neurokit2

Process and analyze physiological signals like ECG, EEG, and EDA with neurokit2.

Updated Mar 10, 2026

Search Tags:#neuroscience #HRV #biosignal processing #physiology #ECG analysis #EEG analysis

Ready to run

npx skills add https://github.com/Yezez9/Research-Agent --skill neurokit2-yezez9

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: neurokit2
Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/neurokit2
Command: npx skills add https://github.com/Yezez9/Research-Agent --skill neurokit2-yezez9

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires neurokit2, and includes references (resource) components.

What problem does it solve?

This Skill simplifies the complex task of analyzing physiological signals like ECG, EEG, and EDA, making advanced biosignal processing accessible for research and clinical applications.

Core Features & Use Cases

Multi-Signal Processing: Unified tools for ECG, EEG, EDA, RSP, EMG, EOG, and PPG.
Advanced Analysis: Includes HRV, microstates, complexity measures, and event-related analysis.
Use Case: Analyze a subject's ECG, respiration, and EDA simultaneously during a stress-inducing task to understand their autonomic nervous system response, identifying changes in heart rate variability, breathing patterns, and electrodermal activity.

Quick Start

Use the neurokit2 skill to process an ECG signal and compute its heart rate variability.

Frequently Asked Questions about neurokit2

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

FAQPage Schema

How do I compute heart rate variability from an ECG signal in Python?▼

You compute heart rate variability (HRV) by processing ECG signals using the neurokit2 Python library. This toolkit provides unified functions to clean ECG data, detect R-peaks, and calculate advanced HRV metrics for physiological analysis.

Can I analyze multiple biosignals like EEG and EDA simultaneously for autonomic nervous system response?▼

Yes, you can analyze multiple biosignals like EEG and EDA simultaneously. The toolkit processes ECG, EEG, EDA, RSP, EMG, EOG, and PPG together to identify changes in autonomic nervous system response during tasks.

What is the best way to perform event-related analysis on physiological signals?▼

The best way to perform event-related analysis on physiological signals is using a comprehensive Python biosignal processing library. neurokit2 facilitates event-related analysis alongside complexity measures and EEG microstates.

Does neurokit2 support EEG microstate analysis?▼

Yes, neurokit2 supports EEG microstate analysis. It provides a comprehensive Python toolkit for processing and analyzing physiological signals, specifically including advanced features for EEG microstates and complexity measures.

Do I need to install the neurokit2 library before processing biosignals?▼

Yes, you need to install the neurokit2 library before processing biosignals. The Skill requires this dependency to execute its signal processing and advanced physiological analysis workflows.

Why use a unified Python toolkit for biosignal processing instead of separate scripts?▼

Using a unified Python toolkit for biosignal processing simplifies analyzing signals like ECG, EEG, and EDA. It makes advanced physiological analysis accessible for research by providing consistent processing functions across multiple biosignal domains.