Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
Xuerui Wang, Rebecca Hutchinson, Tom M. Mitchell
We consider learning to classify cognitive states of human subjects, based on their brain activity observed via functional Magnetic Resonance Imaging (fMRI). This problem is important because such classiﬁers con- stitute “virtual sensors” of hidden cognitive states, which may be useful in cognitive science research and clinical applications. In recent work, Mitchell, et al. [6,7,9] have demonstrated the feasibility of training such classiﬁers for individual human subjects (e.g., to distinguish whether the subject is reading an ambiguous or unambiguous sentence, or whether they are reading a noun or a verb). Here we extend that line of research, exploring how to train classiﬁers that can be applied across multiple hu- man subjects, including subjects who were not involved in training the classiﬁer. We describe the design of several machine learning approaches to training multiple-subject classiﬁers, and report experimental results demonstrating the success of these methods in learning cross-subject classiﬁers for two different fMRI data sets.