Analyzing neural time series data : theory and practice / Mike X. Cohen.

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Bibliographic Details
Published:	Cambridge, Massachusetts : The MIT Press, [2014]
Online Access:	Connect to electronic resource
Main Author:	Cohen, Mike X., 1979- (Author)
Series:	Issues in clinical and cognitive neuropsychology
Subjects:	Neural networks (Neurobiology) Neural networks (Computer science) Computational neuroscience. Artificial intelligence > Biological applications.
Format:	Electronic eBook

Analyzing neural time series data : theory and practice /

Table of Contents:

pt. I Introduction
1. The Purpose of This Book, Who Should Read It, and How to Use It
1.1. What Is Cognitive Electrophysiology?
1.2. What Is the Purpose of This Book?
1.3. Why Shouldn't You Use <Insert Name of M/EEG Software Analysis Package>?
1.4. Why Program Analyses, and Why in Matlab?
1.5. How Best to Learn from and Use This Book
1.6. Sample Data and Online Code
1.7. Terminology Used in This Book
1.8. Exercises
1.9. Is Everything There Is to Know about EEG Analyses in This Book?
1.10. Who Should Read This Book?
1.11. Is This Book Difficult?
1.12. Questions?
2. Advantages and Limitations of Time and Time-Frequency-Domain Analyses
2.1. Why EEG?
2.2. Why Not EEG?
2.3. Interpreting Voltage Values from the EEG Signal
2.4. Advantages of Event-Related Potentials
2.5. Limitations of ERPs
2.6. Advantages of Time-Frequency-Based Approaches
2.7. Limitations of Time-Frequency-Based Approaches.; 2.8. Temporal Resolution, Precision, and Accuracy of EEG
2.9. Spatial Resolution, Precision, and Accuracy of EEG
2.10. Topographical Localization versus Brain Localization
2.11. EEG or MEG?
2.12. Costs of EEG Research
3. Interpreting and Asking Questions about Time-Frequency Results
3.1. EEG Time-Frequency: The Basics
3.2. Ways to View Time-Frequency Results
3.3. Tfviewerx and erpviewerx
3.4. How to View and Interpret Time-Frequency Results
3.5. Things to Be Suspicious of When Viewing Time-Frequency Results
3.6. Do Results in Time-Frequency Plots Mean That There Were Neural Oscillations?
4. Introduction to Matlab Programming
4.1. Write Clean and Efficient Code
4.2. Use Meaningful File and Variable Names
4.3. Make Regular Backups of Your Code and Keep Original Copies of Modified Code
4.4. Initialize Variables
4.5. Help!
4.6. Be Patient and Embrace the Learning Experience
4.7. Exercises.; 5. Introduction to the Physiological Bases of EEG
5.1. Biophysical Events That Are Measurable with EEG
5.2. Neurobiological Mechanisms of Oscillations
5.3. Phase-Locked, Time-Locked, Task-Related
5.4. Neurophysiological Mechanisms of ERPs
5.5. Are Electrical Fields Causally Involved in Cognition?
5.6. What if Electrical Fields Are Not Causally Involved in Cognition?
6. Practicalities of EEG Measurement and Experiment Design
6.1. Designing Experiments: Discuss, Pilot, Discuss, Pilot
6.2. Event Markers
6.3. Intra- and Intertrial Timing
6.4. How Many Trials You Will Need
6.5. How Many Electrodes You Will Need
6.6. Which Sampling Rate to Use When Recording Data
6.7. Other Optional Equipment to Consider
pt. II Preprocessing and Time-Domain Analyses
7. Preprocessing Steps Necessary and Useful for Advanced Data Analysis
7.1. What Is Preprocessing?
7.2. The Balance between Signal and Noise
7.3. Creating Epochs.; 7.4. Matching Trial Count across Conditions
7.5. Filtering
7.6. Trial Rejection
7.7. Spatial Filtering
7.8. Referencing
7.9. Interpolating Bad Electrodes
7.10. Start with Clean Data
8. EEG Artifacts: Their Detection, Influence, and Removal
8.1. Removing Data Based on Independent Components Analysis
8.2. Removing Trials because of Blinks
8.3. Removing Trials because of Oculomotor Activity
8.4. Removing Trials Based on EMG in EEG Channels
8.5. Removing Trials Based on Task Performance
8.6. Removing Trials Based on Response Hand EMG
8.7. Train Subjects to Minimize Artifacts
8.8. Minimize Artifacts during Data Collection
9. Overview of Time-Domain EEG Analyses
9.1. Event-Related Potentials
9.2. Filtering ERPs
9.3. Butterfly Plots and Global Field Power/Topographical Variance Plots
