Introduction | The PhysioEvents Class | Creating Events | Default Values | Inspecting Events | Attaching Events to PhysioExperiment | Setting Events from a Data Frame | Retrieving Events | Adding Events | Removing Events | Time and Sample Conversion | Working with Event Windows | Events and Time Concatenation | Summary | Session Info

Introduction | Creating a PhysioExperiment | Multiple Assays | Accessing Basic Properties | Sampling Rate | Dimensions and Duration | Default Assay | Channel Management | Reading Channel Information | Setting Channel Properties | Subsetting Channels | Reference Electrode | Subsetting and Combining | Time-based Subsetting | Combining Objects | Summary Statistics | NA Handling | Next Steps | Session Info

1. A synthetic single case | 2. Detect change defensibly | 3. Keep the clinician in the loop | 4. Visualise the trajectory | 5. Draft the progress note | 6. Capacity vs performance (cross-modal ICF constructs) | 7. What the ecosystem can measure | Where to go next

1--2. A "real" subject, recorded through a noisy IMU | 3. Personalise the twin from the recording | 4a. In-silico intervention | 4b. A labelled synthetic training set | Validity and verification | Where next

A single-case A-vs-B contrast | Ordinal ratings to an interval scale (Rasch)

What this package does | A node matrix | Time Delay Stability | Organ-interaction network | Network reconfiguration across states | Cardiorespiratory coupling | Brain–heart: the heartbeat-evoked potential | Working with PhysioExperiment objects | References

A synthetic recording | Import and reshape | Normalise | Inspect segment lengths and joint angles | Detect and correct anomalies | Visualise | References

A factory returns target objects | The single-modality factories | Cohorts fan out automatically | Synthetic demo data and pure helpers | Where to go next

The corpus at a glance | Opening one case | Checking the invariants | The research queue | Where to go next

A tiny local lake | Store and retrieve a PhysioExperiment | Store a run bundle with internal lineage | Where to go next

1. A single case (AB design) | 2. Non-overlap effect sizes | 3. Two-SD band | 4. Frame the question as an estimand | Where to go next

Normative reference and z-scores | Change against MDC and MCID | Single-case designs | Bilingual labels | Where to go next

1. Score an instrument | 2. Benchmark against published clinimetrics | 3. Classify the response (dual MDC-vs-MCID) | 4. Normative z-score | 5. Goal Attainment Scaling | 6. Tag outcomes to the ICF (cross-package) | 7. Export to FHIR

1. Ingest the export | 2. Heart rate and heart-rate variability | 3. Blood oxygen (SpO2) | 4. Sleep | 5. Activity | 6. ECG | Scope and limitations

1. Ingest the archive | 2. Heart rate | 3. Blood oxygen (SpO2) | 4. Sleep | 5. Activity | Google Fit and Health Connect | Scope and limitations

Free-living metrics | Notes

A hypergraph from an incidence matrix | Projections and network metrics | Community detection | Perturbation impact | Comparison against a null model | Reproducing the paper's statistics | Where to go next | Reference

The bundled datasets | Comparing a synthetic subject to the knee-flexion band | Where next

1. A head surface and an electrode montage | 2. A cortical source space | 3. The analytic single-sphere leadfield | 4. A boundary-element leadfield, and validating it | Where to go next

Load the hub | Anatomical queries | The knowledge graph | ICF classification | Where to go next

1. Create a benchmark bundle | 2. Validate manifest integrity | 3. Run benchmark suite | 4. Export reports | 5. Use real gold-standard data later

1. Simulate MoCap and EMG data | 2. Process EMG (rectify + RMS envelope + smoothing) | 3. Align EMG to MoCap timeline | 4. Build an integrated analysis table | Notes

1. Simulate force-plate inputs | 2. Run force-plate analysis | 3. Build inverse-dynamics inputs | 4. Estimate inertial parameters and compute moments/power | 5. Multi-plate selection and 3D inverse dynamics | Notes

1. Create demo data | 2. Run one-command quick start | 3. Check data readiness | 4. Inspect key outputs | 5. Start from your own file | 6. Common input entry points | 7. Common troubleshooting

1. Preconditions | 2. Create a benchmark manifest | 3. Point manifest rows to your downloaded files | 4. Validate manifest integrity | 5. Run benchmark suite | 6. Replace placeholders with real external data

The pipeline | A recording (simulated OpenPose output) | Clean with poseFix() | Before vs after | Downstream: knee flexion angle | A simple gait metric | Where to go next | Reference

Route A -- use OpenCap's cloud OpenSim output (self-contained) | Route B -- run OpenSim locally (ID / muscle forces) | Enabling an OpenSim backend | From an OpenCap session, end to end | Downstream | Scope and honest limitations

