Decoding Behavioral Accuracy in an Attention Task Using Brain fMRI Data

摘要

In this paper, we investigate whether we can distinguish that a subject is making a correct or incorrect behavioral response by analyzing the fMRI data of localized brain regions, obtained from a feature-based attention experiment. For each subject, we first construct the feature vectors for each region of interest (including V1, MT or IPS1) from the fMRI signals. Second, we project the feature vectors onto a lower dimensional subspace using Linear Discriminant Analysis (LDA), where the difference between two classes (correct vs. incorrect response) is maximized. Finally, we apply the Bayesian classifier to the projected data, and find that the classification accuracies corresponding to V1, MT and IPS1 are 87.2%, 90.8% and 81.7%, respectively, when all the trials are considered. Our analysis indicates that: when people make correct or incorrect responses, significant difference exists in the fMRI signals, especially in V1 and MT regions, and the difference can be effectively captured by the LDA-Bayesian classifier. We also prove that: when the original data are normally distributed, LDA, which aims to maximize the difference between different classes, is equivalent to the optimal Maximum Likelihood (ML) based classification method.