9.4. The Flicker Effect
9.5. Topographical Maps
9.6. Microstates
9.7. ERP Images
9.8. Exercises.; pt. III Frequency and Time-Frequency Domains Analyses
10. The Dot Product and Convolution
10.1. Dot Product
10.2. Convolution
10.3. How Does Convolution Work?
10.4. Convolution versus Cross-Covariance
10.5. The Purpose of Convolution for EEG Data Analyses
10.6. Exercises
11. The Discrete Time Fourier Transform, the FFT, and the Convolution Theorem
11.1. Making Waves
11.2. Finding Waves in EEG Data with the Fourier Transform
11.3. The Discrete Time Fourier Transform
11.4. Visualizing the Results of a Fourier Transform
11.5.Complex Results and Negative Frequencies
11.6. Inverse Fourier Transform
11.7. The Fast Fourier Transform
11.8. Stationarity and the Fourier Transform
11.9. Extracting More or Fewer Frequencies than Data Points
11.10. The Convolution Theorem
11.11. Tips for Performing FFT-Based Convolution in Matlab
11.12. Exercises
12. Morlet Wavelets and Wavelet Convolution
12.1. Why Wavelets?; 12.2. How to Make Wavelets
12.3. Wavelet Convolution as a Bandpass Filter
12.4. Limitations of Wavelet Convolution as Discussed Thus Far
12.5. Exercises
13.Complex Morlet Wavelets and Extracting Power and Phase
13.1. The Wavelet Complex
13.2. Imagining the Imaginary
13.3. Rectangular and Polar Notation and the Complex Plane
13.4. Euler's Formula
13.5. Euler's Formula and the Result of Complex Wavelet Convolution
13.6. From Time Point to Time Series
13.7. Parameters of Wavelets and Recommended Settings
13.8. Determining the Frequency Smoothing of Wavelets
13.9. Tips for Writing Efficient Convolution Code in Matlab
13.10. Describing This Analysis in Your Methods Section
13.11. Exercises
14. Bandpass Filtering and the Hilbert Transform
14.1. Hilbert Transform
14.2. Filtering Data before Applying the Hilbert Transform
14.3. Finite versus Infinite Impulse Response Filters
14.4. Bandpass, Band-Stop, High-Pass, Low-Pass.; 14.5. Constructing a Filter
14.6. Check Your Filters
14.7. Applying the Filter to Data
14.8. Butterworth (IIR) Filter
14.9. Filtering Each Trial versus Filtering Concatenated Trials
14.10. Multiple Frequencies
14.11.A World of Filters
14.12. Describing This Analysis in Your Methods Section
14.13. Exercises
15. Short-Time FFT
15.1. How the Short-Time FFT Works
15.2. Taper the Time Series
15.3. Time Segment Lengths and Overlap
15.4. Power and Phase
15.5. Describing This Analysis in Your Methods Section
15.6. Exercises
16. Multitapers
16.1. How the Multitaper Method Works
16.2. The Tapers
16.3. When You Should and Should Not Use Multitapers
16.4. The Multitaper Framework and Advanced Topics
16.5. Describing This Analysis in Your Methods Section
16.6. Exercises
17. Less Commonly Used Time-Frequency Decomposition Methods
17.1. Autoregressive Modeling
17.2. Hilbert-Huang (Empirical Mode Decomposition).; 17.3. Matching Pursuit
17.4.P-Episode
17.5.S-Transform
18. Time-Frequency Power and Baseline Normalizations
18.1.1/f Power Scaling
18.2. The Solution to 1/f Power in Task Designs
18.3. Decibel Conversion
18.4. Percentage Change and Baseline Division
18.5.Z-Transform
18.6. Not All Transforms Are Equal
18.7. Other Transforms
18.8. Mean versus Median
18.9. Single-Trial Baseline Normalization
18.10. The Choice of Baseline Time Window
18.11. Disadvantages of Baseline-Normalized Power
18.12. Signal-to-Noise Estimates
18.13. Number of Trials and Power Estimates
18.14. Downsampling Results after Analyses
18.15. Describing This Analysis in Your Methods Section
18.16. Exercises
19. Intertrial Phase Clustering
19.1. Why Phase Values Cannot Be Averaged
19.2. Intertrial Phase Clustering
19.3. Strength in Numbers
19.4. Using ITPC When There Are Few Trials or Condition Differences in Trial Count.; 19.5. Effects of Temporal Jitter on ITPC and Power
19.6. ITPC and Power
19.7. Weighted ITPC
19.8. Multimodal Phase Distributions
19.9. Spike-Field Coherence
19.10. Describing This Analysis in Your Methods Section
19.11. Exercises
20. Differences among Total, Phase-Locked, and Non-Phase-Locked Power and Intertrial Phase Consistency
20.1. Total Power
20.2. Non-Phase-Locked Power
20.3. P