Introduction | Creating Synthetic Data | The MultiPhysioExperiment Container | Signal Alignment | Spectral Coherence | Welch-Method Coherence | Multitaper Coherence | Cross-Spectral Density | Phase Synchrony | Phase Locking Value (PLV) | Phase Lag Index (PLI) | Weighted PLI (wPLI) | Directed Coupling: Granger Causality | Time-Domain Coupling | Cross-Correlation | Sliding-Window Cross-Correlation | Wavelet Time-Frequency Analysis | Wavelet Coherence | Wavelet PLV | Unified Interface: couplingAnalysis() | Multi-Channel Coupling Matrices | Statistical Significance Testing | Surrogate Testing | Bootstrap Confidence Intervals | Matrix-Level Significance with Multiple Comparison Correction | Complete Workflow Example | Session Information

The integration problem | Shared vs modality-specific: JIVE | Covarying directions: CCA and PLS | A common latent drive: coupled NMF | Whole-block association | One index, one matrix, one latent model | Choosing a method | Correctness | See also

Overview | Creating simulated EEG and EMG data | Building PhysioExperiment objects | Creating a MultiPhysioExperiment | Spectral coherence (CMC) | Visualisation | Phase Locking Value (PLV) | Granger causality | The couplingAnalysis() wrapper | Working with raw numeric vectors | Signal alignment utilities | Statistical significance testing | Surrogate testing | Bootstrap confidence intervals | Wavelet coherence | Wavelet coherence visualization | Multi-channel coupling matrices | Summary

From raw acceleration to ENMO | Step detection and intensity | A day-level free-living summary | Where next

Overview | Checking Your Build | Working with Models (Native Backend) | Template-Based Setup | Strict vs. Non-Strict Mode | Running Tools | Backend Selection Guide

Overview | Prerequisites | Step 1: Inverse Kinematics | Step 2: Inverse Dynamics | Step 3: Static Optimization | Running the Full Pipeline | Inspecting Results | Backend Selection

Introduction | Setup | Simulating ECG Data | Signal Quality Assessment | Baseline Correction | R-Peak Detection | Adjusting Detection Parameters | RR Interval Computation | Ectopic Beat Detection and Correction | HRV Time-Domain Analysis | HRV Frequency-Domain Analysis | HRV Nonlinear Analysis | Individual Functions | Convenience Wrapper | Complete Analysis Pipeline | References | Session Info

Introduction | Setup | Simulating ECG with PQRST Morphology | R-Peak Detection | PQRST Waveform Delineation | Understanding Delineation Output | Validating Against Known Fiducials | Computing Clinical ECG Intervals | QRS Duration Analysis | PR Interval Analysis | QT Interval and Bazett Correction | Beat-to-Beat Variability | Complete Morphology Analysis Pipeline | Handling Multi-Channel ECG | References | Session Info

Introduction | Creating or Loading EDA Data | Signal Quality Assessment | Preprocessing | Filtering | Downsampling | Artifact Detection and Correction | Tonic/Phasic Decomposition | Highpass Method | Median Filter Method | Continuous Decomposition Analysis (CDA) | cvxEDA | Visualizing the Decomposition | SCR Peak Detection | Feature Extraction | Data Transformation | Plotting the Raw Signal | Complete Pipeline Example | References | Session Info

Introduction | Overview of the ER-SCR Workflow | Preparing Data with Events | Adding Events Manually | Preprocessing for ER-SCR | Decomposition | Event-Related SCR Extraction | Understanding the Parameters | Interpreting the Results | Working with ER-SCR Results | Response Rate | Mean Amplitude | Comparing Conditions | Visualizing Event-Related EDA | Plotting the Signal with Event Markers | Plotting SCR Peaks | Data Transformation for Statistics | Complete Event-Related Analysis Example | Best Practices | References | Session Info

1. Define the trial | 2. Plan the sample size | 3. Randomize with allocation concealment | 4. CONSORT flow | 5. Analysis populations | 6. CDISC-shaped export | Where to go next

1. Simulate a recording with known motor units | 2. Decompose | 3. Inspect the recovered units | 4. Validate against the ground truth | Working with your own recordings

A small synthetic recording | Optical density | Haemoglobin concentration (MBLL) | Motion correction | Signal quality and channel pruning | Short-separation regression | Activation GLM | Where to go next

Introduction | Motor Imagery Paradigm | SSVEP Paradigm | Classification Pipeline | Setup | Motor Imagery Analysis | Common Spatial Patterns (CSP) | Feature Extraction | SSVEP Detection | Classification | Performance Evaluation | Classification Accuracy | Cohen's Kappa | Information Transfer Rate (ITR) | Cross-Validation Strategies | Summary

