KRIGOLSON TEACHING
  • NEUROSCIENCE
    • NEUROSCIENCE 100 >
      • NEURO 100 INTRODUCTION
      • NEURO 101 ADVANCED
      • NEURO 102 AGING
      • NEURO 103 MEMORY
      • NEURO 104 DECISION MAKING
      • NEURO 105 LEARNING
      • Research Statistics
    • NRSC 500B / MEDS 470
  • Kinesiology
    • EPHE 245 >
      • LABORATORY
      • PRACTICUM
    • EPHE 357
  • STATISTICS
    • LECTURE >
      • INTRODUCTION TO R
      • DESCRIPTIVE STATISTICS
      • VISUALIZING DATA
      • Correlation and Regression
      • MULTIPLE REGRESSION
      • LOGIC OF NHST
      • T TESTS
      • ANOVA
      • POST HOC ANALYSIS
      • NON PARAMETRIC STATISTICS
      • FACTORIAL ANOVA
      • Repeated Measures ANOVA
      • Mixed ANOVA
      • MULTIVARIATE ANOVA
      • THE NEW STATISTICS
      • Bayesian Methods
    • ASSIGNMENTS >
      • Introduction to R >
        • INTRODUCTION TO R
        • LOADING DATA
        • DATA TABLES
      • Descriptive Statistics >
        • Mean, Median, and Mode
        • VARIANCE
        • CONFIDENCE INTERVALS
        • SHORTCUTS
      • Visualizing Data >
        • PLOTTING BASICS
        • BAR GRAPHS
        • BOXPLOTS
        • HISTOGRAMS
        • USING GGPLOT I
        • USING GGPLOT II
        • USING GGPLOT III
      • Correlation and Regression >
        • CORRELATION
        • REGRESSION
      • MULTIPLE REGRESSION >
        • MULTIPLE REGRESSION
      • Logic of NHST >
        • Sample Size and Variance
        • DISTRIBUTIONS
        • TESTING DISTRIBUTIONS
      • T-Tests >
        • Single Sample TTests
        • Paired Sample TTests
        • Independent Sample TTests
      • ANOVA >
        • ANOVA ASSUMPTIONS
        • ANOVA
      • POST HOC ANALYSIS >
        • POSTHOC ANALYSIS
      • NON PARAMETRIC STATISTICS >
        • WILCOXON TEST
        • WILCOXON SIGNED TEST
        • MULTIPLE GROUPS
      • FACTORIAL ANOVA
      • REPEATED MEASURES ANOVA >
        • RM ANOVA
        • TREND ANALYSIS
      • MIXED ANOVA
      • MULTIVARIATE ANOVA
      • THE NEW STATISTICS
      • BAYESIAN TTESTS
    • RESOURCES
    • R TIPS
  • Directed Studies
    • Advanced Topics in Motor Control A
    • Advanced Topics in Motor Control B
    • An Introduction to EEG
    • Advanced EEG and ERP Methods
    • Neural Correlates of Human Reward Processing
    • Independent Research Project
  • MATLAB
    • THE BASICS >
      • Hello World
      • BASIC MATHEMATICS
      • VARIABLES
      • Matrices
      • Writing Scripts
      • PATHS AND DIRECTORIES
      • USER INPUT
      • FOR LOOPS
      • WHILE LOOPS
      • IF STATEMENTS
      • RANDOM NUMBERS
    • STATISTICS >
      • LOADING DATA
      • DESCRIPTIVE STATISTICS
      • MAKING FUNCTIONS
      • BAR GRAPHS
      • LINE GRAPHS
      • TTESTS
    • EXPERIMENTS: THE BASICS >
      • DRAWING A CIRCLE
      • DRAWING MULTIPLE OBJECTS
      • DRAWING TEXT
      • DRAWING AN IMAGE
      • PLAYING A TONE
      • KEYBOARD INPUT
      • BUILDING A TRIAL
      • BUILDING TRIALS
      • NESTED LOOPS
      • RIGHT OR WRONG
      • SAVING DATA
    • EXPERIMENTS: ADVANCED >
      • STROOP
      • N BACK
      • Oddball
      • Animation
      • VIDEO
    • EEG and ERP Analysis >
      • ERP Analysis
  • RESOURCES
    • EXCEL
    • HOW TO READ A RESEARCH PAPER
    • HOW TO WRITE A RESEARCH PAPER
  • Workshops
    • Iowa State EEG Workshop 2018
  • Python
    • The Basics >
      • Setting Up Python
      • Hello, world!
      • Basic Math & Using Import
      • Variables
      • Matrices
      • Scripts
      • User Input
      • For Loops

