A computational framework for discriminating among different cognitive workloads in EEG signals

In recent years, electroencephalography (EEG) has been used to provide adapting frameworks to the computational procedure according to subject’s implicit recordings. In this direction, the present manuscript provides an acquisition protocol along with a post-processing methodology for discriminating participant’s cognitive workload in different tasks. Subjects participated in three different cognitive workload procedures, the Baseline in which they were verbally counting, the Language test in which they participated in Verbal Fluency Tests, and the Logic test in which they participated in the Raven Advanced Progressive Matrices Tests, while their brain activation was recorded through an EEG device. The raw data were pre-processed and then analyzed using statistical and discriminative analysis. The proposed methodology was able to discriminate between the three different cognitive workloads, thus providing the basis for a media adaptation framework.