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Abstract

A computer-aided training system uses electroencephalograms (EEGs) recorded from the trainee's scalp to alter the training protocol being presented by the computer, for example to present a new task to the trainee when he or she has mastered and automatized the current task. The index of the trainee's skill mastery and automatization is determined by analysis of the EEG using mathematical classification functions which distinguish different levels of skill acquisition. The functions are computed by computer neural networks and consist of a combination of EEG and other physiological variables which specifically characterize a trainee's level of focused attention and neurocognitive workload and his or her neurocognitive strategy. The functions are derived either for a group of trainees, or each trainee individually, performing a battery of one or more standard training tasks while wearing an EEG hat.

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First Claim