太原理工大学学报2017，Vol.48Issue(1)：86-90,5.DOI:10.16355/j.cnki.issn1007-9432tyut.2017.01.014
基于fMRI脑机接口的数据分类方法的研究
Study of Data Classification Method Based on fMRI Brain-Computer Interface
摘要
Abstract
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 .关键词
脑机接口/功能磁共振成像/支持向量机/分类/血氧水平依赖Key words
brain-computer interface/functional magnetic resonance imaging (fMRI)/support vector machines/classification/BOLD分类
信息技术与安全科学引用本文复制引用
张巍,陈俊杰..基于fMRI脑机接口的数据分类方法的研究[J].太原理工大学学报,2017,48(1):86-90,5.基金项目
国家自然科学基金资助项目抑郁症f M RI数据分析方法及辅助诊断治疗模型研究（61170136），多模态脑功能复杂网络分析方法及应用研究（61373101）；山西省软科学研究资助项目 （）
