Applied Event-Related Potential Data Analysis 2e

by Steven J Luck

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This book provides a practical overview of the analysis of event-related potentials (ERPs). It includes data processing operations that are used to preprocess the EEG, extract ERPs from the EEG, and conduct all subsequent processing steps.

Description from LibreTexts.

Publisher
University of California, Davis
Subjects
Social Sciences, Psychology
Licence
CC BY-NC 4.0, as LibreTexts states it. Non-commercial use only.
Who can read
Teens profiles and older, judged from its title and subjects

Contents

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  1. Front Matter
  2. TitlePage
  3. InfoPage
  4. Table of Contents
  5. Licensing
  6. Hardware and Software Requirements
  7. Preface
  8. Acknowledgments
  9. How to Cite This Book
  10. 1: First Steps
  11. 1.1: Getting Started
  12. 1.2: Installing the Software and Downloading the Data
  13. 1.3: Exercise- Loading and Viewing an EEG File
  14. 1.4: Exercise - Viewing EEG Spectra
  15. 1.5: Exercise- Loading and Plotting ERPs
  16. 1.6: Understanding the Matlab Path
  17. 1.7: Key Takeaways and References
  18. 2: Processing the Data from One Participant in the ERP CORE N400 Experiment
  19. 2.1: Data For This Chapter
  20. 2.2: Design of the N400 Experiment
  21. 2.3: Exercise - Looking at the EEG and the Event Codes
  22. 2.4: Exercise- Filtering Out Low-Frequency Drifts from the EEG
  23. 2.5: Exercise- Creating an EventList
  24. 2.6: Exercise- Assigning Events to Bins with BINLISTER
  25. 2.7: Exercise- Epoching and Baseline Correction
  26. 2.8: Exercise- Artifact Detection
  27. 2.9: Exercise- Averaged ERPs
  28. 2.10: Exercise- Data Quality
  29. 2.11: Review of Processing Steps
  30. 2.12: A Simple Matlab Script
  31. 2.13: Key Takeaways and References
  32. 3: Processing Multiple Participants in the ERP CORE N400 Experiment
  33. 3.1: Data for This Chapter
  34. 3.2: Exercise- Organizing and Loading the Data from 10 Participants
  35. 3.3: Exercise- Examining the Single-Participant ERPsets
  36. 3.4: Exercise- “Bad” Data
  37. 3.5: Exercise- Making a Grand Average
  38. 3.6: Exercise- Low-Pass Filtering
  39. 3.7: Exercise - Scoring N400 Amplitude
  40. 3.8: Exercise- Simple Statistical Analysis of N400 Data
  41. 3.9: Exercise- A More Complex Analysis
  42. 3.10: Exercise- ERP Channel Operations
  43. 3.11: Exercise- ERP Bin Operations
  44. 3.12: Review of Processing Steps
  45. 3.13: Matlab Scripts For This Chapter
  46. 3.14: Key Takeaways and References
  47. 4: Filtering the EEG and ERPs
  48. 4.1: Data for this Chapter
  49. 4.2: Classes of Filters
  50. 4.3: Exercise- Assessing the Frequency Content of the Noise
  51. 4.4: Exercise- Filtering the 60 Hz Noise
  52. 4.5: Practical Advice About Filtering
  53. 4.6: Exercise- Filtering as a Linear Operation
  54. 4.7: Exercise- The Impulse Response Function
  55. 4.8: Exercise- Filtering a Series of Impulses (and a Sneaky Introduction to Convolution)
  56. 4.9: Predicting Waveform Distortion from the Impulse Response Function
  57. 4.10: Exercise- High-Pass Filtering
  58. 4.11: Background- How Should We Define the Optimal Filter?
  59. 4.12: Background- Quantifying Signal, Noise, and Waveform Distortion
  60. 4.13: Exercise- Creating artificial waveforms
  61. 4.14: Exercise- Quantifying Waveform Distortion in the Artificial Waveforms
  62. 4.15: Exercise- Quantifying Data Quality
  63. 4.16: Selecting the Optimal Filter
  64. 4.17: Matlab Scripts for this Chapter
  65. 4.18: Key Takeaways and References
  66. 5: Referencing and Other Channel Operations
