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Applied Event-Related Potential Data Analysis

Nguồn ngoài

Applied Event-Related Potential Data Analysis

The purpose of this book is to provide you with a practical overview of the data processing operations that are used to extract the ERPs from the EEG. This includes preprocessing operations that reduce noise and artifacts, the signal averaging operation at the core of extracting the ERPs, and the postprocessing operations that are used to convert the ERP waveforms into a small set of variables for statistical analysis.

PsychologySocial Sciences
Tác giả
Steven J Luck
Nhà xuất bản
University of California, Davis
Nguồn
LibreTexts
Giấy phép
CC BY
Ngôn ngữ
EN
Danh mục
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Mục lục· 168 mục
  1. 01: First Steps
  2. 1.01: Getting Started
  3. 1.02: Installing the Software and Downloading the Data
  4. 1.03: Exercise- Loading an EEG File
  5. 1.04: Exercise - Viewing Continuous EEG Waveforms
  6. 1.05: Exercise - Viewing EEG Spectra
  7. 1.06: Exercise- Loading ERPs and Plotting ERP Waveforms
  8. 1.07: Exercise- Plotting ERP Scalp Maps
  9. 1.08: Finding the Right Routine in EEGLAB and ERPLAB
  10. 1.09: Understanding the Matlab Path
  11. 1.10: Key Takeaways and References
  12. 02: Processing the Data from One Participant in the ERP CORE N400 Experiment
  13. 2.01: Data For This Chapter
  14. 2.02: Design of the N400 Experiment
  15. 2.03: Exercise - Looking at the EEG and the Event Codes
  16. 2.04: Exercise- Filtering Out Low-Frequency Drifts from the EEG
  17. 2.05: Exercise- Creating an EventList
  18. 2.06: Exercise- Assigning Events to Bins with BINLISTER
  19. 2.07: Exercise- Epoching and Baseline Correction
  20. 2.08: Exercise- Artifact Detection
  21. 2.09: Exercise- Averaged ERPs
  22. 2.10: Exercise- Data Quality
  23. 2.11: Review of Processing Steps
  24. 2.12: A Simple Matlab Script
  25. 2.13: Key Takeaways and References
  26. 03: Processing Multiple Participants in the ERP CORE N400 Experiment
  27. 3.01: Data for This Chapter
  28. 3.02: Exercise- Preprocessing and Averaging the Data from 10 Participants
  29. 3.03: Exercise- Examining the Single-Participant ERPsets
  30. 3.04: Exercise- Bad Data
  31. 3.05: Exercise- Making a Grand Average
  32. 3.06: Exercise- Low-Pass Filtering
  33. 3.07: Exercise - Scoring N400 Amplitude
  34. 3.08: Exercise- Simple Statistical Analysis of N400 Data
  35. 3.09: Exercise- A More Complex Analysis
  36. 3.10: Exercise- ERP Channel Operations
  37. 3.11: Exercise- ERP Bin Operations
  38. 3.12: Review of Processing Steps
  39. 3.13: Matlab Scripts For This Chapter
  40. 3.14: Key Takeaways and References
  41. 04: Filtering the EEG and ERPs
  42. 4.01: Data for this Chapter
  43. 4.02: Classes of Filters
  44. 4.03: Exercise- Assessing the Frequency Content of the Noise
  45. 4.04: Exercise- Filtering the Artificial Waveforms
  46. 4.05: Exercise- The Impulse Response Function
  47. 4.06: Exercise- Applying the Impulse Response Function to a Series of Impulses
  48. 4.07: Background- Filtering with a Running Average
  49. 4.08: Background- Filtering with a Weighted Running Average
  50. 4.09: Exercise- Distortion of Onset and Offset Times by Low-Pass Filters
  51. 4.10: Exercise- High-Pass Filtering
  52. 4.11: Practical Advice
  53. 4.12: Exercise- Creating and Importing Artificial Waveforms
  54. 4.13: Matlab Script for this Chapter
  55. 4.14: Key Takeaways and References
  56. 05: Referencing and Other Channel Operations
  57. 5.01: Data for This Chapter
  58. 5.02: Background- Understanding Active Reference and Ground Electrodes
  59. 5.03: Exercise- Working with the Artificial Data
  60. 5.04: Exercise- Average Mastoids as the Reference
  61. 5.05: Exercise- Re-Referencing the N400 ERP CORE Data
  62. 5.06: Exercise- The Average Reference
  63. 5.07: What is the Best Reference Site
  64. 5.08: Exercise- Current Density
  65. 5.09: Exercise- Global Field Power
  66. 5.10: Exercise- Referencing the EEG Data from the ERP CORE N400 Experiment
  67. 5.11: Exercise- Other Common Re-Referencing Scenarios
  68. 5.12: Matlab Script For This Chapter
  69. 5.13: Key Takeaways and References
  70. 06: Assigning Events to Bins Averaging Baseline Correction and Assessing Data Quality
  71. 6.01: Data for This Chapter
  72. 6.02: Design of the ERP CORE Visual Oddball P3b Experiment
  73. 6.03: The Event Code Scheme
  74. 6.04: Overview of Bin Descriptor Files
  75. 6.05: Exercise - A Basic Assignment of Events to Bins
  76. 6.06: Exercise - Looking at the Averaged ERPs
  77. 6.07: Exercise - The Signal-to-Noise Ratio
  78. 6.08: Exercise - Response-Locked Averaging
  79. 6.09: Exercise - Comparing Correct and Error Trials