pt. I Introduction
1. The Purpose of This Book, Who Should Read It, and How to Use It
1.1. What Is Cognitive Electrophysiology?
1.2. What Is the Purpose of This Book?
1.3. Why Shouldn't You Use <Insert Name of M/EEG Software Analysis Package>?
1.4. Why Program Analyses, and Why in Matlab?
1.5. How Best to Learn from and Use This Book
1.6. Sample Data and Online Code
1.7. Terminology Used in This Book
1.8. Exercises
1.9. Is Everything There Is to Know about EEG Analyses in This Book?
1.10. Who Should Read This Book?
1.11. Is This Book Difficult?
1.12. Questions?
2. Advantages and Limitations of Time and Time-Frequency-Domain Analyses
2.1. Why EEG?
2.2. Why Not EEG?
2.3. Interpreting Voltage Values from the EEG Signal
2.4. Advantages of Event-Related Potentials
2.5. Limitations of ERPs
2.6. Advantages of Time-Frequency-Based Approaches
2.7. Limitations of Time-Frequency-Based Approaches.

2.8. Temporal Resolution, Precision, and Accuracy of EEG
2.9. Spatial Resolution, Precision, and Accuracy of EEG
2.10. Topographical Localization versus Brain Localization
2.11. EEG or MEG?
2.12. Costs of EEG Research
3. Interpreting and Asking Questions about Time-Frequency Results
3.1. EEG Time-Frequency: The Basics
3.2. Ways to View Time-Frequency Results
3.3. Tfviewerx and erpviewerx
3.4. How to View and Interpret Time-Frequency Results
3.5. Things to Be Suspicious of When Viewing Time-Frequency Results
3.6. Do Results in Time-Frequency Plots Mean That There Were Neural Oscillations?
4. Introduction to Matlab Programming
4.1. Write Clean and Efficient Code
4.2. Use Meaningful File and Variable Names
4.3. Make Regular Backups of Your Code and Keep Original Copies of Modified Code
4.4. Initialize Variables
4.5. Help!
4.6. Be Patient and Embrace the Learning Experience
4.7. Exercises.

5. Introduction to the Physiological Bases of EEG
5.1. Biophysical Events That Are Measurable with EEG
5.2. Neurobiological Mechanisms of Oscillations
5.3. Phase-Locked, Time-Locked, Task-Related
5.4. Neurophysiological Mechanisms of ERPs
5.5. Are Electrical Fields Causally Involved in Cognition?
5.6. What if Electrical Fields Are Not Causally Involved in Cognition?
6. Practicalities of EEG Measurement and Experiment Design
6.1. Designing Experiments: Discuss, Pilot, Discuss, Pilot
6.2. Event Markers
6.3. Intra- and Intertrial Timing
6.4. How Many Trials You Will Need
6.5. How Many Electrodes You Will Need
6.6. Which Sampling Rate to Use When Recording Data
6.7. Other Optional Equipment to Consider
pt. II Preprocessing and Time-Domain Analyses
7. Preprocessing Steps Necessary and Useful for Advanced Data Analysis
7.1. What Is Preprocessing?
7.2. The Balance between Signal and Noise
7.3. Creating Epochs.

