# neuropsychology/NeuroKit

NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing

Repository: https://github.com/neuropsychology/NeuroKit
Canonical: https://ross.abutalabs.com/products/neurokit
Homepage: https://neuropsychology.github.io/NeuroKit
Language: Python
License: MIT
License Family: permissive
Topics: python, ecg, eda, emg, eeg, ppg, signal, physiology, biosignals, scr, skin-conductance, heart-rate, hrv, tutorial, signal-processing, cardiac, software, eog, entropy, hacktoberfest
Last push: 2026-08-07T23:42:29+00:00

## Health v2 (maintenance only)
Score: 80/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 48, longevity 100
- inputs: {"age_days": 2500, "days_push": 26, "days_rel": 184, "gap_med": 146.0, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2332, forks 538 (observed 2026-08-28T04:06:37.811067+00:00)

## What it is
NeuroKit2 is a Python library for neurophysiological and biosignal processing, offering easy access to advanced routines for signals like ECG, EEG, EDA, EMG, and PPG. It lets researchers and clinicians analyze physiological data with minimal code, from preprocessing to feature extraction.

## Use cases
- analyze ECG signals and compute heart rate variability in Python
- process EEG data for research studies
- extract features from skin conductance (EDA/SCR) recordings
- preprocess and analyze EMG muscle signals
- detect peaks in PPG photoplethysmography signals
- compute entropy and complexity measures of biosignals
- batch-process physiological data from experiments with two lines of code

## When to choose
- you need user-friendly biosignal processing without deep signal-processing expertise
- you work with multiple physiological signal types (ECG, EEG, EDA, EMG, PPG, RSP) in one pipeline
- you want well-documented, community-maintained Python tools for physiological research
- you need quick feature extraction for heart rate variability or cardiac analysis

## When to avoid
- you need real-time or low-latency signal processing in production systems
- you require highly customizable, low-level control over filtering algorithms
- you work outside Python or need GUI-based analysis tools
- you need clinical-grade certified medical device software

## Facets
- artifact type: library
- maturity: active
- function: data-science, analytics, machine-learning
- domain: healthcare, data-science, bioinformatics
- platform: python, cross-platform
- tags: biosignals, ecg, eeg, eda, emg, ppg, hrv, heart-rate-variability, physiology, neurophysiology, entropy, signal-processing, research

## Member repositories
- neuropsychology/NeuroKit (main) score 80

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:37.811067+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:37:57.268519+00:00, confidence not recorded.
  - readme: https://github.com/neuropsychology/NeuroKit (fetched 2026-08-28T04:06:37.811067+00:00, sha 3bf26a5da538)
  - homepage: https://neuropsychology.github.io/NeuroKit (fetched 2026-08-29T10:18:27.518806+00:00, sha 7b3033c8b85a)
- Data as of 2026-08-30T08:39:29.467469+00:00.