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This paper describes the framework for a branch of augmented cognition research performed at the Adaptive Multimodal Laboratory at the University of Hawaii and a spec application involving the identification of a computer user based on the forces applied to a computer mouse (i.e., click signature) during a task. Data was collected from six people during a pilot study. Two methods used to identify users were a back propagation neural network and discriminant analysis. Results indicate that the discriminant analysis was slightly better at identifying users than the neural network, but its primary advantage was that it required less data preparation. Continuous identification of the user is possible with either method. Successful, identification of the user is a useful first step to proceed to the next stage of the research framework, which is to identify the user's cognitive state for implementation in an augmented cognition system.