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A Python package for the analysis of biopsychological data.

With this package you have everything you need for analyzing biopsychological data, including:

  • Data processing pipelines for various physiological signals (ECG, EEG, Respiration, Motion, …).

  • Algorithms and data processing pipelines for sleep/wake prediction and computation of sleep endpoints based on activity or IMU data.

  • Functions to import and process data from sleep trackers (e.g., Withings Sleep Analyzer)

  • Functions for processing and analysis of salivary biomarker data (cortisol, amylase).

  • Implementation of various psychological and HCI-related questionnaires.

  • Implementation of classes representing different psychological protocols (e.g., TSST, MIST, Cortisol Awakening Response Assessment, etc.)

  • Functions for easily setting up statistical analysis pipelines.

  • Functions for setting up and evaluating machine learning pipelines.

  • Plotting wrappers optimized for displaying biopsychological data.


Analysis of Physiological Signals

ECG Processing

BioPsyKit provides a whole ECG data processing pipeline, consisting of:

  • Loading ECG data from:

    • Generic .csv files

    • NilsPod binary (.bin) files (requires NilsPodLib)

    • Other sensor types (coming soon)

  • Splitting data into single study parts (based on time intervals) that will be analyzed separately

  • Performing ECG processing, including:

    • R peak detection (using Neurokit)

    • R peak outlier removal and interpolation

    • HRV feature computation

    • ECG-derived respiration (EDR) estimation for respiration rate and respiratory sinus arrhythmia (RSA) (experimental)

    • Instantaneous heart rate resampling

    • Computing aggregated results (e.g., mean and standard error) per study part

  • Creating plots for visualizing processing results

Quick Example

from biopsykit.signals.ecg import EcgProcessor
from biopsykit.example_data import get_ecg_example

ecg_data, sampling_rate = get_ecg_example()

ep = EcgProcessor(ecg_data, sampling_rate)


… more biosignals coming soon!

Sleep/Wake Prediction

BioPsyKit allows to process sleep data collected from IMU or activity sensors (e.g., Actigraphs). This includes:

  • Detection of wear periods

  • Detection of time spent in bed

  • Detection of sleep and wake phases

  • Computation of sleep endpoints (e.g., sleep and wake onset, net sleep duration wake after sleep onset, etc.)

Quick Example

import biopsykit as bp
from biopsykit.example_data import get_sleep_imu_example

imu_data, sampling_rate = get_sleep_imu_example()

sleep_results = bp.sleep.sleep_processing_pipeline.predict_pipeline_acceleration(imu_data, sampling_rate)
sleep_endpoints = sleep_results["sleep_endpoints"]


Salivary Biomarker Analysis

BioPsyKit provides several methods for the analysis of salivary biomarkers (e.g. cortisol and amylase), such as:

  • Import data from Excel and csv files into a standardized format

  • Compute standard features (maximum increase, slope, area-under-the-curve, mean, standard deviation, …)

Quick Example

import biopsykit as bp
from biopsykit.example_data import get_saliva_example

saliva_data = get_saliva_example(sample_times=[-20, 0, 10, 20, 30, 40, 50])

max_inc = bp.saliva.max_increase(saliva_data)
# remove the first saliva sample (t=-20) from computing the AUC
auc = bp.saliva.auc(saliva_data, remove_s0=True)



BioPsyKit implements various established psychological (state and trait) questionnaires, such as:

  • Perceived Stress Scale (PSS)

  • Positive and Negative Affect Schedule (PANAS)

  • Self-Compassion Scale (SCS)

  • Big Five Inventory (BFI)

  • State Trait Depression and Anxiety Questionnaire (STADI)

  • Trier Inventory for Chronic Stress (TICS)

  • Primary Appraisal Secondary Appraisal Scale (PASA)

Quick Example

import biopsykit as bp
from biopsykit.example_data import get_questionnaire_example

data = get_questionnaire_example()

pss_data = data.filter(like="PSS")
pss_result = bp.questionnaires.pss(pss_data)


List Supported Questionnaires

import biopsykit as bp


Psychological Protocols

BioPsyKit implements methods for easy handling and analysis of data recorded with several established psychological protocols, such as:

  • Montreal Imaging Stress Task (MIST)

  • Trier Social Stress Test (TSST)

  • Cortisol Awakening Response Assessment (CAR)

Quick Example

from biopsykit.protocols import TSST
from biopsykit.example_data import get_saliva_example
from biopsykit.example_data import get_hr_subject_data_dict_example
# specify TSST structure and the durations of the single phases
structure = {
   "Pre": None,
   "TSST": {
       "Preparation": 300,
       "Talk": 300,
       "Math": 300
   "Post": None
tsst = TSST(name="TSST", structure=structure)

saliva_data = get_saliva_example(sample_times=[-20, 0, 10, 20, 30, 40, 50])
hr_subject_data_dict = get_hr_subject_data_dict_example()
# add saliva data collected during the whole TSST procedure
tsst.add_saliva_data(saliva_data, saliva_type="cortisol")
# add heart rate data collected during the "TSST" study part
tsst.add_hr_data(hr_subject_data_dict, study_part="TSST")
# compute heart rate results: normalize ECG data relative to "Preparation" phase; afterwards, use data from the 
# "Talk" and "Math" phases and compute the average heart rate for each subject and study phase, respectively
        "normalize_to": "Preparation",
        "select_phases": ["Talk", "Math"]

Statistical Analysis

BioPsyKit implements methods for simplified statistical analysis of biopsychological data by offering an object-oriented interface for setting up statistical analysis pipelines, displaying the results, and adding statistical significance brackets to plots.

