Runnable, copy-pasteable scripts that demonstrate the toolbox end to end. Each
script is self-contained: open it in MATLAB and run it (most use the bundled
pf2.import.sampleData* so they work out of the box). New to the toolbox? Work
through them in roughly the order below.
| Script | What it covers |
|---|---|
| tutorial_end_to_end.m | Single subject: import → process → blocks → Experiment → stats/export. The best first script. |
| tutorial_batch_workflow.m | Multi-subject: directory import, CSV metadata, batch process, LME, batch export. |
| example_basic_viewing.m | Viewing raw & processed data, markers, and segments. |
| Script | What it covers |
|---|---|
| example_pipeline_basics.m | Build, inspect, tune, and run processing pipelines (the Pipeline API). |
| example_pipeline_custom_function.m | Write and integrate a custom processing step. |
| example_global_signal_removal.m | Remove systemic/global interference: CAR vs PCA-GSR vs short-channel regression, compared against ground truth. |
| example_qc_pipeline.m | Quality-control pipeline: checks, thresholds, applying recommendations. |
| Script | What it covers |
|---|---|
| example_import_blocks.m | CSV metadata import, block definition, BIDS events, per-trial behavioral data. |
| example_averaging_modes.m | Hierarchy vs flat vs none averaging; pseudoreplication. |
| example_gca_timemodel.m | Growth Curve Analysis with a polynomial TimeModel. |
| example_glm_analysis.m | GLMExperiment workflow from continuous recordings. |
| example_glm_advanced.m | Manual GLM pipeline with design matrix and first-level contrasts. |
| example_glm_connectivity.m | GLM-based connectivity (two methods). |
| example_experiment_cli.m | Experiment class CLI: grouping, behavioral vars, aux signals, ROI, LME. |
| example_neural_efficiency.m | Neural efficiency: brain activation vs behavioral performance. |
| example_group_stats_bridge.m | Bridge group LME results to a brain projection. |
| Script | What it covers |
|---|---|
| example_connectivity.m | Within-subject functional connectivity. |
| example_hyperscanning.m | Inter-brain synchrony for paired recordings. |
| example_ppi_hyperscanning.m | Cross-brain PPI: speaker→listener, HRV-derived, triad coupling, group PPI. |
| example_hbica.m | HB-ICA hyperscanning: dyad, group, block-wise, visualization. |
| Script | What it covers |
|---|---|
| example_plot_options.m | Plot types: error bands, scatter, heatmap, topo, LME, composite, saving. |
| example_spatial_visualizations.m | Time-animation movies, sensitivity kernel, parcel projection, connectome, dual-brain synchrony. |
| example_stat_visualizations.m | 3D stat projections: p-values, F-stats, correlations, biomarkers. |
| example_brain_render_styles.m | High-quality 3D rendering styles, materials, colormaps, and the Explore3D explorer. |
| SampleInterpolateValues3D.m · SampleInterpolateValues3D_Animation.m | Lower-level 3D surface interpolation primitives. |
| Script | What it covers |
|---|---|
| example_dot_reconstruction.m | DOT: PMDF forward model, coverage, banana projection, image reconstruction, cortical render. |
| example_snirf_export.m | Exporting fNIRS data to SNIRF format. |
The ../notebooks/ directory holds longer analysis templates
(task block design, resting state, hyperscanning, longitudinal, sample-report
generation). These are scaffolds for structuring a full analysis — adapt the
data paths to your own dataset.
See the project README for installation and a quick start,
and docs/ for the reference documentation.