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首页> 外文会议>International conference on artificial neural networks;ICANN 2011 >Face Prediction from fMRI Data during Movie Stimulus: Strategies for Feature Selection
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Face Prediction from fMRI Data during Movie Stimulus: Strategies for Feature Selection

机译：电影刺激期间基于fMRI数据的人脸预测：特征选择策略

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We investigate the suitability of the multi-voxel pattern analysis approach to analyze diverse movie stimulus functional magnetic resonance imaging (fMRI) data. We focus on predicting the presence of faces in the drama movie based on the fMRI measurements of 12 subjects watching the movie. We pose the prediction as a regression problem where regression coefficients estimated from the training data are used to estimate the presence of faces in the stimulus for the test data. Because the number of features (voxels) exceeds the number of training samples, an emphasis is placed on the feature selection. We compare four automatic feature selection approaches. The best results were achieved by sparse regression models. The correlations between the face presence time-course predicted from fMRI data and manual face annotations were in the range from 0.43 to 0.62 depending on the subject and pre-processing options, i.e., the prediction was successful. This suggests that proposed methods are useful in testing novel research hypotheses with natural stimulus fMRI data.
机译：我们调查多体素模式分析方法的适用性，以分析各种电影刺激功能磁共振成像（fMRI）数据。我们专注于根据看电影的12位受试者的功能磁共振成像测量预测戏曲电影中面部的存在。我们将预测作为一个回归问题，其中从训练数据估计的回归系数用于估计测试数据刺激中面部的存在。由于特征（体素）的数量超过训练样本的数量，因此重点放在特征选择上。我们比较了四种自动特征选择方法。最好的结果是通过稀疏回归模型获得的。从fMRI数据预测的面部存在时间进程与手动面部注释之间的相关性在0.43至0.62的范围内，这取决于对象和预处理选项，即，预测是成功的。这表明所提出的方法可用于测试具有自然刺激功能磁共振成像数据的新颖研究假设。

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