Machine Learning and Analyzing Human Brain Activity

In recent years there has been a breakthrough in instruments for observing human brain activity, and even more recently machine learning methods have emerged as a valuable new approach to analyze this data.  This talk will present our recent research exploring the patterns of human brain activity associated with the meanings of different words and pictures. For example, machine learning methods can be used to train classifiers to decode whether a person is reading a word about tools or buildings from the fMRI image of their brain activation.  The same trained classifier can then decode the semantic category of a new stimulus whether it is an English word, a Portuguese word, or a line drawing of the object.  We will describe efforts to use machine learning to study the neural representations of meaning in the human brain.

Tom M. Mitchell, Carnegie Mellon University

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