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The main goal of the present study is to launch the foundations of a pipeline for fMRI-based human behavior classification, addressing however some particularities of cognitive processes. While studying cognition, much of the experiments with fMRI use devices to record subjects’ responses, which recruits the participation of the motor cortex. Although the influence of this aspect may be reduced in subtractive univariate analyses methods, it may negatively interfere in multivariate methods. The fMRI data here used is exempted of motor responses. Subjects were asked to form impressions about persons, objects, and brands, but their thoughts were not recorded by devices. The feedforward backpropagation artificial neural network was used. With this procedure it was possible to correctly classify above randomness. The analysis of the hidden nodes reveals the extensive participation of the fusiform gyri and lateral occipital cortex in this cognitive process, corroborating the critical participation of these structures during classification in the natural brain.

'Mind Reading': Hitting Cognition by Using ANNs to Analyze fMRI Data in a Paradigm Exempted from Motor Responses