DIRECTED STUDIES
​

​An Introduction to Electroencephalography

This directed studies course is intended for undergraduate and graduate students wishing to learn more about EEG and ERP research.

​Note students in this course can enrol in these either EPHE 494: Directed Studies or MEDS 490: Directed Studies. Note, students can only do a maximum of 3.0 units of EPHE 494 and 3.0 units of MEDS 490.

Course Grade
Assignments /Quizzes (10): 5% each              TOTAL: 50%
Final Exam (1): 50%                                          TOTAL: 50%

Assignments and Quizzes
Weekly assignments / quizzes will be used to ensure students read and understand the course material.

Final Exam
Students will complete a final exam on all of the assigned readings and videos. The exam will be comprised of 10 short answer questions.

Pro Forma
Download the form HERE for EPHE 494 and HERE for MEDS 490, send to Dr. Krigolson with your information filled in and the information below. DO NOT SIGN IT.
1. Fill in the Academic Term - you can find this in the UVic Academic Calendar.
2. Select whether this is an In person or Online course.
3. Fill in the due date - for quizzes its the last day of November, March, or July - for the final exam it is the 15th of December, April, or August.
4. Fill in the Course Section Dates.
5. Fill in the Dean for your faculty.
Curriculum
Week One: What is EEG? 
Watch: What is EEG?
Watch: Brain Waves and Their Functions
Read: Neurophysiologic Basis of EEG

To Do: View and record your own continuous EEG data with a MUSE and the PEER application
​
Discovery Questions
1. What is EEG?
2. How is EEG generated?
3. What is happening in the brain that results in changes in the continuous observed EEG voltage over  time?
Week Two: How is EEG Quantified
Read: Teplan 2002
Read: Jackson 2014
Watch: EEG Frequency Bands I
Watch: EEG Frequency Bands II

To Do: Ensure you on PEER using a MUSE that you play with the Raw EEG display so you can see not only the continuous EEG but also activity in the different frequency bands.

Discovery Questions:
1. How is EEG data recorded?
2. What issues must be considered when recording EEG data?
3. What is EEG power?
4. What are the delta, theta, alpha, beta, and gamma bands? What does activity in these ranges reflect?
Week Three: Background Knowledge
Watch: What is Ohm's Law?
Read: An Overview of Ohm's Law
Read: Another Overview of Ohm's Law

​To Do: Using PEER Analytics and a MUSE, play with the electrode positions and look at how the waveform lines change colour. These colour changes reflect changes in electrical impedance.

Discovery Questions:
1. What is voltage, current, and resistance?
2. What is the difference between resistance and impedance?
3. Review all of the readings to date. What things can be done in an EEG laboratory to reduce impedance?
Week Four: Understanding Waveforms
Read: Sampling
Watch: Digitization
Watch: Sampling

​To Do: Watch a big system testing session.

Discovery Questions:
1. What is the difference between a digital and an analog signal?
2. What is meant by sampling? Think of EEG - how do the electrodes work?
3. Consider what sampling your heart rate via a stethoscope versus EEG looks like. Draw this out.
4. Look at the waveform of an audio file. How can you relate this to sampling?
Week Five: Deeper Understanding
Read: Rugg and Coles, Chapter One

​To Do: Help with a big system testing session.
To Do: Play the Oddball Task using PEER Analytics and a MUSE. Afterwards, review the data and select N200 and P300 to see your own P300 ERP component!