7.4. Matching Trial Count across Conditions
7.5. Filtering
7.6. Trial Rejection
7.7. Spatial Filtering
7.8. Referencing
7.9. Interpolating Bad Electrodes
7.10. Start with Clean Data
8. EEG Artifacts: Their Detection, Influence, and Removal
8.1. Removing Data Based on Independent Components Analysis
8.2. Removing Trials because of Blinks
8.3. Removing Trials because of Oculomotor Activity
8.4. Removing Trials Based on EMG in EEG Channels
8.5. Removing Trials Based on Task Performance
8.6. Removing Trials Based on Response Hand EMG
8.7. Train Subjects to Minimize Artifacts
8.8. Minimize Artifacts during Data Collection
9. Overview of Time-Domain EEG Analyses
9.1. Event-Related Potentials
9.2. Filtering ERPs
9.3. Butterfly Plots and Global Field Power/Topographical Variance Plots
9.4. The Flicker Effect
9.5. Topographical Maps
9.6. Microstates
9.7. ERP Images
9.8. Exercises.

pt. III Frequency and Time-Frequency Domains Analyses
10. The Dot Product and Convolution
10.1. Dot Product
10.2. Convolution
10.3. How Does Convolution Work?
10.4. Convolution versus Cross-Covariance
10.5. The Purpose of Convolution for EEG Data Analyses
10.6. Exercises
11. The Discrete Time Fourier Transform, the FFT, and the Convolution Theorem
11.1. Making Waves
11.2. Finding Waves in EEG Data with the Fourier Transform
11.3. The Discrete Time Fourier Transform
11.4. Visualizing the Results of a Fourier Transform
11.5.Complex Results and Negative Frequencies
11.6. Inverse Fourier Transform
11.7. The Fast Fourier Transform
11.8. Stationarity and the Fourier Transform
11.9. Extracting More or Fewer Frequencies than Data Points
11.10. The Convolution Theorem
11.11. Tips for Performing FFT-Based Convolution in Matlab
11.12. Exercises
12. Morlet Wavelets and Wavelet Convolution
12.1. Why Wavelets?

12.2. How to Make Wavelets
12.3. Wavelet Convolution as a Bandpass Filter
12.4. Limitations of Wavelet Convolution as Discussed Thus Far
12.5. Exercises
13.Complex Morlet Wavelets and Extracting Power and Phase
13.1. The Wavelet Complex
13.2. Imagining the Imaginary
13.3. Rectangular and Polar Notation and the Complex Plane
13.4. Euler's Formula
13.5. Euler's Formula and the Result of Complex Wavelet Convolution
13.6. From Time Point to Time Series
13.7. Parameters of Wavelets and Recommended Settings
13.8. Determining the Frequency Smoothing of Wavelets
13.9. Tips for Writing Efficient Convolution Code in Matlab
13.10. Describing This Analysis in Your Methods Section
13.11. Exercises
14. Bandpass Filtering and the Hilbert Transform
14.1. Hilbert Transform
14.2. Filtering Data before Applying the Hilbert Transform
14.3. Finite versus Infinite Impulse Response Filters
14.4. Bandpass, Band-Stop, High-Pass, Low-Pass.

14.5. Constructing a Filter
14.6. Check Your Filters
14.7. Applying the Filter to Data
14.8. Butterworth (IIR) Filter
14.9. Filtering Each Trial versus Filtering Concatenated Trials
14.10. Multiple Frequencies
14.11.A World of Filters
14.12. Describing This Analysis in Your Methods Section
14.13. Exercises
15. Short-Time FFT
15.1. How the Short-Time FFT Works
15.2. Taper the Time Series
15.3. Time Segment Lengths and Overlap
15.4. Power and Phase
15.5. Describing This Analysis in Your Methods Section
15.6. Exercises
16. Multitapers
16.1. How the Multitaper Method Works
16.2. The Tapers
16.3. When You Should and Should Not Use Multitapers
16.4. The Multitaper Framework and Advanced Topics
16.5. Describing This Analysis in Your Methods Section
16.6. Exercises
17. Less Commonly Used Time-Frequency Decomposition Methods
17.1. Autoregressive Modeling
17.2. Hilbert-Huang (Empirical Mode Decomposition).

