Tools & Software
Tools
Based on a collaborative initiative to streamline and standardize the analysis of peripheral physiological data – mainly cardiovascular activity (electrocardiography [ECG], photoplethysmography [PPG]) and respiration – in relation to brain and behavioral data, we provide a
set of open-access, Python-based pipelines, implemented with the modular structure of Jupyter notebooks.
Our mission is to facilitate and enhance the reproducibility and transparency in peripheral physiological signal analysis by offering
open-access, customizable pipelines with step-by-step tutorials. Whether you are a novice or an expert in brain-body interactions, BBSIG provides tools and guidance to support your research.
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With one command scilaunch
creates the folder structure for your new research project on your machine. Moreover, it
prepares Python files such that you can use your research code as your own Python package, with the first line of code. It
prepares a conda environment for you and initializes a git repository.
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[Template] Research Project for Notion-based project management.
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(Quintero et al., 2021) User-friendly open-source Unity plugin for XR creators to integrate physiological user information in the development and evaluation of immersive content. Specifically, it allows to record, analyze, and visualize metrics, for example, of
heart activity using wearables (e.g., the Polar H10 chest strap) and of
movement (e.g., trajectories) from VR devices using
LabStreamingLayer.
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(Fourcade, Malandrone et al., 2024)The AffectTracker is a t
ool which allows users to continuously track and record moment-by-moment ratings of valence and arousal (i.e., pleasantness and intensity of feelings) in Unity. With the help of an VR input device, users indicate their affective state by positioning a cursor in the valence-arousal space of the affect grid and receive customizable real-time visual feedback.
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xai4mri is designed for advanced MRI analysis combining deep learning with explainable A.I. (XAI). It offers the following key functionalities:
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Data Integration: Effortlessly import new MRI datasets and apply the models to generate accurate predictions.
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Model Loading: Load (pretrained) 3D-convolutional neural network models tailored for MRI predictions.
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Interpretation Tools: Utilize analyzer tools, such as Layer-wise Relevance Propagation (LRP), to interpret model predictions through intuitive heatmaps.
With
xai4mri, you can complement your MRI analysis pipeline, ensuring precise predictions and insightful interpretations.
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Open stimuli
An open set of
standardized and validated 3D household objects for virtual reality-based research, assessment, and therapy.
(Tromp et al., 2020)
Open data
Publicly available dataset of 228 healthy participants comprising a younger (N=154, 25.1±3.1 years, range 20–35 years, 45 female) and an
older group (N=74, 67.6±4.7 years, range 59–77 years, 37 female) acquired cross-sectionally in Leipzig, Germany, between 2013 and 2015 to study mind-body-emotion interactions. During a two-day assessment, participants completed MRI at 3 Tesla (resting-state fMRI, quantitative T1 (MP2RAGE), T2-weighted, FLAIR, SWI/QSM, DWI) and a 62-channel EEG experiment at rest. During task-free resting-state fMRI, cardiovascular measures (blood pressure, heart rate, pulse, respiration) were continuously acquired. Anthropometrics, blood samples, and urine drug tests were obtained. Psychiatric symptoms were identified with Standardized Clinical Interview for DSM IV (SCID-I), Hamilton Depression Scale, and Borderline Symptoms List. Psychological assessment comprised 6 cognitive tests as well as 21 questionnaires related to emotional behavior, personality traits and tendencies, eating behavior, and addictive behavior. We provide information on study design, methods, and details of the data in our dataset descriptor paper.
Data and scripts from individual studies
Please note that we streamlined our scripts for the preprocessing and analysis of cardiac data by porting them to Python. You can find the “Brain-Body Analysis Special Interest Group” (BBSIG) notebooks here.
Behavior and electrophysiology (ECG, EEG, etc.)
Klotzsche, F.; Gaebler, M.; Villringer, A.; Sommer, W.; Nikulin, V. V.; Ohl, S.:
Visual short‐term memory‐related EEG components in a virtual reality setup.
Psychophysiology 60 (11), e14378 (2023)
data: https://doi.org/10.17617/3.WRDUGO
scripts: https://github.com/eioe/vMemEcc-Analyse
Grund, M.; Al, E.; Pabst, M.; Dabbagh, A.; Stephani, T.; Nierhaus, T.; Gaebler, M.; Villringer, A.:
Respiration, heartbeat, and conscious tactile perception.
