Analyzing neural time series data : theory and practice /

"This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG),...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Cohen, Mike X., 1979- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, Massachusetts : The MIT Press, [2014]
Colección:Issues in clinical and cognitive neuropsychology.
Temas:
Neural networks (Neurobiology)
Neural networks (Computer science)
Computational neuroscience.
Artificial intelligence > Biological applications.
Neural circuitry.
Nerve Net
Neural Networks, Computer
Réseaux neuronaux (Neurobiologie)
Réseaux neuronaux (Informatique)
Neurosciences informatiques.
Intelligence artificielle > Applications biologiques.
Réseaux nerveux.
MEDICAL > Physiology.
SCIENCE > Life Sciences > Human Anatomy & Physiology.
Neural circuitry
Artificial intelligence > Biological applications
Computational neuroscience
NEUROSCIENCE/General
COGNITIVE SCIENCES/General
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.?

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