Detailed Licensing
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Title: Book: Applied Event-Related Potential Data Analysis (Luck)
Webpages: 182
All licenses found:
- CC BY 4.0: 98.4% (179 pages)
- Undeclared: 1.6% (3 pages)
By Page
- Book: Applied Event-Related Potential Data Analysis (Luck) —
CC BY 4.0
- Front Matter — CC BY 4.0
- 1: First Steps —
CC BY 4.0
- 1.1: Getting Started — CC BY 4.0
- 1.2: Installing the Software and Downloading the Data — CC BY 4.0
- 1.3: Exercise- Loading an EEG File — CC BY 4.0
- 1.4: Exercise - Viewing Continuous EEG Waveforms — CC BY 4.0
- 1.5: Exercise - Viewing EEG Spectra — CC BY 4.0
- 1.6: Exercise- Loading ERPs and Plotting ERP Waveforms — CC BY 4.0
- 1.7: Exercise- Plotting ERP Scalp Maps — CC BY 4.0
- 1.8: Finding the Right Routine in EEGLAB and ERPLAB — CC BY 4.0
- 1.9: Understanding the Matlab Path — CC BY 4.0
- 1.10: Key Takeaways and References — CC BY 4.0
- 2: Processing the Data from One Participant in the ERP CORE N400 Experiment —
CC BY 4.0
- 2.1: Data For This Chapter — CC BY 4.0
- 2.2: Design of the N400 Experiment — CC BY 4.0
- 2.3: Exercise - Looking at the EEG and the Event Codes — CC BY 4.0
- 2.4: Exercise- Filtering Out Low-Frequency Drifts from the EEG — CC BY 4.0
- 2.5: Exercise- Creating an EventList — CC BY 4.0
- 2.6: Exercise- Assigning Events to Bins with BINLISTER — CC BY 4.0
- 2.7: Exercise- Epoching and Baseline Correction — CC BY 4.0
- 2.8: Exercise- Artifact Detection — CC BY 4.0
- 2.9: Exercise- Averaged ERPs — CC BY 4.0
- 2.10: Exercise- Data Quality — CC BY 4.0
- 2.11: Review of Processing Steps — CC BY 4.0
- 2.12: A Simple Matlab Script — CC BY 4.0
- 2.13: Key Takeaways and References — CC BY 4.0
- 3: Processing Multiple Participants in the ERP CORE N400 Experiment —
CC BY 4.0
- 3.1: Data for This Chapter — CC BY 4.0
- 3.2: Exercise- Preprocessing and Averaging the Data from 10 Participants — CC BY 4.0
- 3.3: Exercise- Examining the Single-Participant ERPsets — CC BY 4.0
- 3.4: Exercise- “Bad” Data — CC BY 4.0
- 3.5: Exercise- Making a Grand Average — CC BY 4.0
- 3.6: Exercise- Low-Pass Filtering — CC BY 4.0
- 3.7: Exercise - Scoring N400 Amplitude — CC BY 4.0
- 3.8: Exercise- Simple Statistical Analysis of N400 Data — CC BY 4.0
- 3.9: Exercise- A More Complex Analysis — CC BY 4.0
- 3.10: Exercise- ERP Channel Operations — CC BY 4.0
- 3.11: Exercise- ERP Bin Operations — CC BY 4.0
- 3.12: Review of Processing Steps — CC BY 4.0
- 3.13: Matlab Scripts For This Chapter — CC BY 4.0
- 3.14: Key Takeaways and References — CC BY 4.0
- 4: Filtering the EEG and ERPs —
CC BY 4.0
- 4.1: Data for this Chapter — CC BY 4.0
- 4.2: Classes of Filters — CC BY 4.0
- 4.3: Exercise- Assessing the Frequency Content of the Noise — CC BY 4.0
- 4.4: Exercise- Filtering the Artificial Waveforms — CC BY 4.0
- 4.5: Exercise- The Impulse Response Function — CC BY 4.0
- 4.6: Exercise- Applying the Impulse Response Function to a Series of Impulses — CC BY 4.0
- 4.7: Background- Filtering with a Running Average — CC BY 4.0
- 4.8: Background- Filtering with a Weighted Running Average — CC BY 4.0
- 4.9: Exercise- Distortion of Onset and Offset Times by Low-Pass Filters — CC BY 4.0
- 4.10: Exercise- High-Pass Filtering — CC BY 4.0
- 4.11: Practical Advice — CC BY 4.0
- 4.12: Exercise- Creating and Importing Artificial Waveforms — CC BY 4.0
- 4.13: Matlab Script for this Chapter — CC BY 4.0
- 4.14: Key Takeaways and References — CC BY 4.0