17.3. Matching Pursuit
17.4.P-Episode
17.5.S-Transform
18. Time-Frequency Power and Baseline Normalizations
18.1.1/f Power Scaling
18.2. The Solution to 1/f Power in Task Designs
18.3. Decibel Conversion
18.4. Percentage Change and Baseline Division
18.5.Z-Transform
18.6. Not All Transforms Are Equal
18.7. Other Transforms
18.8. Mean versus Median
18.9. Single-Trial Baseline Normalization
18.10. The Choice of Baseline Time Window
18.11. Disadvantages of Baseline-Normalized Power
18.12. Signal-to-Noise Estimates
18.13. Number of Trials and Power Estimates
18.14. Downsampling Results after Analyses
18.15. Describing This Analysis in Your Methods Section
18.16. Exercises
19. Intertrial Phase Clustering
19.1. Why Phase Values Cannot Be Averaged
19.2. Intertrial Phase Clustering
19.3. Strength in Numbers
19.4. Using ITPC When There Are Few Trials or Condition Differences in Trial Count.

19.5. Effects of Temporal Jitter on ITPC and Power
19.6. ITPC and Power
19.7. Weighted ITPC
19.8. Multimodal Phase Distributions
19.9. Spike-Field Coherence
19.10. Describing This Analysis in Your Methods Section
19.11. Exercises
20. Differences among Total, Phase-Locked, and Non-Phase-Locked Power and Intertrial Phase Consistency
20.1. Total Power
20.2. Non-Phase-Locked Power
20.3. Phase-Locked Power
20.4. ERP Time-Frequency Power
20.5. Intertrial Phase Clustering
20.6. When to Use What Approach
20.7. Exercise
21. Interpretations and Limitations of Time-Frequency Power and ITPC Analyses
21.1. Terminology
21.2. When to Use What Time-Frequency Decomposition Method
21.3. Interpreting Time-Frequency Power
21.4. Interpreting Time-Frequency Intertrial Phase Clustering
21.5. Limitations of Time-Frequency Power and Intertrial Phase Clustering
21.6. Do Time-Frequency Analyses Reveal Neural Oscillations?

pt. IV Spatial Filters
22. Surface Laplacian
22.1. What Is the Surface Laplacian?
22.2. Algorithms for Computing the Surface Laplacian for EEG Data
22.3. Surface Laplacian for Topographical Localization
22.4. Surface Laplacian for Connectivity Analyses
22.5. Surface Laplacian for Cleaning Topographical Noise
22.6. Describing This Analysis in Your Methods Section
22.7. Exercises
23. Principal Components Analysis
23.1. Purpose and Interpretations of Principal Components Analysis
23.2. How PCA Is Computed
23.3. Distinguishing Significant from Nonsignificant Components
23.4. Rotating PCA Solutions
23.5. Time-Resolved PCA
23.6. PCA with Time-Frequency Information
23.7. PCA across Conditions
23.8. Independent Components Analysis
23.9. Describing This Method in Your Methods Section
23.10. Exercises
24. Basics of Single-Dipole and Distributed-Source Imaging
24.1. The Forward Solution
24.2. The Inverse Problem.

