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Process models of cognition, written in architectures such as ACT-R and EPIC, should be able to interact with the same software with which human subjects interact. By eliminating the need to simulate the experiment, this approach would simplify the modeler’s effort, while ensuring that all steps required of the human are also required by the model. In practice, the difficulties of allowing one software...
Most of the current EEG-based workload classifiers are subject-specific; that is, a new classifier is built and trained for each human subject. In this paper we introduce a cross-subject workload classifier based on a hierarchical Bayes model. The cross-subject classifier is trained and tested with data from a group of subjects. In our work, it was trained and tested on EEG data collected from 8 subjects...
EEG data has been used to discriminate levels of mental workload when classifiers are created for each subject, but the reliability of classifiers trained on multiple subjects has yet to be investigated. Artificial neural network and naive Bayesian classifiers were trained with data from single and multiple subjects and their ability to discriminate among three difficulty conditions was tested. When...
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