- 5: Referencing and Other Channel Operations —
CC BY 4.0
- 5.1: Data for This Chapter — CC BY 4.0
- 5.2: Background- Understanding Active, Reference, and Ground Electrodes — CC BY 4.0
- 5.3: Exercise- Working with the Artificial Data — CC BY 4.0
- 5.4: Exercise- Average Mastoids as the Reference — CC BY 4.0
- 5.5: Exercise- Re-Referencing the N400 ERP CORE Data — CC BY 4.0
- 5.6: Exercise- The Average Reference — CC BY 4.0
- 5.7: What is the Best Reference Site? — CC BY 4.0
- 5.8: Exercise- Current Density — CC BY 4.0
- 5.9: Exercise- Global Field Power — CC BY 4.0
- 5.10: Exercise- Referencing the EEG Data from the ERP CORE N400 Experiment — CC BY 4.0
- 5.11: Exercise- Other Common Re-Referencing Scenarios — CC BY 4.0
- 5.12: Matlab Script For This Chapter — CC BY 4.0
- 5.13: Key Takeaways and References — CC BY 4.0
- 6: Assigning Events to Bins, Averaging, Baseline Correction, and Assessing Data Quality —
CC BY 4.0
- 6.1: Data for This Chapter — CC BY 4.0
- 6.2: Design of the ERP CORE Visual Oddball P3b Experiment — CC BY 4.0
- 6.3: The Event Code Scheme — CC BY 4.0
- 6.4: Overview of Bin Descriptor Files — CC BY 4.0
- 6.5: Exercise - A Basic Assignment of Events to Bins — CC BY 4.0
- 6.6: Exercise - Looking at the Averaged ERPs — CC BY 4.0
- 6.7: Exercise - The Signal-to-Noise Ratio — CC BY 4.0
- 6.8: Exercise - Response-Locked Averaging — CC BY 4.0
- 6.9: Exercise - Comparing Correct and Error Trials — CC BY 4.0
- 6.10: Exercise - Sequential Analysis of the P3b — CC BY 4.0
- 6.11: Exercise - Combining Bins — CC BY 4.0
- 6.12: Exercise - Overlap — CC BY 4.0
- 6.13: Matlab Script For This Chapter — CC BY 4.0
- 6.14: Key Takeaways and References — CC BY 4.0
- 7: Inspecting the EEG and Interpolating Bad Channels —
CC BY 4.0
- 7.1: Data for This Chapter — CC BY 4.0
- 7.2: Design of the Mismatch Negativity (MMN) Experiment — CC BY 4.0
- 7.3: Video Demonstration- Performing an Initial Inspection of a Participant’s EEG — CC BY 4.0
- 7.4: The Fundamental Goal of EEG Preprocessing — CC BY 4.0
- 7.5: Background- Interpolating Bad Channels — CC BY 4.0
- 7.6: Exercise - Interpolating Bad Channels — CC BY 4.0
- 7.7: Matlab Script For This Chapter — CC BY 4.0
- 7.8: Key Takeaways and References — CC BY 4.0
- 8: Artifact Detection and Rejection —
CC BY 4.0
- 8.1: Data for This Chapter — CC BY 4.0
- 8.2: Overview — CC BY 4.0
- 8.3: Background- Why Do We Reject Artifacts? — CC BY 4.0
- 8.4: Background- The General Approach — CC BY 4.0
- 8.5: Exercise- Simple Blink Detection — CC BY 4.0
- 8.6: Exercise- Adjusting the Threshold — CC BY 4.0
- 8.7: An Iterative Approach to Setting Parameters — CC BY 4.0
- 8.8: Exercise- Data Quality and Confounds — CC BY 4.0
- 8.9: Exercise- Better Blink Detection — CC BY 4.0
- 8.10: Exercise- Detecting Eye Movements — CC BY 4.0
- 8.11: Exercise- Deciding on a Threshold for Eye Movements — CC BY 4.0
- 8.12: Exercise- Commonly Recorded Artifactual Potentials (C.R.A.P.) — CC BY 4.0
- 8.13: Using Artifact Detection to Avoid Changes to Visual Inputs — CC BY 4.0
- 8.14: The ERP CORE N2pc Experiment — CC BY 4.0
- 8.15: Exercise- Visualizing the Eye Movements — CC BY 4.0
- 8.16: Exercise- Using the Averaged HEOG to Visualize Consistent Eye Movements — CC BY 4.0
- 8.17: Exercise- A Two-Stage Strategy for Eliminating Small But Consistent Eye Movements — CC BY 4.0
- 8.18: Matlab Script For This Chapter — CC BY 4.0
