neurokit2

Process and analyze physiological signals like ECG and EEG with Python.

1|Updated Mar 12, 2026
One-click install
npx skills add https://github.com/yf8578/clawomics --skill neurokit2-yf8578
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: neurokit2
Source: https://github.com/yf8578/clawomics/tree/main/skills/neurokit2
Command: npx skills add https://github.com/yf8578/clawomics --skill neurokit2-yf8578

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the complex challenge of processing and analyzing diverse physiological signals, turning raw biosignal data into actionable insights for research and clinical applications.

Core Features & Use Cases

  • Multi-Signal Processing: Integrates ECG, EEG, EDA, RSP, EMG, EOG, and PPG signals.
  • Advanced Analysis: Offers comprehensive tools for HRV, complexity, microstates, and event-related analysis.
  • Use Case: A researcher studying stress responses can use this Skill to simultaneously process ECG for heart rate variability, EDA for skin conductance, and RSP for breathing patterns, providing a holistic view of autonomic nervous system activity.

Quick Start

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

Frequently Asked Questions about neurokit2

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

FAQPage Schema
How do I process ECG and EEG signals for psychophysiology research?

To process ECG and EEG signals for psychophysiology research, this toolkit provides signal cleaning, peak detection, and feature extraction to compute metrics like heart rate variability and event-related responses across multiple physiological modalities.

What is the best way to compute heart rate variability from raw ECG data?

To compute heart rate variability from raw ECG data, apply robust signal cleaning followed by peak detection and feature extraction. This toolkit facilitates advanced HRV analysis to yield actionable autonomic nervous system insights for clinical research.

Can I simultaneously analyze EDA and RSP signals to study stress responses?

You can simultaneously analyze EDA and RSP signals to study stress responses. This toolkit integrates multi-signal processing to evaluate skin conductance and breathing patterns alongside ECG, providing a holistic view of autonomic nervous system activity.

Does this biosignal processing toolkit support EEG microstates and complexity measures?

This biosignal processing toolkit supports EEG microstates and complexity measures. It offers advanced analyses across multiple physiological modalities, allowing you to decompose signals and extract complex features for human-computer interaction applications.

How do I extract features from EMG and EOG signals for human-computer interaction?

To extract features from EMG and EOG signals for human-computer interaction, this toolkit provides robust decomposition and feature extraction tools. It processes physiological signals to turn raw biosignal data into actionable insights for research applications.