24.3. Dipole Fitting
24.4. Nonadaptive Distributed-Source Imaging Methods
24.5. Adaptive Distributed-Source Imaging
24.6. Theoretical and Practical Limits of Spatial Precision and Resolution
pt. V Connectivity
25. Introduction to the Various Connectivity Analyses
25.1. Why Only Two Sites (Bivariate Connectivity)?
25.2. Important Concepts Related to Bivariate Connectivity
25.3. Which Measure of Connectivity Should Be Used?
25.4. Phase-Based Connectivity
25.5. Power-Based Connectivity
25.6. Granger Prediction
25.7. Mutual Information
25.8. Cross-Frequency Coupling
25.9. Graph Theory
25.10. Potential Confound of Volume Conduction
26. Phase-Based Connectivity
26.1. Terminology
26.2. ISPC over Time
26.3. ISPC-Trials
26.4. ISPC and the Number of Trials
26.5. Relation between ISPC and Power
26.6. Weighted ISPC-Trials
26.7. Spectral Coherence (Magnitude-Squared Coherence)
26.8. Phase Lag-Based Measures.

26.9. Which Measure of Phase Connectivity Should You Use?
26.10. Testing the Mean Phase Angle
26.11. Describing These Analyses in Your Methods Section
26.12. Exercises
27. Power-Based Connectivity
27.1. Spearman versus Pearson Coefficient for Power Correlations
27.2. Power Correlations over Time
27.3. Power Correlations over Trials
27.4. Partial Correlations
27.5. Matlab Programming Tips
27.6. Describing This Analysis in Your Methods Section
27.7. Exercises
28. Granger Prediction
28.1. Univariate Autoregression
28.2. Bivariate Autoregression
28.3. Autoregression Errors and Error Variances
28.4. Granger Prediction over Time
28.5. Model Order
28.6. Frequency Domain Granger Prediction
28.7. Time Series Covariance Stationarity
28.8. Baseline Normalization of Granger Prediction Results
28.9. Statistics
28.10. Additional Applications of Granger Prediction
28.11. Exercises
29. Mutual Information
29.1. Entropy.

29.2. How Many Histogram Bins to Use
29.3. Enjoy the Entropy
29.4. Joint Entropy
29.5. Mutual Information
29.6. Mutual Information and Amount of Data
29.7. Mutual Information with Noisy Data
29.8. Mutual Information over Time or over Trials
29.9. Mutual Information on Real Data
29.10. Mutual Information on Frequency-Band-Specific Data
29.11. Lagged Mutual Information
29.12. Statistics
29.13. More Information
29.14. Describing This Analysis in Your Methods Section
29.15. Exercises
30. Cross-Frequency Coupling
30.1. Visual Inspection of Cross-Frequency Coupling
30.2. Power-Power Correlations
30.3.A Priori Phase-Amplitude Coupling
30.4. Separating Task-Related Phase and Power Coactivations from Phase-Amplitude Coupling
30.5. Mixed A Priori/Exploratory Phase-Amplitude Coupling
30.6. Exploratory Phase-Amplitude Coupling
30.7. Notes about Phase-Amplitude Coupling
30.8. Phase-Phase Coupling.

30.9. Other Methods for Quantifying Cross-Frequency Coupling
30.10. Cross-Frequency Coupling over Time or over Trials
30.11. Describing This Analysis in Your Methods Section
30.12. Exercises
31. Graph Theory
31.1.Networks as Matrices and Graphs
31.2. Thresholding Connectivity Matrices
31.3. Connectivity Degree
31.3. Clustering Coefficient
31.4. Path Length
31.5. Small-World Networks
31.6. Statistics
31.7. How to Describe These Analyses in Your Paper
31.8. Exercises
pt. VI Statistical Analyses
32. Advantages and Limitations of Different Statistical Procedures
32.1. Are Statistics Necessary?
32.2. At What Level Should Statistics Be Performed?
32.3. What p-Value Should Be Used, and Should Multiple-Comparisons Corrections Be Applied?
32.4. Are p-Values the Only Statistical Metric?
32.5. Statistical Significance versus Practical Significance
32.6. Type I and Type II Errors.

