Process and Report Gazepoint Biometrics Data

Gazepoint-native tools for reproducible analysis and reporting of Gazepoint Biometrics and GP3-derived physiological, gaze, pupil, fixation, AOI, marker, and event-level exports. The package supports import, validation, preprocessing, missing-value imputation, EDA/GSR, SCR epoching, IBI/RR/HRV, PPG/pulse workflows, pupil blink detection and cleaning, gaze filtering, fixation summaries, engagement-dial indices, multimodal trial regressors, quality reports, exclusion and QC recommendations, synthetic eye-data generation, and interoperability or workflow-inspired helpers for common psychophysiology toolchains. The package is designed for transparent, CRAN-safe, scriptable analysis rather than as a full clone of external commercial or open-source toolboxes.

gpbiometrics

gpbiometrics provides reproducible Gazepoint-native infrastructure for importing, validating, quality-checking, preprocessing, synchronising, summarising, modelling, plotting, and reporting physiological and eye-tracking exports. It covers EDA/GSR/SCR, PPG, HR, IBI/HRV, pupil, gaze, fixations, AOIs, TTL/event timing, multimodal alignment, interoperability, reporting, and privacy-safe validation.

Current GitHub release: 2.1.0 (v2.1.0, 20 September 2026) · CRAN: 0.1.0 (verified October 2026)

Website · Reference · Articles · Release · Signals paper

What gpbiometrics provides

• Gazepoint biometric, gaze, pupil, fixation, AOI, EDA/GSR/SCR, PPG, HR, IBI, and HRV workflows;
• explicit schema, sampling, missingness, timebase, TTL, synchronization, and quality audits;
• event-locked, AOI-linked, trial-level, and model-ready summaries;
• HeartPy-style, pyHRV-style, BioSPPy-style, and PsPM-style Gazepoint-native workflows without claiming exact external-toolbox equivalence;
• MNE/EEG/LSL, BIDS, eyetrackingR, PupillometryR, gazeR, BioSPPy, pyHRV, and gp3tools interoperability helpers;
• reproducibility manifests, reporting bundles, reviewer-facing audits, release-readiness checks, and privacy-safe real-data smoke testing.

September 2026 measurement-accountability additions

The published GitHub 2.1.0 release added four conservative diagnostics motivated by the September 2026 methods-surveillance tranche:

• compare_hrv_prv_devices() reports ICC(A,1), Lin's CCC, Bland–Altman bias, and 95% limits for one derived metric at a time, while retaining signal-source and sensor-site provenance. It does not create a global ECG/PPG interchangeability decision.
• scr_responsivity_sensitivity() preserves the conventional SCR non-responder flag, estimates a Beta-Binomial posterior response probability, and retains low-reactive participants for model-based sensitivity analysis instead of imposing irreversible preprocessing deletion.
• physiology_validation_ladder() separates acquisition QC, analytical QC, construct evidence, within-person evidence, and held-out-person generalization. Population/generalization claims require held-out-person validation to pass.
• ppg_topology_features() is opt-in and experimental. It summarizes H0 persistence lifetimes from a delay embedding as a structural waveform descriptor, not a direct physiological surrogate, and carries a participant-grouped validation guardrail.

These are published in immutable GitHub v2.1.0, and do not change the historical v2.0.0 archive. The October 2026 synthetic acquisition-availability article is a subsequent source/documentation addition, not a new stable release or clinical validation.

See the Measurement accountability for Gazepoint physiology article.

Installation

Install the exact published GitHub 2.1.0 release:

install.packages("remotes")
remotes::install_github("stefanosbalaskas/gpbiometrics", ref = "v2.1.0")

For current development sources, including October documentation changes:

remotes::install_github("stefanosbalaskas/gpbiometrics")

Quick start

library(gpbiometrics)

folder <- "path/to/gazepoint_exports"
workflow <- run_gazepoint_biometrics_workflow(
  path = folder,
  include_all_gaze = TRUE,
  include_fixations = FALSE,
  include_other_csv = FALSE,
  expected_sampling_rate_hz = 60
)

summarise_gazepoint_biometrics_workflow(workflow)

For private real data, keep source exports and generated smoke-test outputs outside the package repository.

Main workflow

Gazepoint / external exports
  -> schema + signal availability audit
  -> timing / TTL / missingness / quality checks
  -> EDA / PPG / IBI / pupil / gaze preprocessing
  -> fixation / AOI / event / multimodal alignment
  -> physiological and process feature extraction
  -> model-ready tables
  -> plots + reports + reproducibility evidence
  -> optional external-toolbox cross-checks

The package is intentionally modular: researchers can run a complete workflow or call individual helpers while keeping preprocessing and interpretation decisions explicit.

Release validation

The v2.0.0 release record reports:

• 3,515 package tests passed;
• 49 release-readiness tests passed;
• 40 interoperability-version tests passed;
• 55 private smoke-harness tests passed;
• R CMD check: 0 errors, 0 warnings, 0 notes; and
• pkgdown configuration: no problems found.

The September 2026 additions are development functionality layered on top of this qualified release baseline.

Interpretation guardrails

gpbiometrics treats biometric outputs as physiological descriptors, quality-control evidence, or analysis-ready measurements. It does not infer emotion, stress, cognition, preference, attention quality, health status, diagnosis, or another psychological/clinical state directly from EDA, HRV/PRV, PPG, pupil, gaze, or multimodal signals.

Metric agreement does not imply sensor interchangeability; retention-first responsivity does not diagnose participant state; held-out prediction does not establish construct validity; and experimental topology features are not physiological surrogates without independent validation.

Documentation

• Article roadmap
• EDA/GSR/SCR workflow
• PPG/HRV workflow
• Pupil QC workflow
• Event alignment and AOI workflow
• MNE/EEG/LSL interoperability
• Measurement accountability
• Complete function reference

Citation

For the installed package citation:

citation("gpbiometrics")
packageVersion("gpbiometrics")

The package paper is “gpbiometrics: An R Package for Reproducible Analysis and Reporting of Gazepoint Biometrics Exports”, Signals (2026), DOI 10.3390/signals7050086.

License

MIT License.