高级检索
    邓红霞, 相 洁, 游 雅, 李海芳. 基于fMRI的思维数据分析方法研究[J]. 计算机研究与发展, 2014, 51(4): 773-780.
    引用本文: 邓红霞, 相 洁, 游 雅, 李海芳. 基于fMRI的思维数据分析方法研究[J]. 计算机研究与发展, 2014, 51(4): 773-780.
    Deng Hongxia, Xiang Jie, You Ya, Li Haifang. Analysis Method of Thinking Data Based on fMRI[J]. Journal of Computer Research and Development, 2014, 51(4): 773-780.
    Citation: Deng Hongxia, Xiang Jie, You Ya, Li Haifang. Analysis Method of Thinking Data Based on fMRI[J]. Journal of Computer Research and Development, 2014, 51(4): 773-780.

    基于fMRI的思维数据分析方法研究

    Analysis Method of Thinking Data Based on fMRI

    • 摘要: 利用功能磁共振成像(functional magnetic resonance imaging, fMRI)技术解读思维数据,已经实现大脑活动的功能定位,但是大脑的思维过程具体如何运行还不得而知;利用何种分析方法更能揭示思维过程也待进一步研究.采用解决4×4数独问题和图像情感反应的两种刺激任务获取思维过程数据,来分别解读不同的思维状态,探索适用于不同思维数据的分析方法.实验数据证明t-test的特征选择方法、峰值所在时间点的特征提取的方法和SVM分类算法较适用于分析这两种不同思维状态的fMRI数据,揭示正确的思维状态.

       

      Abstract: Recently, a growing number of studies have shown that machine learning technologies can be used to decode mental state from functional magnetic resonance imaging (fMRI) data. It has achieved the functional positioning of the brain activity using fMRI technology to interpret the thinking data.But how to run the specific operation of the brains thinking process is still unknown. The analytical methods to better reveal the thinking process need to be further studied. Through adopting two stimuli tasks of solving the 4×4 Sudoku problems and image emotional reaction, the thinking process data which interpret the different state of mind is gotten, and different ways of thinking data analysis methods are explored. The experiments proved that the feature selection methods of t-test, the feature extraction methods of the peak time and the classification algorithm of SVM are more suitable for analyzing the fMRI data, especially to two different states of mind data above, which can reveal the correct state of mind. This essay should provide the theory basis and data for promoting the fMRI technology to interpret the thinking data.

       

    /

    返回文章
    返回