Setup | Spike Detection | Basic Spike Detection | Parameter Tuning | Template Matching | Sensitivity Analysis | Quantitative EEG (QEEG) | Custom Band Definitions | Normative Comparison | Asymmetry Indices | Custom Electrode Pairs and Bands | Clinical Interpretation | EEG Suppression | Interpreting BSR | EEG Slowing | Peak Frequency Method | Focal vs. Generalized Slowing | Full Clinical Workflow | Clinical Interpretation Guidelines | Spike Detection Limitations | QEEG Interpretation | When to Combine with Clinical Judgment

Introduction | Setup | Coherence Analysis | Magnitude-Squared Coherence | Imaginary Coherence | Phase Locking Value (PLV) | Weighted Phase Lag Index (wPLI) | Granger Causality | Building Connectivity Matrices | Thresholding Strategies | Visualization | Heatmap View | Circle Plot | Method Comparison | Practical Recommendations | Frequency Band Considerations | Conclusion | References

Introduction | Setup | Filtering with eegFilter() | Highpass Filtering | Lowpass Filtering | Bandpass Filtering | Notch Filtering | FIR vs IIR Filters | Re-referencing with eegRereference() | Average Reference | Mastoid Reference | Single Channel Reference | When to Use Each Type | Bad Channel Detection and Interpolation | Detecting Bad Channels | Interpolating Bad Channels | Recommended Workflow | ICA Artifact Removal | Running ICA | Detecting Artifact Components | Removing Artifact Components | Typical Workflow | Epoching with eegEpoch() | Creating Event Markers | Extracting Epochs | Alternative Baseline Methods | Artifact Rejection with eegArtifactReject() | Threshold-Based Rejection | Gradient-Based Rejection | Joint Probability Method | When to Use Each Method | Full Pipeline with eegPreprocess() | Parameter Selection for Different Paradigms | Best Practices | Recommended Parameter Ranges | Pipeline Order Matters | Always Inspect Data Between Steps | Document Your Pipeline

Setup | Forward Model | Spherical Head Model | Simplified BEM Model | Leadfield Interpretation | Source Estimation | MNE (Minimum Norm Estimate) | sLORETA (Standardized Low-Resolution Brain Electromagnetic Tomography) | eLORETA (Exact Low-Resolution Electromagnetic Tomography) | Comparing Methods | Beamformer | LCMV Beamformer | DICS Beamformer | Source Power | Custom Frequency Bands | Visualization | Scatter Plot | Flat Map | Comparing Methods Visually | Method Comparison | Distributed Source Imaging vs. Beamforming | Choosing a Method | Best Practices | Electrode Density | SNR Considerations | Regularization | Preprocessing Recommendations

Simulating an Oddball Experiment | ERP Component Detection | ERP Measurement Methods | Fractional Area Latency | Difference Waves | Artifact Removal with ICA Before ERP

Creating Test Data | Independent Component Analysis (ICA) | ERP Analysis | Quantitative EEG (QEEG) | Microstate Analysis | Brain-Computer Interface (BCI) | Sleep Analysis | Clinical EEG Analysis

Introduction | Setup and Data Preparation | Preprocessing for Sleep EEG | Recommended Filter Settings | Artifact Detection and Removal | Automatic Sleep Staging | Sleep Stage Characteristics | Interpreting Staging Output | Sleep Spindle Detection | Detection Parameters | Clinical Significance | K-Complex Detection | Detection Methodology | Clinical Applications | Slow Wave Detection and Analysis | Slow Wave Detection | SWA Dynamics Across the Night | Clinical Interpretation | Sleep Architecture Metrics | Normal Adult Sleep Architecture | Hypnogram Visualization | Clinical Interpretation and Reporting | Complete Analysis Workflow | Abnormalities to Look For | Insomnia Pattern | Sleep Apnea | Depression | Neurodegenerative Disease | Limitations of Automated Staging | Best Practices | Conclusion | References | Session Information

Introduction | Setup | Morlet Wavelet Transform | Short-Time Fourier Transform | Multitaper Spectral Analysis | Event-Related Spectral Perturbation (ERSP) | Intertrial Coherence (ITC) | Visualization with eegPlotSpectrogram | Method Comparison Guide | Session Info

Introduction | Creating EMG Data | Amplitude Envelope Extraction | Amplitude Normalization | Muscle Activation Onset Detection | Hodges-Bui Method | Teager-Kaiser Energy Operator | Comparing Methods | Complete Workflow Example | References

Introduction | Fatigue Analysis | Creating Fatigue Test Data | Windowed Fatigue Tracking | Fatigue Index | Spectral Moments | Muscle Synergy Decomposition | Decomposition Methods | NMF Synergy Extraction | PCA and ICA Methods | Determining the Number of Synergies | Synergy Reconstruction | Comparing Synergy Solutions | Complete Fatigue and Synergy Workflow | References