Discovery Questions:
1. What is an event-related brain potential (ERP)?
2. How are ERPs related to EEG?
3. The P300 is an example of an event related brain potential. What is it and what does it reflect?
Week Six: A Broad Overview of the ERP Technique
Read: Luck, Chapter 1

​To Do: Help with a big system testing session.

Discovery Questions:
1.    What is EEG? What is an ERP? Be careful to explain how they are similar and how they differ.
2.    
Explain a typical ERP system setup (Figure 1.1A). Do not memorize all of the electrode positions, but you should know what the letters and numbers mean and their relationship to head position.

3.     
How are ERPs created from a continuous EEG signal? (Figure 1C to 1F).

4.    
What are basic naming conventions of ERP components? (Page 8).

5.    
What are the neural origins of ERP components? (Page 12-13)

6.    
What are ERPs good for? (Page 25)
7.     What are ERPs bad for? (Page 29)
Week Seven: A Closer Look at ERPs and ERP Components
Read: Luck, Chapter 2

​To Do: Help with a big system testing session.

Discovery Questions:1.    What sort of electrical activity of neurons forms the basis of ERP components? (Page 39)
2
.    What is the principle of post-synaptic summation? (Page 40) What role does volume conduction play in generating ERP components?
3.    
What is an equivalent current dipole and how does it create ERP components? (Page 43, be able to explain Figure 2.2).

4.    
Explain Figures 2.3 and 2.5. What is the difference between an underlying component and a waveform peak?

5.    
What are difference waveforms and why do we use them?
6.     How is ERP component amplitude affected by differences in latency within subjects and between subjects?
Week Eight: Overview of ERP Components
Read: Luck, Chapter 3

​To Do: Help with a big system testing session.

Discovery Questions:
1.    What are the P1 and N1?
2.    
What is the N2?

3.     
What is the P3?
4.    What are the ERN and FRN?
Week Nine: Linking ERPs with Cognitive Processes
Read: ​Luck, Chapter 3 Supplementary Material

​To Do: Help with a big system testing session.

Discovery Questions:
1. What are meant by antecedents to ERP component generation?
2. What is a functional theory of an ERP component?
3. Summarize why functional theories of physiological measures difficult to test.
4. What is the problem of forward inference and why is it an issue when studying ERP components?

Week Ten: Basic Principles of ERP Recording
Read: Luck, Chapter 5

​To Do: Help with a big system testing session.

Discovery Questions:
1. Why is it important to collect clean data?
2. What is a potential difference?
3. What does the reference electrode do?
4. What does the ground electrode do?
5. What do active electrodes do?
6. Why do we re-reference EEG data offline? What types of references are used for this process?
7. What is impedance and why is it an issue when we record EEG data?
8. Briefly describe how an EEG amplifier works.
Week Eleven: More on the Basic Principles of ERP Recording
Read: Luck, Chapter 5 Supplementary Material

​To Do: Help with a big system testing session.

Discovery Questions:
1. How many electrodes do you actually need to collect ERP data? Why do we use 64 electrodes in the Krigolson Laboratory?
2. What are skin potentials and how do they impact experimental results?
Week Twelve: Exam
Review: All Discovery Questions