The Journal of Neuroscience 42 (4), pp. 643 - 656 (2022)
data and scripts: https://github.com/grundm/respirationCA
Hofmann, S.; Klotzsche, F.; Mariola, A.; Nikulin, V. V.; Villringer, A.; Gaebler, M.:
Decoding subjective emotional arousal from EEG during an immersive virtual reality experience.
eLife 10, e64812 (2021)
scripts: https://github.com/SHEscher/NeVR
Kunzendorf, S.; Klotzsche, F.; Akbal, M.; Villringer, A.; Ohl, S.; Gaebler, M.:
Active information sampling varies across the cardiac cycle.
Psychophysiology 56 (5), e13322 (2019)
data and scripts: https://github.com/SKunzendorf/0303_INCASI
Motyka, P.; Grund, M.; Forschack, N.; Al, E.; Villringer, A.; Gaebler, M.:
Interactions between cardiac activity and conscious somatosensory perception.
Psychophysiology 56 (10), e13424 (2019)
data and scripts: https://github.com/Pawel-Motyka/CCSomato
Masurovsky, A.; Chojecki, P.; Runde, D.; Lafci, M.; Przewozny, D.; Gaebler, M.:
Controller-free hand tracking for grab-and-place tasks in immersive virtual reality: Design elements and their empirical study.
Multimodal Technologies and Interaction 4 (4), 91 (2020)
data and scripts: https://https://github.com/alexmasurovsky/leap-vr-ux-study/
Klotzsche, F.; Motyka, P.; Molak, A.; Sahula, V.; Darmová, B.; Byrnes, C.; Fajnerová, I.; Gaebler, M.:
No cardiac phase bias for threat-related distance perception under naturalistic conditions in immersive virtual reality.
Royal Society Open Science 11 (10), 241072 (2024)
experiment: https://osf.io/2m8h5
data: https://edmond.mpg.de/dataset.xhtml?persistentId=doi:10.17617/3.KJGEZQ
scripts: https://github.com/eioe/vrcc_analysis
Hofmann, S. M., Ciston, A., Koushik, A., Klotzsche, F., Hebart, M. N., Müller, K.-R., Villringer, A., Scherf, N., Hilsmann, A., Nikulin, V. V., & Gaebler, M.:
Human-aligned deep and sparse encoding models of dynamic 3D face similarity perception.
OSF (2024)
scripts: https://shescher.github.io/FaceSim3D/
documentation: https://shescher.github.io/FaceSim3D/
Fourcade, A.; Klotzsche, F.; Hofmann, S.; Mariola, A.; Nikulin, V. V.; Villringer, A.; Gaebler, M.:
Linking brain-heart interactions to emotional arousal in immersive virtual reality.
Psychophysiology 61 (12), e14696 (2024)
data and scripts: https://github.com/afourcade/evrbhi
MRI
Uhlig, M.; Reinelt, J.; Lauckner, M.; Kumral, D.; Schaare, H. L.; Mildner, T.; Babayan, A.; Möller, H. E.; Engert, V.; Villringer, A. et al.:
Rapid volumetric brain changes after acute psychosocial stress.
NeuroImage 265, 119760 (2023)
scripts: https://gitlab.gwdg.de/necos/vbm
results (t-maps): https://www.neurovault.org/collections/SFQXOIUB/
Kumral, D.; Schaare, H. L.; Beyer, F.; Reinelt, J.; Uhlig, M.; Liem, F.; Lampe, L.; Babayan, A.; Reiter, A.; Erbey, M. et al.:
The age-dependent relationship between resting heart rate variability and functional brain connectivity.
NeuroImage 185, pp. 521 - 533 (2019)
results (t-maps): https://neurovault.org/collections/WWOKVUDV/
Grosse Rueschkamp, J. M.; Brose, A.; Villringer, A.; Gaebler, M.:
Neural correlates of up-regulating positive emotions in fMRI and their link to affect in daily life.
Social Cognitive and Affective Neuroscience 14 (10), pp. 1049 - 1059 (2019)
results (t-maps): https://neurovault.org/collections/MAZDXCZW/
Schaare, H. L.; Kharabian, S.; Beyer, F.; Kumral, D.; Uhlig, M.; Reinelt, J.; Reiter, A.; Lampe, L.; Babayan, A.; Erbey, M. et al.:
Association of peripheral blood pressure with gray matter volume in 19- to 40-year-old adults.
Neurology 92 (8), pp. e758 - e773 (2019)
results (t-maps): https://neurovault.org/collections/FDWHFSYZ/
Gaebler, M.; Daniels, J. K.; Lamke, J.-P.; Fydrich, T.; Walter, H.:
Behavioural and neural correlates of self-focused emotion regulation in social anxiety disorder.
Journal of Psychiatry & Neuroscience 39 (4), pp. 249 - 258 (2014)
results (t-maps): https://neurovault.org/collections/206/