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 The existing digital signal analysis tool AIDA used to process the physiological data has been replaced by the Dynalyzer developed by the author with considerable improvements in accuracy and performance.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.
Table of Contents
- Introduction
- Mental Blocks
- Thesis Structure
- Digital Signal Analysis
- Analog to Digital Conversion
- The Fourier Transformation
- The Fast Fourier Transformation
- The Discrete Cosine Transformation
- Windowing
- Window Size
- Window Functions
- The Short Time Fourier Transformation
- Signal Analysis with Correlation Methods
Objectives and Key Themes
This diploma thesis explores the detection of mental blocks in humans using physiological measurements, specifically skin potential and skin resistance. The primary objective is to develop and evaluate a new analysis tool, Dynalyzer, for identifying mental blocks based on data from the Musico Cause and Effect study. The thesis aims to compare Dynalyzer with the existing analysis tool, AIDA, and demonstrate improvements in accuracy and performance.
- Detection of mental blocks using physiological measures.
- Development and evaluation of a new analysis tool, Dynalyzer.
- Comparison of Dynalyzer with existing analysis tool, AIDA.
- Analysis of physiological data and signal processing techniques.
- Exploration of the characteristics of mental blocks in physiological and neuronal contexts.
Chapter Summaries
- Introduction: This chapter introduces the concept of mental blocks and outlines the structure of the thesis. It provides a brief overview of the research objectives and the methodology employed.
- Digital Signal Analysis: This chapter explores various signal processing techniques, including analog-to-digital conversion, the Fourier transformation (Fast Fourier Transformation and Discrete Cosine Transformation), windowing (window size, window functions, and the Short Time Fourier Transformation), and signal analysis with correlation methods.
Keywords
The key concepts and focus topics of this thesis include the detection of mental blocks, signal processing, and physiological measures, specifically skin potential and skin resistance. The research utilizes data from the Musico Cause and Effect study and compares the performance of the developed analysis tool, Dynalyzer, with existing methods.
- Citar trabajo
- Dipl.Ing. Franz-Josef Auernigg (Autor), 2006, Detection of mental blocks in humans based on physiological measures, Múnich, GRIN Verlag, https://www.grin.com/document/54433
