| title | Resources |
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| permalink | /resources/ |
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.
| 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 |
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.
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.
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-plotcommand 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.
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
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 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.
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.
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.
For questions about our tools and resources:
- General inquiries: micah@cfin.au.dk
- Bluesky: @the-ecg.org
- Twitter: @visceral_mind