GRIN Detection of mental blocks in humans based on physiological measures

Diploma Thesis from the year 2006 in the subject Computer Science - Applied, grade: 2.0, University of Salzburg (Department of Computer Sciences), course: Natural Computation, language: English, abstract: This thesis presents techniques to detect mental blocks in humans based on the physiological parameters skin potential and skin resistance. We examine physiological measures from the Musico Cause and Effect study of the Science Network for Man and Music at the University of Music and Dramatic Arts, Mozarteum Salzburg. The existing digital signal analysis tool AIDA used to process the physiological data has been replaced by the Dynalyzer developed by the author with considerable improvements in accuracy and performance. We present fundamentals of digital signal processing, outline the measurement of physiological data, and discuss characteristics of mental blocks. We suggest several criteria for the detection of mental blocks based on characteristic features of the physiological time series in the time and/or frequency domain. In first experiments the potential of these criteria is evaluated by applying them to actual physiological data of a test subject. As no ground truth on the occurrence of mental blocks is available, the experimental results can only be an indicator for the quality of the detection of mental blocks. Further experiments are conducted with data from the Vienna determination test assessing the reactive stress tolerance and attention deficits of human test subjects.