Resources

Open Science Commitment

100% of our publications include a GitHub repository containing full data, code, experimental tasks, stimuli, and analysis pipelines. We believe reproducible science requires transparent sharing of all research materials.

Browse all our repositories at our GitHub Organization.

Open Source Software

Repository	Description
Systole	Cardiac signal analysis for psychophysiology
Cardioception	Cardiac interoception measurement tasks
respyra	Respiratory motor tracking for interoception research
GastroPy	Electrogastrography signal processing and gastric-brain coupling
metadpy	Bayesian modeling of behavioral metacognition
Hierarchical Interoception	Bayesian analysis for interoceptive psychophysics
RRST	Respiratory interoception measurement
Raincloud Plots	Multi-platform tool for robust data visualization

Systole

GitHub

A Python package for cardiac signal analysis in psychophysiology research. Systole provides comprehensive tools for:

Signal Processing: Pre-processing, visualization, and artefact detection/correction for cardiac data
Heart Rate Variability: Time-domain, frequency-domain, and non-linear HRV indices
Peak Detection: Automated R-peak detection using the Pan-Tompkins method with interactive correction
Experimental Integration: Synchronization of stimulus presentation with cardiac phases via PsychoPy

Features BIDS-format compatibility, native hardware integration with Nonin pulse oximeters and BrainVision amplifiers, and web-based viewers for annotating cardiac data.

Citation: Legrand & Allen (2022). Systole: A python package for cardiac signal synchrony and analysis. JOSS, 7(69), 3832.

Cardioception

GitHub

A Python package implementing validated psychophysical tasks for measuring cardiac interoception—how accurately people perceive their own heartbeats. Includes:

Heartbeat Counting Task: Participants count heartbeats during timed intervals for accuracy assessment
Heart Rate Discrimination Task: Adaptive psychophysical procedure measuring accuracy and precision of interoceptive beliefs using auditory feedback

Designed for minimal hardware requirements (computer + pulse oximeter), with flexible integration for ECG, M/EEG, and fMRI setups. Includes R-based hierarchical Bayesian modeling tools for analysis.

Citation: Legrand, N., Nikolova, N., Correa, C., Brændholt, M., Stuckert, A., Kildahl, N., Vejlø, M., Fardo, F., & Allen, M. (2022). The heart rate discrimination task: A psychophysical method to estimate the accuracy and precision of interoceptive beliefs. Biological Psychology, 168, 108239.

respyra

GitHub PyPI

A Python toolbox for respiratory motor tracking experiments in interoception research. respyra integrates a wireless chest-mounted force sensor with PsychoPy to create closed-loop breathing paradigms with real-time visual feedback.

Real-Time Display: Scrolling waveform with target dot and participant trace with graded color-coded feedback
Visuomotor Perturbation: Configurable gain manipulation for studying respiratory motor recalibration
Automated Calibration: Percentile-based range calibration with outlier rejection and saturation warnings
Crash-Resilient Logging: Row-level CSV flushing ensures no data loss mid-session
Post-Session Visualization: respyra-plot command for generating 6-panel summary figures

High split-half reliability (r = .86 Spearman-Brown corrected), suitable for individual-differences research.

Citation: Allen, M. (2026). respyra: A General-Purpose Respiratory Tracking Toolbox for Interoception Research. PsyArXiv.

GastroPy

GitHub

A Python package providing a modular pipeline for electrogastrography (EGG) signal processing and gastric-brain coupling analysis. Designed for researchers studying gastric electrical activity and its relationship to brain imaging data.

Signal Processing: Power spectral density, bandpass filtering (FIR/IIR), Hilbert-transform phase extraction, cycle detection, and artifact handling
Metrics & Analysis: Gastric frequency band classification, instability coefficients, cycle statistics, and quality assessment
fMRI Integration: Scanner trigger detection, volume windowing, confound regression, voxelwise BOLD phase extraction, and PLV map computation with NIfTI support
Coupling Analysis: Phase-locking values, surrogate testing, and circular statistics (Rayleigh tests, resultant length)
High-Level Pipeline: One-liner egg_process() function for complete workflow automation
Visualization: Publication-ready PSD plots, 4-panel EGG overviews, cycle histograms, and brain coupling maps

Citation: Allen, M. (2026). GastroPy: A Python Package for Electrogastrography Signal Processing and Gastric-Brain Coupling Analysis. GitHub. https://github.com/embodied-computation-group/gastropy

metadpy

GitHub

A Python library for Bayesian modeling of behavioral metacognition, providing the Python equivalent to the hMeta-d toolbox. Computes standard signal detection theory indices and metacognitive efficiency measures from trial-level performance and confidence ratings.

