The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMFI data
The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMFI data
复制标题
DOI：
10.1016/j.neuroimage.2006.08.016
复制
发表时间：
2006-12-01
期刊：
影响因子：
5.7
通讯作者：
Brammer, Michael
中科院分区：
文献类型：
--
作者：
Mourao-Miranda, Janaina;Reynaud, Emanuelle;Brammer, Michael
在本研究中，我们比较了时间压缩（多次扫描的平均值）和空间选择（即从整个大脑中选择“感兴趣的区域”）对使用支持向量机（SVM）的功能磁共振成像数据的单主题和多主题分类的影响。我们的目的是研究在训练SVM之前可以应用的各种数据转换，以保留任务判别方差，同时抑制方差的不相关分量。数据是在一个封闭的实验设计：观看不愉快（类1），中性（类2）和愉快的图片（类3）。在多受试者水平分析中，我们使用了“leave-one-subject-out”方法，即在每次迭代中，我们使用来自除一个受试者之外的所有受试者的数据训练SVM，并测试其在预测最后一个受试者数据的类别标签方面的性能。在单受试者水平分析中，我们使用了“留一个块”方法，即对于每个受试者，我们在每个条件下随机选择一个块作为测试块，并使用来自剩余块的数据训练SNM。我们的研究结果表明，在一个单一的主题水平的时间压缩和空间选择提高了支持向量机的准确性。然而，在多主题水平，时间压缩提高了支持向量机的性能。但空间选择对分类精度没有影响。(c)2006年爱思唯尔公司All rights reserved.
In the present study, we compared the effects of temporal compression (averaging across multiple scans) and space selection (i.e. selection of "regions of interest" from the whole brain) on single-subject and multi-subject classification of fMRI data using the support vector machine (SVM). Our aim was to investigate various data transformations that could be applied before training the SVM to retain task discriminatory variance while suppressing irrelevant components of variance. The data were acquired during a blocked experiment design: viewing unpleasant (Class 1), neutral (Class 2) and pleasant pictures (Class 3). In the multi-subject level analysis, we used a "leave-one-subject-out" approach, i.e. in each iteration, we trained the SVM using data from all but one subject and tested its performance in predicting the class label of the this last subject's data. In the single-subject level analysis, we used a "leave-one-block-out" approach, i.e. for each subject, we selected randomly one block per condition to be the test block and trained the SNM using data from the remaining blocks. Our results showed that in a single-subject level both temporal compression and space selection improved the SVM accuracy. However, in a multi-subject level, the temporal compression improved the performance of the SVM. but the space selection had no effect on the classification accuracy. (c) 2006 Elsevier Inc. All rights reserved.