The galvanic skin response alludes to changes in perspiration organ movement that are intelligent of the power of our passionate state, also called enthusiastic excitement. it has normally been explored through single or few measures and in one exploratory situation. Galvanic Skin Response (GSR) has as of late stood out for researchers as a planned physiological marker of intellectual feelings. In this exploration, intending to play out a far reaching study, we have evaluated GSR information capture for fifteen writing task and three difficulty levels and determined time and frequency domain feature. We determined that cognitive load is proportional to the task difficulty level. If cognitive load increases, task difficulty level will increase. If cognitive load decreases, task difficulty level will decrease.
Table of Contents
Chapter 1. Introduction
1.1 Introduction
1.3 Objectives
1.3 Back Ground of this Thesis
1.4 Motivation
1.5 Thesis Layout
Chapter 2. Literature Review
2.1 Literature Review
Chapter 3. Cognitive Load
3.1 Introduction
3.2 Types of Cognitive load
3.2.1. Intrinsic load
3.2.2. Extraneous load
3.2.3. Germane Load
Chapter 4. Galvanic Skin Response
4.1 Introduction
4.2 The Background of GSR signals
4.3 GSR signals explained
Chapter 5. Data Collection
5.1. Task Description
5.2. Apparatus
5..3. QS222 GSR Sensor
5.4. Labquest Mini
5.5 Logger Pro Software
Chapter 6. Process of GSR Analysis
6.2. Simulation Example 01
6.3. Simulation Example 02
6.4. Time domain feature
6.5 Frequency domain features
Chapter 7. Analysis of Varince Test
7.1 Introduction
7.2 Types of Tests
7.2.1 One Way ANOVA
7.2.2 Two Way ANOVA
7.3 ANOVA vs. T Test
7.4 Alpha Level
7.5 P-Value
7.6 Difference between an alpha level and a p-value
Chapter 8. Experimental Results & Discussion
8.1 Introduction
8.2 For time domain
8.3 For frequency Domain
Chapter 9. Conclusions and Future Research
Research Objectives and Focus
The primary objective of this thesis is to explore the use of Galvanic Skin Response (GSR) as a physiological marker to measure cognitive load fluctuations during writing and listening tasks. The research investigates whether GSR signals can reliably indicate levels of cognitive effort across tasks of varying difficulty, aiming to provide a methodology that helps educators optimize learning materials and decrease unnecessary mental strain on students.
- Measurement of cognitive load using Galvanic Skin Response (GSR).
- Statistical validation of cognitive load indicators using ANOVA tests.
- Comparison of time-domain and frequency-domain signal features.
- Evaluation of task difficulty impacts on physiological stress responses.
- Normalization of subjective GSR data for reliable cross-subject analysis.
Excerpt from the Book
4.3 GSR Signals Explained
The time course of the signal is considered to be the result of two additive processes: a tonic base level driver, which fluctuates very slowly, and a faster-varying phasic component. Changes in phasic activity can be identified in the continuous data stream as these bursts have a steep incline to a distinctive peak and a slow decline relative to the baseline level. Researchers focus on the latency and amplitudes of the phasic bursts with respect to stimulus onset when investigating GSR signal changes in response to sensory stimuli (images, videos, sounds).
The GSR signal is very easy to record. In general just two electrodes put at the second and third finger of one hand are necessary. The variation of a low-voltage applied current between the two electrodes is used as measure of the EDA. Recently, new commercial healthcare devices more and more wearable and fancy (bracelets, watchs) have been developed, thus such measure is usable in each research activity in the neuroscience domain also in no-laboratory settings.
When there are significant changes in GSR activity in response to a stimulus, it is referred to as an Event-Related Skin Conductance Response (ER-SCR). These responses, otherwise known as GSR peaks, can provide information about emotional arousal to stimuli.Other peaks in GSR activity that are not related to the presentation of a stimulus are referred to as Non-Stimulus-locked Skin Conductance Responses (NS-SCR). By using the skin conductance values, or the number of GSR peaks, it’s possible to add quantitative data to studies of emotional arousal. With more data at hand, it’s easier to uncover new findings, and make new discoveries about human behavior.
Summary of Chapters
Chapter 1. Introduction: This chapter introduces the research context, highlighting the role of human emotions and cognitive load, and defines the objectives and motivations for using GSR in educational settings.
Chapter 2. Literature Review: The chapter covers the history of GSR research, explaining the distinction between tonic and phasic activity and the current state of physiological stress measurement.
Chapter 3. Cognitive Load: It provides a theoretical overview of Cognitive Load Theory, including the differentiation between intrinsic, extraneous, and germane cognitive load.
Chapter 4. Galvanic Skin Response: This chapter explains the physiological mechanisms of GSR and discusses how it serves as a marker for emotional arousal and mental state tracking.
Chapter 5. Data Collection: Details the experimental setup, including the use of the Q-S222 GSR sensor, LabQuest interface, and Logger Pro software to capture data from participants.
Chapter 6. Process of GSR Analysis: This section describes the data processing steps, including the implementation of time-domain and frequency-domain features and the necessity of data normalization.
Chapter 7. Analysis of Varince Test: An overview of the statistical methods used, specifically explaining one-way and two-way ANOVA tests and the relationship between alpha levels and p-values.
Chapter 8. Experimental Results & Discussion: Presents the findings of the experiments, demonstrating the significance of normalized data in distinguishing task difficulty levels through GSR analysis.
Chapter 9. Conclusions and Future Research: Summarizes the key findings, confirming that cognitive load correlates with task difficulty, and suggests future research directions involving machine learning.
Keywords
Cognitive Load, Galvanic Skin Response, GSR, Electrodermal Activity, ANOVA, Physiological Markers, Stress Detection, Time Domain Analysis, Frequency Domain Analysis, Task Difficulty, Data Normalization, Human-Computer Interaction, Learning Efficiency, Biofeedback, Sensor Technology.
Frequently Asked Questions
What is the core focus of this research?
The research focuses on utilizing Galvanic Skin Response (GSR) as a physiological tool to measure and analyze cognitive load fluctuations in individuals during various writing and listening tasks.
What are the key thematic areas addressed?
The thesis covers cognitive load theory, physiological stress signals (GSR), data collection techniques using specialized sensors, and statistical evaluation methods like ANOVA.
What is the primary research goal?
The goal is to determine if cognitive load levels are proportional to task difficulty and if GSR can serve as a non-intrusive method to identify and quantify this relationship.
Which methodology is employed in this thesis?
The study uses experimental data collection via Q-S222 sensors, followed by a signal analysis process that includes data normalization and statistical validation using one-way ANOVA.
What topics are discussed in the main body?
The main body details the historical background of GSR, the theoretical framework of cognitive load, technical specifications of the hardware, signal processing techniques, and the statistical interpretation of results.
Which keywords characterize this work?
Key terms include Galvanic Skin Response, Cognitive Load, ANOVA, Data Normalization, and Physiological Stress Measurement.
Why is data normalization necessary for this analysis?
GSR signals are highly subject-dependent and subjective; normalization is required to reduce individual variability and obtain comparable data across different participants.
What was the outcome of the frequency domain analysis?
The research found that segmenting the signal into 16 data points provided the most statistically significant values for evaluating cognitive load changes.
What does the author suggest for future research?
The author concludes that future research should incorporate machine learning methodologies to further enhance the accuracy of cognitive load detection.
- Arbeit zitieren
- Abrar Nayeem (Autor:in), 2019, Measurement of Cognitive Load in Accordance with Listening and Observation for Writing Tasks Using Galvanic Skin Response, München, GRIN Verlag, https://www.grin.com/document/509821