- 8.19: Key Takeaways and References — CC BY 4.0
- 9: Artifact Correction with Independent Component Analysis —
CC BY 4.0
- 9.1: Data for this Chapter — CC BY 4.0
- 9.2: Exercise- A First Pass at ICA-Based Blink Correction — CC BY 4.0
- 9.3: Exercise- Evaluating the Impact of Artifact Correction — CC BY 4.0
- 9.4: Background- A Quick Conceptual Overview of ICA — CC BY 4.0
- 9.5: Exercise- Making ICA Work Better — CC BY 4.0
- 9.6: Exercise- Transferring the Weights and Assessing the ICs — CC BY 4.0
- 9.7: Exercise- Deciding Which ICs to Exclude — CC BY 4.0
- 9.8: Exercise- Deleting C.R.A.P. Prior to ICA — CC BY 4.0
- 9.9: General Recommendations — CC BY 4.0
- 9.10: Matlab Scripts For This Chapter — CC BY 4.0
- 9.11: Key Takeaways and References — CC BY 4.0
- 10: Scoring and Statistical Analysis of ERP Amplitudes and Latencies —
CC BY 4.0
- 10.1: Data for This Chapter — CC BY 4.0
- 10.2: Design of the Flankers Experiment — CC BY 4.0
- 10.3: Exercise- Examining the Grand Averages — CC BY 4.0
- 10.4: Exercise- A First Pass at Scoring and Statistical Analysis — CC BY 4.0
- 10.5: Exercise- Simplifying the Statistical Analysis — CC BY 4.0
- 10.6: Exercise- Peak Amplitude — CC BY 4.0
- 10.7: Exercise- Peak Latency — CC BY 4.0
- 10.8: Exercise- Fractional Area Latency — CC BY 4.0
- 10.9: Exercise- Quantifying Onset Latency — CC BY 4.0
- 10.10: Exercise- Collapsing Across Channels and Correlating Latencies with Response Times — CC BY 4.0
- 10.11: Matlab Scripts For This Chapter — CC BY 4.0
- 10.12: Key Takeaways and References — CC BY 4.0
- 11: EEGLAB and ERPLAB Scripting —
CC BY 4.0
- 11.1: Data for This Chapter — CC BY 4.0
- 11.2: Expected Background Knowledge — CC BY 4.0
- 11.3: Bugs as an Opportunity for Growth — CC BY 4.0
- 11.4: Design of the N170 Experiment — CC BY 4.0
- 11.5: Exercise- The Matlab Command Line and the EEG Variable — CC BY 4.0
- 11.6: Exercise- The ALLEEG Variable and Redrawing the GUI — CC BY 4.0
- 11.7: Exercise- EEG.history and eegh — CC BY 4.0
- 11.8: Exercise- From the Command Line to a Script — CC BY 4.0
- 11.9: Exercise- Using a Variable for the Path — CC BY 4.0
- 11.10: Exercise- Loops — CC BY 4.0
- 11.11: Exercise- Looping Through Data from Multiple Participants — CC BY 4.0
- 11.12: Rapid Cycling Between Coding and Testing — CC BY 4.0
- 11.13: Exercise- Referencing with a Script — CC BY 4.0
- 11.14: Exercise- Improving the Referencing Script — CC BY 4.0
- 11.15: Exercise- Preprocessing the EEG and Using a Spreadsheet to Store Subject-Specific Information — CC BY 4.0
- 11.16: Exercise- Building an Entire EEG Processing Pipeline — CC BY 4.0
- 11.17: Exercise- Averaging with a Custom aSME Time Window — CC BY 4.0
- 11.18: Exercise- Scoring Amplitudes and Latencies and Performing Statistical Analyses — CC BY 4.0
- 11.19: Key Takeaways and References — CC BY 4.0
- 12: Appendix 1: A Very Brief Introduction to EEG and ERPs — CC BY 4.0
- 13: Appendix 2: Troubleshooting Guide —
CC BY 4.0
- 13.1: A2.1 The First Step — CC BY 4.0
- 13.2: A2.2 Some Basic Solutions — CC BY 4.0
- 13.3: A2.3 Taking a Scientific Approach — CC BY 4.0
- 13.4: A2.4 Deciphering Matlab’s Error Messages — CC BY 4.0
- 13.5: A2.5 Debugging Scripts by Performing Experiments and Collecting Data — CC BY 4.0
- 13.6: A2.6 Avoiding Bugs in Your Scripts with Good Programming Practices — CC BY 4.0
- 13.7: A2.7 References — CC BY 4.0
- 14: Appendix 3: Example Processing Pipeline — CC BY 4.0
- Back Matter — CC BY 4.0