Detection of mental blocks in humans based on physiological measures

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.