Workflow map¶
Use this page as the shortest route from what you recorded to what to do next.
Start with the row that matches your data. Each path links to executable examples, generated plots, and deeper articles.
| You have | Start with | Typical next steps | Go to |
|---|---|---|---|
| EDA / GSR | channel + artifact QC | tonic/phasic decomposition, SCR detection, event summaries | EDA / GSR / SCR |
| PPG / pulse waveform | signal-quality + peak detection | IBI, HR, HRV, Poincaré/tachogram diagnostics | PPG / HRV |
| IBI / RR intervals | interval validity | time/frequency/nonlinear HRV, toolbox cross-checks | PPG / HRV |
| Pupil diameter | missingness/blink/pupil QC | baseline/change summaries, event windows, AOI linkage | Pupil / gaze / AOI |
| Gaze coordinates / fixations | validity + timing QC | AOIs, transitions, saccades, linked physiology | Pupil / gaze / AOI |
| TTL / event markers | event extraction + timebase audit | trial windows, event locking, multimodal synchronization | Multimodal alignment |
| Multiple synchronized streams | schema + timing audit | alignment, windowed features, dashboards, model-ready tables | Multimodal alignment |
| External neuro/physiology files | backend/version audit | MNE, LSL/XDF, BioSPPy, HeartPy, pyHRV, NeuroKit bridges | Interoperability |
| A completed analysis | QC evidence + manifest/reporting | visual audit, reproducibility, interpretation guardrails | QC + reporting |
Recommended research pipeline¶
1IngestRead exports and standardize schema.
2AuditCheck channels, timing, missingness, dropouts and markers.
3ProcessApply modality-specific preprocessing.
4AlignJoin trials, events, AOIs and external streams.
5SummarizeBuild features, plots and model-ready tables.
6ReportRetain QC, provenance and cautious interpretation.
What makes a workflow complete?¶
A defensible workflow should usually leave behind more than a final feature table. For reproducibility, retain:
- the raw-to-standardized schema decisions;
- signal/timebase validity outputs;
- event and alignment diagnostics;
- preprocessing settings;
- exclusion or dropout evidence;
- software/backend versions for optional integrations;
- generated figures used for QC;
- the final reporting or reproducibility object.
Cross-toolbox validation¶
gpbiometricspy can work with, or mirror workflows from, several established Python ecosystems. These bridges are for interoperability and cross-checking, not for silently changing the package's declared analysis contract.
Need the evidence layer?¶
Go to Parity & validation for the frozen R contract, Deep validation for cross-runtime checks, and Private real-data validation for the privacy-safe real-data harness.