IGA) is used to optimize the parameters of MKL-SVM. The experimental results on BCI2005desc-Ⅲa dataset show that the classification model proposed in this paper not only effectively overcomes the shortcomings that is the traditional single-domain feature extraction algorithm is lack of information description, but also more accurately expresses the multi-domain characteristics of different subjects. It achieves a recognition rate of 94.21%, which is better than other methods of using the same data set.
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Key words:
- Discrete wavelet transform /
- common spatial patterns /
- multiple kernel learning support vector machine /
- immune genetic algorithm /
- motor imagery /
- electroencephalography
1) 本文责任编委 董峰 -
图 1 不同小波函数的SVM分类正确率
Fig. 1 Classification accuracies of different wavelets using the SVM classifier
图 2 不同种类核函数MKL-SVM分类正确率
Fig. 2 Classification accuracy of different kinds of kernel functions MKL-SVM
图 3 不同基核个数的训练耗时对比
Fig. 3 Training time-consuming obtained by different number of kernel
图 4 免疫多域特征融合的多核学习SVM运动想象脑电信号分类算法流程图
Fig. 4 Flow chart of multi-class motion imaging EEG classification based on immune multi-domain-feature fusion and multiple kernel learning SVM
图 8 不同数量的训练集分类正确率
Fig. 8 Classification accuracy of different numbers of training sets
表 1 DWT分解250 Hz采样频率所对应各层的频率
Table 1 Frequencies corresponding to different levels of DWT decomposition with a 250 Hz sampling rate
分解信号 频率范围(Hz) A1 0$\sim$62.5 D1 62.5$\sim$125 D2 31.25$\sim$62.5 D3 15.625$\sim$31.25 D4 7.8125$\sim$15.625 
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表 2 免疫遗传算法参数设置
Table 2 IGA parameter settings
参数 说明 参数 说明 参数 说明 编码方式 二进制编码 选择 轮盘赌选择 适应度值偏差 $1\times 10^{-7}$ 初始种群 随机产生0、1矩阵 交叉 低位交叉, 交叉率0.9 种群多样性参数 $ps=0.95$ 种群大小 30 变异 高位变异, 变异率0.1 抗体浓度阈值 $ t=0.6 $ 记忆库大小 10 停止方式 停止代数和适应度偏差 适应度 测试集识别率 个体大小 151 停止代数 20
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表 3 四种算法识别率对比
Table 3 Recognition rate comparison of four algorithms
方法 识别率(%) K3b K6b L1b 三者 本文方法 98.21 93.91 92.63 94.21 CSP-SVM 88.71 82.55 76.13 83.21 DWT-SVM 82.21 74.24 78.00 77.11 CSP+DWT-SVM 87.14 74.70 83.13 82.69
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表 4 四种算法下K3b受试者识别率对比
Table 4 Recognition rate comparison of four algorithms for K3b
方法 识别率(%) 1 2 3 平均 本文方法 92.36 94.44 93.06 93.29 CSP-SVM 81.94 83.33 82.64 82.64 DWT-SVM 78.47 79.86 79.17 79.17 CSP+DWT-SVM 80.56 81.94 79.86 80.79
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