Abstract
Corpus-based approaches are widely used in modern neuroscience. In the field, it is important to build a model of the mental context of a subject during functional magnetic resonance imaging (fMRI) experiments. This paper compares the different methods frequently used in natural language processing (NLP). For a better translation between language and brain activation, additional techniques related to NLP, such as pointwise mutual information (PMI) and Normalized Google Distance (NGD), are also introduced. These methods are compared so as to fit brain activations when shown stimulus nouns. The experimental results indicate that NGD was more effective among these methods when applying NLP techniques.