  67. 5.1: Data for This Chapter
  68. 5.2: Background- Understanding Active, Reference, and Ground Electrodes
  69. 5.3: Exercise- Working with the Artificial Data
  70. 5.4: Exercise- Average Mastoids as the Reference
  71. 5.5: Exercise- Re-Referencing the N400 ERP CORE Data
  72. 5.6: Exercise- The Average Reference
  73. 5.7: What is the Best Reference Site?
  74. 5.8: Exercise- Current Density
  75. 5.9: Exercise- Global Field Power
  76. 5.10: Exercise- Referencing the EEG Data from the ERP CORE N400 Experiment
  77. 5.11: Exercise- Other Common Re-Referencing Scenarios
  78. 5.12: Matlab Script For This Chapter
  79. 5.13: Key Takeaways and References
  80. 6: Assigning Events to Bins, Averaging, Baseline Correction, and Assessing Data Quality
  81. 6.1: Data for This Chapter
  82. 6.2: Design of the ERP CORE Visual Oddball P3b Experiment
  83. 6.3: The Event Code Scheme
  84. 6.4: Overview of Bin Descriptor Files
  85. 6.5: Exercise - A Basic Assignment of Events to Bins
  86. 6.6: Exercise - Looking at the Averaged ERPs
  87. 6.7: Exercise - The Signal-to-Noise Ratio
  88. 6.8: Exercise - Response-Locked Averaging
  89. 6.9: Exercise - Comparing Correct and Error Trials to See the Error-Related Negativity
  90. 6.10: Exercise - Sequential Analysis of the P3b
  91. 6.11: Exercise - Combining Bins
  92. 6.12: Exercise - Overlap
  93. 6.13: Matlab Script For This Chapter
  94. 6.14: Key Takeaways and References
  95. 7: Inspecting the EEG and Interpolating Bad Channels
  96. 7.1: Data for This Chapter
  97. 7.2: Design of the Mismatch Negativity (MMN) Experiment
  98. 7.3: Video Demonstration- Performing an Initial Inspection of a Participant’s EEG
  99. 7.4: Background- Interpolating Bad Channels
  100. 7.5: Exercise - Interpolating Bad Channels
  101. 7.6: Matlab Script For This Chapter
  102. 7.7: Key Takeaways and References
  103. 8: Artifact Detection and Rejection
  104. 8.1: Data for This Chapter
  105. 8.2: Overview
  106. 8.3: Background- Why Do We Reject Artifacts?
  107. 8.4: Background- The General Approach
  108. 8.5: Exercise- Simple Blink Detection
  109. 8.6: Exercise- Adjusting the Threshold
  110. 8.7: Exercise- Manually Marking and Unmarking Artifacts
  111. 8.8: An Iterative Approach to Setting Parameters
  112. 8.9: Exercise- Effects of Artifact Rejection on Data Quality
  113. 8.10: Exercise- Better Blink Detection
  114. 8.11: Exercise- Commonly Recorded Artifactual Potentials (C.R.A.P.)
  115. 8.12: Exercise- Detecting Eye Movements
  116. 8.13: Using Artifact Detection to Avoid Changes to Visual Inputs
  117. 8.14: The ERP CORE N2pc Experiment
  118. 8.15: Exercise- Visualizing the Eye Movements
  119. 8.16: Exercise- Using the Averaged HEOG to Visualize Consistent Eye Movements
  120. 8.17: Exercise- A Two-Stage Strategy for Eliminating Small But Consistent Eye Movements
  121. 8.18: Matlab Script For This Chapter
  122. 8.19: Key Takeaways and References
  123. 9: Artifact Correction with Independent Component Analysis
  124. 9.1: Data for this Chapter
  125. 9.2: Exercise- A First Pass at ICA-Based Blink Correction
  126. 9.3: Background- A Quick Conceptual Overview of ICA
  127. 9.4: Exercise- Making ICA Work Better
  128. 9.5: Exercise- Transferring the Weights, Identifying the ICs, Correcting the EEG, and Interpolating
  129. 9.6: Exercise- Deleting C.R.A.P. Prior to ICA
  130. 9.7: General Recommendations for ICA-Based Artifact Correction
  131. 9.8: Background- Evaluating the Effectiveness of Artifact Correction+Rejection
  132. 9.9: Exercise- Determining Whether Blinking is a Confound
  133. 9.10: Exercise- Was Blink Correction Effective?