32.7. What Kinds of Statistics Should Be Applied?
32.8. How to Combine Data across Subjects
33. Nonparametric Permutation Testing
33.1. Advantages of Nonparametric Permutation Testing
33.2. Creating a Null-Hypothesis Distribution
33.3. How Many Iterations Are Necessary for the Null-Hypothesis Distribution?
33.4. Determining Statistical Significance
33.5. Multiple Comparisons and Their Corrections
33.6. Correction for Multiple Comparisons Using Pixel-Based Statistics
33.7. Corrections for Multiple Comparisons Using Cluster-Based Statistics
33.8. False Discovery Rate for Multiple-Comparisons Correction
33.9. What Should Be Permuted?
33.10. Nonparametric Permutation Testing beyond Simple Bivariate Cases
33.11. Describing This Analysis in Your Methods Section
34. Within-Subject Statistical Analyses
34.1. Changes in Task-Related Power Compared to Baseline
34.2. Discrete Condition Differences in Power.

34.3. Continuous Relationship with Power: Single-Trial Correlations
34.4. Continuous Relationships with Power: Single-Trial Multiple Regression
34.5. Determining Statistical Significance of Phase-Based Data
34.6. Testing Preferred Phase Angle across Conditions
34.7. Testing the Statistical Significance of Correlation Coefficients
35. Group-Level Analyses
35.1. Avoid Circular Inferences
35.2. Group-Level Analysis Strategy 1: Test Each Pixel and Apply a Mapwise Threshold
35.3. Group-Level Analysis Strategy 2a: Time-Frequency Windows for Hypothesis-Driven Analyses
35.4. Group-Level Analysis Strategy 2b: Subject-Specific Time-Frequency Windows for Hypothesis-Driven Analyses
35.5. Determining How Many Subjects You Need for Group-Level Analyses
36. Recommendations for Reporting Results in Figures, Tables, and Text
36.1. Recommendation 1: One Figure, One Idea
36.2. Recommendation 2: Show Data.

36.3. Recommendation 3: Highlight Significant Effects Instead of Removing Nonsignificant Effects
36.4. Recommendation 4: Show Specificity (or Lack Thereof) in Frequency, Time, and Space
36.5. Recommendation 5: Use Color
36.6. Recommendation 6: Use Informative Figure Labels and Captions
36.7. Recommendation 7: Avoid Showing "Representative" Data
36.8.A Checklist for Making Figures
36.9. Tables
36.10. Reporting Results in the Results Section
pt. VII Conclusions and Future Directions
37. Recurring Themes in This Book and Some Personal Advice
37.1. Theme: Myriad Possible Analyses
37.2. Advice: Avoid the Paralysis of Analysis
37.3. Theme: You Don't Have to Program Your Own Analyses, but You Should Know How Analyses Work
37.4. Advice: If It Feels Wrong, It Probably Is
37.5. Advice: When in Doubt, Plot It Out
37.6. Advice: Know These Three Formulas like the Back of Your Hand
37.7. Theme: Connectivity over Trials or over Time.

37.8. Theme: Most Analysis Parameters Introduce Bias
37.9. Theme: Write a Clear Methods Section so Others Can Replicate Your Analyses
37.10. Theme: Use Descriptive and Appropriate Analysis Terms
37.11. Advice: Interpret Null Results Cautiously
37.12. Advice: Try Simulations but Also Trust Real Data
37.13. Advice: Trust Replications
37.14. Theme: Analyses Are Not Right or Wrong; They Are Appropriate or Inappropriate
37.15. Advice: Hypothesis Testing Is Good/Bad, and So Is Data-Driven Exploration
37.16. Advice: Find Something That Drives You and Study It
37.17. Cognitive Electrophysiology: The Art of Finding Anthills on Mountains
38. The Future of Cognitive Electrophysiology
38.1. Developments in Analysis Methods
38.2. Developments in Understanding the Neurophysiology of EEG
38.3. Developments in Experiment Design
38.4. Developments in Measurement Technology
38.5. The Role of the Body in Brain Function.

38.6. Determining Causality
38.7. Inferring Cognitive States from EEG Signatures: Inverse Inference
38.8. Tables of Activation
38.9. Disease Diagnosis and Predicting Treatment Course and Success
38.10. Clinical Relevance Is Not Necessary for the Advancement of Science
38.11. Replications
38.12. Double-Blind Review for Scientific Publications
38.13.?

Analyzing neural time series data : theory and practice / Mike X. Cohen.

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