Quick Example

import matplotlib.pyplot as plt
from biopsykit.stats import StatsPipeline
from biopsykit.plotting import multi_feature_boxplot
from biopsykit.example_data import get_stats_example

data = get_stats_example()

# configure statistical analysis pipeline which consists of checking for normal distribution and performing paired 
# t-tests (within-variable: time) on each questionnaire subscale separately (grouping data by subscale).
pipeline = StatsPipeline(
    steps=[("prep", "normality"), ("test", "pairwise_ttests")],
    params={"dv": "PANAS", "groupby": "subscale", "subject": "subject", "within": "time"}

# apply statistics pipeline on data

# plot data and add statistical significance brackets from statistical analysis pipeline
fig, axs = plt.subplots(ncols=3)
features = ["NegativeAffect", "PositiveAffect", "Total"]
# generate statistical significance brackets
box_pairs, pvalues = pipeline.sig_brackets(
    "test", stats_effect_type="within", plot_type="single", x="time", features=features, subplots=True
# plot data
    data=data, x="time", y="PANAS", features=features, group="subscale", order=["pre", "post"],
    stats_kwargs={"box_pairs": box_pairs, "pvalues": pvalues}, ax=axs

Machine Learning Analysis

BioPsyKit implements methods for simplified and systematic evaluation of different machine learning pipelines.

Quick Example

# Utils
from sklearn.datasets import load_breast_cancer
# Preprocessing & Feature Selection
from sklearn.feature_selection import SelectKBest
from sklearn.preprocessing import MinMaxScaler, StandardScaler
# Classification
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
# Cross-Validation
from sklearn.model_selection import KFold

from biopsykit.classification.model_selection import SklearnPipelinePermuter

# load example dataset
breast_cancer = load_breast_cancer()
X =
y =

# specify estimator combinations
model_dict = {
    "scaler": {
        "StandardScaler": StandardScaler(),
        "MinMaxScaler": MinMaxScaler()
    "reduce_dim": {
        "SelectKBest": SelectKBest(),
    "clf" : {
        "KNeighborsClassifier": KNeighborsClassifier(),
        "DecisionTreeClassifier": DecisionTreeClassifier(),
# specify hyperparameter for grid search
params_dict = {
    "StandardScaler": None,
    "MinMaxScaler": None,
    "SelectKBest": { "k": [2, 4, "all"] },
    "KNeighborsClassifier": { "n_neighbors": [2, 4], "weights": ["uniform", "distance"] },
    "DecisionTreeClassifier": {"criterion": ['gini', 'entropy'], "max_depth": [2, 4] },

pipeline_permuter = SklearnPipelinePermuter(model_dict, params_dict), y, outer_cv=KFold(5), inner_cv=KFold(5))

# print mean performance scores for each pipeline and parameter combinations, averaged over all outer CV folds
# print overall best-performing pipeline and the performances over all outer CV folds
# print summary of all relevant metrics for the best pipeline for each evaluated pipeline combination


BioPsyKit requires Python >=3.8. First, install a compatible version of Python. Then install BioPsyKit via pip.

Installation from PyPi:

pip install biopsykit

Installation from PyPi with extras (e.g., jupyter to directly install all required dependencies for the use with Jupyter Lab):

pip install "biopsykit[jupyter]"

Installation from local repository copy:

git clone
cd BioPsyKit
pip install .

For Developer

If you are a developer and want to contribute to BioPsyKit you can install an editable version of the package from a local copy of the repository.

BioPsyKit uses poetry to manage dependencies and packaging. Once you installed poetry, run the following commands to clone the repository, initialize a virtual env and install all development dependencies:

Without Extras

git clone
cd BioPsyKit
poetry install

With all Extras (e.g., extended functionalities for IPython/Jupyter Notebooks)

git clone
cd BioPsyKit
poetry install -E mne -E jupyter 

To run any of the tools required for the development workflow, use the poe commands of the poethepoet task runner:

$ poe
docs                 Build the html docs using Sphinx.
format               Reformat all files using black.
format_check         Check, but not change, formatting using black.
lint                 Lint all files with Prospector.
test                 Run Pytest with coverage.
update_version       Bump the version in pyproject.toml and biopsykit.__init__ .
register_ipykernel   Register a new IPython kernel named `biopsykit` linked to the virtual environment.
remove_ipykernel     Remove the associated IPython kernel.

Some Notes

  • The poe commands are only available if you are in the virtual environment associated with this project. You can either activate the virtual environment manually (e.g., source .venv/bin/activate) or use the poetry shell command to spawn a new shell with the virtual environment activated.

  • In order to use jupyter notebooks with the project you need to register a new IPython kernel associated with the venv of the project (poe register_ipykernel - see below). When creating a notebook, make to sure to select this kernel (top right corner of the notebook).

  • In order to build the documentation, you need to additionally install pandoc.

See the Contributing Guidelines for further information.


See the Examples Gallery for example on how to use BioPsyKit.

Citing BioPsyKit

If you use BioPsyKit in your work, please report the version you used in the text. Additionally, please also cite the corresponding paper:

Richer et al., (2021). BioPsyKit: A Python package for the analysis of biopsychological data. Journal of Open Source Software, 6(66), 3702,

If you use a specific algorithm please also to make sure you cite the original paper of the algorithm! We recommend the following citation style:

We used the algorithm proposed by Author et al. [paper-citation], implemented by the BioPsykit package [biopsykit-citation].