Signal Detection Theory: d-prime, criterion, hit/false alarm rates, ROC-AUC
Meta-d’ Estimation: Maximum likelihood estimation of metacognitive sensitivity
Hierarchical Bayesian Models: Hierarchical meta-d’ via Hamiltonian Monte Carlo (NUTS), powered by PyMC and PyTensor
Simulation Tools: Response simulation for generating synthetic metacognition datasets
Visualization: Specialized plotting functions for metacognitive data
Pandas Integration: Statistical functions callable directly as DataFrame methods

Citations:

Fleming, S. M. (2017). HMeta-d: hierarchical Bayesian estimation of metacognitive efficiency from confidence ratings. Neuroscience of Consciousness, 3(1), nix007.
Maniscalco, B. & Lau, H. (2012). A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings. Consciousness and Cognition, 21(1), 422–430.

Hierarchical Interoception Toolkit

GitHub

Hierarchical Bayesian psychometric function models for analyzing interoceptive psychophysics data. This toolkit provides:

Statistical modeling using Stan and BRMS for the Heart Rate Discrimination Task (HRDT) and Respiratory Resistance Sensitivity Task (RRST)
Parameter and model recovery validation analyses
Normative priors derived from large datasets
Power analysis tools for study planning
Interactive Shiny app for exploring power across design choices

Includes comprehensive R Markdown workflows demonstrating data simulation, model specification, fitting, diagnostics, and visualization.

Citation: Courtin, A. S., Ehmsen, J. F., Banellis, L., Fardo, F., & Allen, M. G. (2025). Hierarchical Bayesian Modelling of Interoceptive Psychophysics. bioRxiv.

Respiratory Resistance Sensitivity Task (RRST)

GitHub

An automated method for measuring respiratory interoception using a fully 3D-printable apparatus. Key features:

Psychophysical Assessment: Forced-choice discrimination task comparing breaths with varying airway obstruction
Efficient Measurement: Bayesian staircase procedure (Psi) achieves threshold convergence in 20-50 trials
Metacognitive Assessment: Evaluates confidence in perceptual judgments
Accessible Design: 3D-printable components eliminate need for expensive medical equipment

High test-retest reliability with minimal participant discomfort, completing full assessment in 30-45 minutes.

Citation: Nikolova, N., Harrison, O., Toohey, S., Brændholt, M., Legrand, N., Correa, C., Vejlø, M., Jensen, M. S., Fardo, F., & Allen, M. (2022). The respiratory resistance sensitivity task: An automated method for quantifying respiratory interoception and metacognition. Biological Psychology, 170, 108325.

Raincloud Plots

GitHub R Package

A data visualization method combining raw data, probability density, and summary statistics into a single plot. Created by Micah Allen, Raincloud Plots offer a robust alternative to bar charts and box plots that reduces information loss while maintaining clarity.

Multi-language Support: Available in R (ggrain, raincloudplots), Python (PtitPrince), and MATLAB
Publication-Ready: Produces beautiful, statistically valid visualizations with minimal code
Repeated Measures: Supports individually linked data points across conditions and time points
Flexible Designs: Handles 1x1, 2x2, 2x3, and between/within-subject experimental designs

Citations:

Allen, M., Poggiali, D., Whitaker, K., et al. (2021). Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Research, 4:63.
Judd, N., Langen, J. van, Poggiali, D., Whitaker, K., Marshall, T. R., Allen, M., & Kievit, R. (2024). ggrain—A ggplot2 extension for raincloud plots. bioRxiv.

Contact

For questions about our tools and resources:

General inquiries: micah@cfin.au.dk
Bluesky: @the-ecg.org
Twitter: @visceral_mind