A small experiment | Attach subject identity, then split on subjects | Feature extraction | Aligning two domains with CORAL | Backend-gated workflows

1. Simulate epoched MEPs | 2. MEP amplitude | 3. MEP onset latency | 4. Boltzmann recruitment curve | Where to go next

Stream metadata | A deterministic loopback stream | Snapshot to a PhysioExperiment | A synchronous processing pipeline | Online DSP | Clock correction | A governed stimulation trigger | Where to go next

Introduction | Setup | Creating Example Data | FFT Analysis | Interpreting the FFT Output | Power Spectral Density | Band Power Extraction | Spectrogram (Short-Time Fourier Transform) | Window Parameters | Wavelet Transform | Hilbert Transform and Instantaneous Measures | Summary | References | Session Info

Introduction | Setup | Creating Epoched Test Data | Pointwise t-Tests | One-Sample t-Test | Two-Sample t-Test | Paired t-Test | One-Sided Tests | ANOVA Across Conditions | Cluster-Based Permutation Tests | How It Works | One-Sample Cluster Test | Examining the Permutation Distribution | Effect Sizes | Bootstrap Confidence Intervals | Multiple Comparison Correction | Choosing a Correction Method | Finding Significant Time Windows | Complete Workflow Example | References | Session Info

Introduction | Setup | Creating Example Data | Workflow 1: Built-In ICA | Step 1: Decompose into Independent Components | Step 2: Identify Artifact Components | Step 3: Remove Artifact Components | Workflow 2: fastICA Package | Step 1: Run ICA | Step 2: Remove Components | Detecting Bad Channels | Interpolating Bad Channels | Recommended Preprocessing Order | Choosing the Number of Components | Summary | References

Introduction | Setup | Creating Example Data | Filtering | Moving Average Filter | Butterworth Filter | FIR Filter | Notch Filter | Resampling | Arbitrary Resampling | Integer-Factor Decimation | Integer-Factor Interpolation | Re-Referencing | Average Reference | Single Channel Reference | Linked Reference | Checking the Reference | Detrending | Linear and Mean Detrending | Alternative Detrending | Baseline Removal | Preprocessing Pipelines | Creating a Pipeline | Applying a Pipeline | Summary | References

1. A tamper-evident audit trail | 2. An externally authenticated signature | 3. De-identification | 4. Keyed pseudonymization | 5. Project-owned risk and traceability | 6. Engineering-readiness check | Where to go next

1. Overview | 2. Installation | 3. Creating a PhysioExperiment | 3.1 From scratch | 3.2 From a CSV file | 3.3 Adding events | 4. Exploring the Object | 4.1 Printing and dimensions | 4.2 Accessing the sampling rate | 4.3 Channel information | 4.4 Assay access | 4.5 Subsetting | 5. Signal Processing | 5.1 Butterworth filtering | 5.2 Notch filter | 5.3 FIR filter | 5.4 Moving-average filter | 5.5 Detrending | 5.6 Re-referencing | 6. Epoching and Averaging | 6.1 Creating epochs | 6.2 Baseline correction and artifact rejection | 6.3 Averaging epochs | 7. Time-Frequency Analysis | 7.1 Spectrogram (STFT) | 7.2 Wavelet transform | 7.3 Band power | 7.4 Hilbert transform | 7.5 FFT | 8. Visualization | 8.1 Single-channel trace | 8.2 Multi-channel display | 8.3 Power spectral density | 8.4 Event-related potential (ERP) | 8.5 Topographic map | 8.6 Spectrogram | 9. Statistical Testing | 9.1 Pointwise t-test | 9.2 ANOVA | 9.3 Cluster-based permutation test | 9.4 Multiple comparison correction | 9.5 Effect size and confidence intervals | 9.6 Finding significant time windows | 10. Database Integration | 11. Graphical User Interface | Session Information

Introduction | BIDS format support | What is BIDS? | Writing to and reading from a BIDS dataset | Exploring and validating a BIDS dataset | DuckDB database backend | Why use a database? | Connecting, initializing, registering, and querying | Session info

Overview | A small example object | RDS (native R serialization) | CSV / TSV | EDF / EDF+ | HDF5 | MATLAB .mat files | Choosing a format | Session info

Why a bridge, not a rewrite | A lossless data bridge | Let MNE do the analysis, keep the result in R | MNE as a validation oracle for native ops | Toward native brain-imaging integration | Example: template (fsaverage) source localization via MNE

What this package is | Build a PhysioExperiment | Core accessors | Channel management | Events | Extract a time window | Summarize and check for gaps | Where to go next