基于SVM的fMRI数据分类:一种解码思维的方法

SVM Based fMRI Data Classification:An Approach to Decode Mental State

使用机器学习分类f MRI数据的方法已逐渐被应用到解码思维状态的研究中.对比了使用血氧含量水平(blood oxygenlevel dependent,BOLD)累计变化和使用BOLD变化时间序列作为特征值训练SVM分类器,并依此来判断人脑正在执行的高级思维类型.在预测4×4 Sudoku问题类型的实验中,使用BOLD时间序列为特征的方法分类正确率较高.通过分析分类正确率较高的voxel的解剖结构,发现很多voxel位于前额、顶叶、前扣带回等与高级思维关系密切的脑区,实验结论与认知神经科学相关结论吻合.该方法可以进一步应用在脑机接口(brain computer interface,BCI)等领域.

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. Two feature extraction methods are compared in this paper, one is based on the cumulative change of blood oxygen level dependent （BOLD） signal of activated brain areas, and the other is based on the values at each time point in the BOLD signal time course of each trial. The authors collected the fMRI data while participants were performing a simplified 4× 4 Sudoku problems, and predicted the complexity （easy vs. complex） or the steps （1-step vs. 2-steps） of the problem from fMRI data using these two feature extraction methods respectively. Both methods can produce quite high accuracy, and the performance of the latter approach is better than the former. The results indicate that SVM can be used to predict high-level cognitive states from fMRI data. Moreover, the feature extraction based on serial signal change of BOLD effect can predict cognitive state better because it could use abundant and typical information kept in BOLD effect data. By ranking accuracy of every single-voxel based classifier, it is interesting that voxels with higher accuracy are anatomically located in PPC, PFC, ACC and other brain regions closely related to problem neuroscience. The methods might shed solving, which is consistent with light on brain computer interface previous studies in cognitive （BCI）.