Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning to Decode Cognitive States from Brain Images
Machine Learning
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Mitchell et al. [9] demonstrated that support vector machines (SVM) are effective to classify the cognitive state of a human subject based on fRMI images observed over a single time interval. However, the direct use of classifiers on active voxels veils the understanding of brain activity recognition at the neurological level. In this paper, we present neurological insights to this problem by introducing the covariance selection (CS) to model the correlations between active voxels. In particular, we show that new features, i.e., different correlations between active voxels, are valuable to the recognition of brain activities, in a sense that the fMRI image sequences of different brain activities exhibit quite dissimilar patterns after projection onto these features. Based on the new feature, we employ classifiers, e.g., SVM and nearest neighbor classifier, for brain activity recognition. Significant improvements are achieved compared against the method used in [9].