neuropsychology/NeuroKit

Health v2 · maintenance only

Adoption not part of the score

2332 stars · 538 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity active

data-science analytics machine-learning healthcare data-science bioinformatics python cross-platform biosignals ecg eeg eda emg ppg hrv heart-rate-variability physiology neurophysiology entropy signal-processing research

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For agents

markdown · JSON · MCP: product_card(name="neuropsychology/NeuroKit")