Synopsis
Machine Generated Contents Note: 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 ? -- 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 -- Contents Note Continued: 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 -- Contents Note Continued: 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 -- Contents Note Continued: 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 -- Contents Note Continued: 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? -- Contents Note Continued: 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 -- Contents Note Continued: 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) -- Contents Note Continued: 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 -- Contents Note Continued: 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? -- Contents Note Continued: 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 -- Contents Note Continued: 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 -- Contents Note Continued: 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 -- Contents Note Continued: 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 -- Contents Note Continued: 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 -- Contents Note Continued: 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 -- Contents Note Continued: 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 -- Contents Note Continued: 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 -- Contents Note Continued: 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 -- Contents Note Continued: 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.?. Mike X. Cohen. Includes Bibliographical References (pages 549-572) And Index.
