Multi-Voxel Pattern Analysis of fMRI Based on Deep Learning Methods
Multi-Voxel Pattern Analysis of fMRI Based on Deep Learning Methods
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DOI：
10.1007/978-3-319-05527-5_4
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Y. Hatakeyama;Shinichi Yoshida;H. Kataoka;Y. Okuhara
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文献类型：
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作者：
Y. Hatakeyama;Shinichi Yoshida;H. Kataoka;Y. Okuhara
基于多体素模式分析（MVPA），采用深度学习方法进行在线训练，构建了fMRI数据的解码过程。利用深度简网络（DBN）构建的过程提取用于分类的特征，对输入的fMRI数据的每个ROI。对手部运动的解码实验结果表明，基于DBN的解码精度与传统的批量训练方法相当，第一层的特征分割提取在不损失精度的前提下减少了计算时间。所构建的过程应该是每个主题的交互解码实验所必需的。
A decoding process for fMRI data is constructed based on Multi-Voxel Pattern Analysis (MVPA) using deep learning method for online training process. The constructed process with Deep Brief Network (DBN) extracts the feature for classification on each ROI of input fMRI data. The decoding experiment results for hand motion show that the decoding accuracy based on DBN is comparable to that with the conventional process with batch training and that the divided feature extraction in the first layer decreases computational time without loss of accuracy. The constructed process should be necessary for interactive decoding experiments for each subject.