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

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.?. Local Note eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America Subject Neural networks (Neurobiology) Neural networks (Neurobiology) Neural networks (Computer science) Neural networks (Computer science) Computational neuroscience. Computational neuroscience. Artificial intelligence -- Biological applications. Artificial intelligence -- Biological applications. Nerve Net. Genre/Form Electronic books. Other Form: Print version: Cohen, Mike X., 1979- Analyzing neural time series data. Cambridge, Massachusetts : The MIT Press, [2014] 9780262019873 (DLC) 2013016461 (OCoLC)849822345 ISBN 9780262319553 (electronic book) 0262319551 (electronic book) 1306403286 (electronic book) 9781306403283 (electronic book) 9780262019873 (hardcover : alkaline paper) 0262019876 (hardcover : alkaline paper)