​To Do: Have Dr. Krigolson or a graduate student test you on the Discovery Questions
  • NEUROSCIENCE
    • NEUROSCIENCE 100 >
      • NEURO 100 INTRODUCTION
      • NEURO 101 ADVANCED
      • NEURO 102 AGING
      • NEURO 103 MEMORY
      • NEURO 104 DECISION MAKING
      • NEURO 105 LEARNING
      • Research Statistics
    • NRSC 500B / MEDS 470
  • Kinesiology
    • EPHE 245 >
      • LABORATORY
      • PRACTICUM
    • EPHE 357
  • STATISTICS
    • LECTURE >
      • INTRODUCTION TO R
      • DESCRIPTIVE STATISTICS
      • VISUALIZING DATA
      • Correlation and Regression
      • MULTIPLE REGRESSION
      • LOGIC OF NHST
      • T TESTS
      • ANOVA
      • POST HOC ANALYSIS
      • NON PARAMETRIC STATISTICS
      • FACTORIAL ANOVA
      • Repeated Measures ANOVA
      • Mixed ANOVA
      • MULTIVARIATE ANOVA
      • THE NEW STATISTICS
      • Bayesian Methods
    • ASSIGNMENTS >
      • Introduction to R >
        • INTRODUCTION TO R
        • LOADING DATA
        • DATA TABLES
      • Descriptive Statistics >
        • Mean, Median, and Mode
        • VARIANCE
        • CONFIDENCE INTERVALS
        • SHORTCUTS
      • Visualizing Data >
        • PLOTTING BASICS
        • BAR GRAPHS
        • BOXPLOTS
        • HISTOGRAMS
        • USING GGPLOT I
        • USING GGPLOT II
        • USING GGPLOT III
      • Correlation and Regression >
        • CORRELATION
        • REGRESSION
      • MULTIPLE REGRESSION >
        • MULTIPLE REGRESSION
      • Logic of NHST >
        • Sample Size and Variance
        • DISTRIBUTIONS
        • TESTING DISTRIBUTIONS
      • T-Tests >
        • Single Sample TTests
        • Paired Sample TTests
        • Independent Sample TTests
      • ANOVA >
        • ANOVA ASSUMPTIONS
        • ANOVA
      • POST HOC ANALYSIS >
        • POSTHOC ANALYSIS
      • NON PARAMETRIC STATISTICS >
        • WILCOXON TEST
        • WILCOXON SIGNED TEST
        • MULTIPLE GROUPS
      • FACTORIAL ANOVA
      • REPEATED MEASURES ANOVA >
        • RM ANOVA
        • TREND ANALYSIS
      • MIXED ANOVA
      • MULTIVARIATE ANOVA
      • THE NEW STATISTICS
      • BAYESIAN TTESTS
    • RESOURCES
    • R TIPS
  • Directed Studies
    • Advanced Topics in Motor Control A
    • Advanced Topics in Motor Control B
    • An Introduction to EEG
    • Advanced EEG and ERP Methods
    • Neural Correlates of Human Reward Processing
    • Independent Research Project
  • MATLAB
    • THE BASICS >
      • Hello World
      • BASIC MATHEMATICS
      • VARIABLES
      • Matrices
      • Writing Scripts
      • PATHS AND DIRECTORIES
      • USER INPUT
      • FOR LOOPS
      • WHILE LOOPS
      • IF STATEMENTS
      • RANDOM NUMBERS
    • STATISTICS >
      • LOADING DATA
      • DESCRIPTIVE STATISTICS
      • MAKING FUNCTIONS
      • BAR GRAPHS
      • LINE GRAPHS
      • TTESTS
    • EXPERIMENTS: THE BASICS >
      • DRAWING A CIRCLE
      • DRAWING MULTIPLE OBJECTS
      • DRAWING TEXT
      • DRAWING AN IMAGE
      • PLAYING A TONE
      • KEYBOARD INPUT
      • BUILDING A TRIAL
      • BUILDING TRIALS
      • NESTED LOOPS
      • RIGHT OR WRONG
      • SAVING DATA
    • EXPERIMENTS: ADVANCED >
      • STROOP
      • N BACK
      • Oddball
      • Animation
      • VIDEO
    • EEG and ERP Analysis >
      • ERP Analysis
  • RESOURCES
    • EXCEL
    • HOW TO READ A RESEARCH PAPER
    • HOW TO WRITE A RESEARCH PAPER
  • Workshops
    • Iowa State EEG Workshop 2018
  • Python
    • The Basics >
      • Setting Up Python
      • Hello, world!
      • Basic Math & Using Import
      • Variables
      • Matrices
      • Scripts
      • User Input
      • For Loops