基于fMRI脑机接口的数据分类方法的研究

摘要

为了解决脑机接口中功能磁共振成像（functional magnetic resonance imaging ，fMRI）信号的数据分类问题，提出了使用后顶叶皮层进行特征选择的支持向量机分类方法。首先通过核磁设备采集数据，对数据预处理后，将后顶叶皮层的体素选择为特征，然后把血氧水平依赖（blood oxygen level dependent ，BOLD）值的峰值和累积变化作为特征提取，最后使用支持向量机进行数据的分类。实验表明，选择后顶叶皮层作为特征是可行的；使用BOLD 峰值的分类精度要高于使用BOLD累积变化的分类精度。%To solve the data classification of the functional magnetic resonance imaging (fMRI) signals in the brain‐compute interface ,the classification method of support vector machine (SVM ) using posterior parietal cortex (PPC) as feature selection was presented .First ,the data were ac‐quired by the nuclear magnetic device .Next ,the data were preprocessed ,the voxels of PPC were selected as features ,then the peak values and cumulative values of BOLD (blood oxygen level de‐pendent) were selected as the feature extraction .Finally ,SVM was used to classify data .The experiment has show n it is viable to select PPC as feature and the classification accuracy using peak value is higher than the classification accuracy using cumulative value .