  80. 6.10: Exercise - Sequential Analysis of the P3b
  81. 6.11: Exercise - Combining Bins
  82. 6.12: Exercise - Overlap
  83. 6.13: Matlab Script For This Chapter
  84. 6.14: Key Takeaways and References
  85. 07: Inspecting the EEG and Interpolating Bad Channels
  86. 7.01: Data for This Chapter
  87. 7.02: Design of the Mismatch Negativity (MMN) Experiment
  88. 7.03: Video Demonstration- Performing an Initial Inspection of a Participants EEG
  89. 7.04: The Fundamental Goal of EEG Preprocessing
  90. 7.05: Background- Interpolating Bad Channels
  91. 7.06: Exercise- Interpolating Bad Channels
  92. 7.07: Matlab Script For This Chapter
  93. 7.08: Key Takeaways and References
  94. 08: Artifact Detection and Rejection
  95. 8.01: Data for This Chapter
  96. 8.02: Overview
  97. 8.03: Background- Why Do We Reject Artifacts
  98. 8.04: Background- The General Approach
  99. 8.05: Exercise- Simple Blink Detection
  100. 8.06: Exercise- Adjusting the Threshold
  101. 8.07: An Iterative Approach to Setting Parameters
  102. 8.08: Exercise- Data Quality and Confounds
  103. 8.09: Exercise- Better Blink Detection
  104. 8.10: Exercise- Detecting Eye Movements
  105. 8.11: Exercise- Deciding on a Threshold for Eye Movements
  106. 8.12: Exercise- Commonly Recorded Artifactual Potentials (C.R.A.P.)
  107. 8.13: Using Artifact Detection to Avoid Changes to Visual Inputs
  108. 8.14: The ERP CORE N2pc Experiment
  109. 8.15: Exercise- Visualizing the Eye Movements
  110. 8.16: Exercise- Using the Averaged HEOG to Visualize Consistent Eye Movements
  111. 8.17: Exercise- A Two-Stage Strategy for Eliminating Small But Consistent Eye Movements
  112. 8.18: Matlab Script For This Chapter
  113. 8.19: Key Takeaways and References
  114. 09: Artifact Correction with Independent Component Analysis
  115. 9.01: Data for this Chapter
  116. 9.02: Exercise- A First Pass at ICA-Based Blink Correction
  117. 9.03: Exercise- Evaluating the Impact of Artifact Correction
  118. 9.04: Background- A Quick Conceptual Overview of ICA
  119. 9.05: Exercise- Making ICA Work Better
  120. 9.06: Exercise- Transferring the Weights and Assessing the ICs
  121. 9.07: Exercise- Deciding Which ICs to Exclude
  122. 9.08: Exercise- Deleting C.R.A.P. Prior to ICA
  123. 9.09: General Recommendations
  124. 9.10: Matlab Scripts For This Chapter
  125. 9.11: Key Takeaways and References
  126. 10: Scoring and Statistical Analysis of ERP Amplitudes and Latencies
  127. 10.01: Data for This Chapter
  128. 10.02: Design of the Flankers Experiment
  129. 10.03: Exercise- Examining the Grand Averages
  130. 10.04: Exercise- A First Pass at Scoring and Statistical Analysis
  131. 10.05: Exercise- Simplifying the Statistical Analysis
  132. 10.06: Exercise- Peak Amplitude
  133. 10.07: Exercise- Peak Latency
  134. 10.08: Exercise- Fractional Area Latency
  135. 10.09: Exercise- Quantifying Onset Latency
  136. 10.10: Exercise- Collapsing Across Channels and Correlating Latencies with Response Times
  137. 10.11: Matlab Scripts For This Chapter
  138. 10.12: Key Takeaways and References
  139. 11: EEGLAB and ERPLAB Scripting
  140. 11.01: Data for This Chapter
  141. 11.02: Expected Background Knowledge
  142. 11.03: Bugs as an Opportunity for Growth
  143. 11.04: Design of the N170 Experiment
  144. 11.05: Exercise- The Matlab Command Line and the EEG Variable
  145. 11.06: Exercise- The ALLEEG Variable and Redrawing the GUI
  146. 11.07: Exercise- EEG.history and eegh
  147. 11.08: Exercise- From the Command Line to a Script
  148. 11.09: Exercise- Using a Variable for the Path
  149. 11.10: Exercise- Loops
  150. 11.11: Exercise- Looping Through Data from Multiple Participants
  151. 11.12: Rapid Cycling Between Coding and Testing
  152. 11.13: Exercise- Referencing with a Script
  153. 11.14: Exercise- Improving the Referencing Script
  154. 11.15: Exercise- Preprocessing the EEG and Using a Spreadsheet to Store Subject-Specific Information
  155. 11.16: Exercise- Building an Entire EEG Processing Pipeline
  156. 11.17: Exercise- Averaging with a Custom aSME Time Window
  157. 11.18: Exercise- Scoring Amplitudes and Latencies and Performing Statistical Analyses
  158. 11.19: Key Takeaways and References
  159. 12: Appendix 1: A Very Brief Introduction to EEG and ERPs
  160. 13: Appendix 2: Troubleshooting Guide
  161. 13.01: A2.1 The First Step
  162. 13.02: A2.2 Some Basic Solutions
  163. 13.03: A2.3 Taking a Scientific Approach
  164. 13.04: A2.4 Deciphering Matlabs Error Messages
  165. 13.05: A2.5 Debugging Scripts by Performing Experiments and Collecting Data
  166. 13.06: A2.6 Avoiding Bugs in Your Scripts with Good Programming Practices
  167. 13.07: A2.7 References
  168. 14: Appendix 3: Example Processing Pipeline