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neurokit2

Extract cardiovascular, neural, and autonomic biomarkers from biosignals using NeuroKit2.

1|Updated Mar 4, 2026
Search Tags:#eda#signal-processing#biosignals#hrv#eeg#neurokit2#rsp
One-click install
npx skills add https://github.com/Hung-3008/agusta --skill neurokit2-hung-3008
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: neurokit2
Source: https://github.com/Hung-3008/agusta/tree/main/.agents/skills/neurokit2
Command: npx skills add https://github.com/Hung-3008/agusta --skill neurokit2-hung-3008

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

NeuroKit2 provides a comprehensive, modular Python toolkit for processing and analyzing physiological signals (biosignals) such as ECG, EEG, EDA, RSP, PPG, EMG, and EOG, enabling researchers to extract robust metrics and run multi-modal analyses with a consistent API.

Core Features & Use Cases

  • Supports a wide range of signals and metrics (HRV, EEG power, SCR analyses, respiration, EMG, blink detection) with unified workflows.
  • Enables multi-modal processing and analysis via bio_process and bio_analyze, plus event-related and interval-related paradigms for both short and long recordings.
  • Real-world applications include psychophysiology experiments, clinical research, and human-computer interaction studies requiring integrated biomarker extraction and visualization.

Quick Start

Process sample ECG data with the NeuroKit2 pipeline and extract HRV metrics.

Frequently Asked Questions about neurokit2

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract HRV and EDA biomarkers from biosignals in Python?▼

You can extract HRV and EDA biomarkers by running physiological signals through a unified biosignal processing pipeline. This toolkit identifies robust metrics for cardiovascular and autonomic analyses using a consistent modular API.

Can I process multi-modal biosignals like EEG, ECG, and EMG together?▼

Yes, multi-modal biosignal processing is supported. You can integrate EEG, ECG, and EMG analysis using combined processing and analyze functions, enabling event-related and interval-related paradigms across sessions.

Does biosignal processing for EEG source localization require MNE?▼

Full EEG workflows, microstate analysis, and source localization require optional EEG tooling like MNE. The core biosignal processing pipeline itself requires Python with NeuroKit2 installed.

What is the best way to run event-related psychophysiology analysis on ECG recordings?▼

The best way to run event-related psychophysiology analysis is using a modular biosignal processing toolkit. It supports event-related paradigms for short and long recordings to extract integrated cardiovascular metrics.

Why does my EEG power analysis need a unified Python API?▼

A unified Python API is needed for EEG power analysis to ensure consistent extraction of neural biomarkers across diverse physiological signals. This prevents fragmented workflows when running multi-modal studies.

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