  134. 9.11: Exercise- Does Artifact Correction+Rejection Improve Data Quality?
  135. 9.12: Matlab Scripts For This Chapter
  136. 9.13: Key Takeaways and References
  137. 10: Scoring and Statistical Analysis of ERP Amplitudes and Latencies
  138. 10.1: Data for This Chapter
  139. 10.2: Design of the Flankers Experiment
  140. 10.3: Exercise- Examining the Grand Averages
  141. 10.4: Exercise- A First Pass at Scoring and Statistical Analysis
  142. 10.5: Exercise- Simplifying the Statistical Analysis
  143. 10.6: Exercise- Peak Amplitude
  144. 10.7: Exercise- Peak Latency
  145. 10.8: Exercise- Fractional Area Latency
  146. 10.9: Exercise- Quantifying Onset Latency
  147. 10.10: Exercise- Collapsing Across Channels and Correlating Latencies with Response Times
  148. 10.11: Matlab Scripts For This Chapter
  149. 10.12: Key Takeaways and References
  150. 11: EEGLAB and ERPLAB Scripting
  151. 11.1: Data for This Chapter
  152. 11.2: Expected Background Knowledge
  153. 11.3: Bugs as an Opportunity for Growth
  154. 11.4: Design of the N170 Experiment
  155. 11.5: Exercise- The Matlab Command Line and the EEG Variable
  156. 11.6: Exercise- The ALLEEG Variable and Refreshing the ERPLAB Studio GUI
  157. 11.7: Exercise- EEG.history and eegh
  158. 11.8: Exercise- From the Command Line to a Script
  159. 11.9: Exercise- Using a Variable for the Path
  160. 11.10: Exercise- Loops
  161. 11.11: Exercise- Looping Through Data from Multiple Participants
  162. 11.12: Rapid Cycling Between Coding and Testing
  163. 11.13: Exercise- Using a Script to Reference EEGsets
  164. 11.14: Exercise- Improving the Script and Adding 3D Channel Locations
  165. 11.15: Exercise- Preprocessing the EEG and Using a Spreadsheet to Store Subject-Specific Information
  166. 11.16: Exercise- Building an Entire EEG Processing Pipeline
  167. 11.17: Exercise- Averaging, Quantifying Data Quality, and Plotting
  168. 11.18: Exercise- Scoring Amplitudes and Latencies and Performing Statistical Analyses
  169. 11.19: Key Takeaways and References
  170. 12: Multivariate Pattern Classification (Decoding)
  171. 12.1: Data for This Chapter
  172. 12.2: Example- Decoding Face Identity and Emotional Expression
  173. 12.3: The Essence of Decoding
  174. 12.4: Exercise- Binary Decoding of ID 1 versus ID 2
  175. 12.5: Exercise- Decoding Multiple BESTsets and Making a Grand Average
  176. 12.6: Exercise- 4-way Decoding of Face Identity and Emotional Expression
  177. 12.7: Exercise- Exporting the Data for Statistical Analysis
  178. 12.8: Exercise- Using Decoding in Place of Conventional Mean Amplitude Analyses
  179. 12.9: Exercise- Temporal Generalization
  180. 12.10: Demonstration- Decoding in the Frequency-Domain
  181. 12.11: Exercise- Artificially Inflated Decoding with Unbalanced Classes
  182. 12.12: Exercise- Methods for Appropriate Decoding with Unbalanced Classes
  183. 12.13: Exercise- Using Area Under the Curve (AUC) with the ERP CORE P3b Oddball Paradigm
  184. 12.14: Matlab Script For This Chapter
  185. 12.15: Key Takeaways and References
  186. 13: Appendix 1- A Very Brief Introduction to EEG and ERPs
  187. 14: Appendix 2- Troubleshooting Guide
  188. 14.1: Data for This Appendix
  189. 14.2: A2.1 The First Step
  190. 14.3: A2.2 Some Basic Solutions
  191. 14.4: A2.3 Taking a Scientific Approach
  192. 14.5: A2.4 Deciphering Matlab’s Error Messages
  193. 14.6: A2.5 Debugging Scripts by Performing Experiments and Collecting Data
  194. 14.7: A2.6 Avoiding Bugs in Your Scripts with Good Programming Practices
  195. 14.8: A2.7 References
  196. 15: Appendix 3- Example Processing Pipeline
  197. Back Matter
  198. Index
  199. Glossary
  200. Detailed Licensing