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RamanEbrahimi
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neurokit2

Analyze ECG, EEG, EDA, RSP, PPG, EMG and EOG signals with Python using NeuroKit2.

3|Updated Apr 17, 2026
Search Tags:#machine learning#bioinformatics#signal processing#neuroscience#psychophysiology#physiological signals
One-click install
npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill neurokit2-ramanebrahimi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: neurokit2
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/neurokit2
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill neurokit2-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires neurokit, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides comprehensive analysis of physiological signals, including ECG, EEG, EDA, RSP, PPG, EMG, and EOG, for various research and clinical applications.

Core Features & Use Cases

  • Cardiac Signal Processing: Analyze ECG and PPG signals for cardiovascular analysis, including HRV and pulse analysis.
  • Brain Signal Analysis: Analyze EEG signals for frequency power, complexity, and microstate patterns.
  • Autonomic Nervous System Assessment: Assess autonomic activity using EDA signals and sympathetic index calculations.
  • Respiratory Pattern Analysis: Analyze RSP signals for breathing rate, respiratory variability, and volume per time.
  • Muscle Activity Analysis: Analyze EMG signals for activation detection and amplitude analysis.
  • Eye Tracking Analysis: Analyze EOG signals for blink detection and analysis.
  • Multi-modal Signal Integration: Process multiple physiological signals simultaneously for comprehensive analysis.
  • Complexity Analysis: Compute nonlinear dynamics, fractal dimensions, and information-theoretic measures for signal complexity assessment.
  • Event-related Analysis: Create epochs around stimulus events and analyze physiological responses.
  • Multi-Signal Integration: Process multiple physiological signals simultaneously with unified output.
  • Analysis Modes: Automatically selects between event-related and interval-related analysis based on data duration.
  • Installation: Simple installation via pip with support for development versions.
  • Quick Start: Example workflows for ECG analysis, multi-modal analysis, and event-related potential analysis are provided.

Quick Start

Use the neurokit2 skill to process an ECG signal and perform HRV analysis. Example command: use neurokit2 -c "ecg_process(ecg_signal, sampling_rate=1000)" -c "hrv(peaks, sampling_rate=1000)".

Frequently Asked Questions about neurokit2

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

FAQPage Schema
How do I process ECG signals and calculate heart rate variability?▼

To process ECG signals and calculate heart rate variability, you can use the neurokit2 skill to clean raw data, detect R-peaks, and compute HRV metrics. It automatically selects interval-related or event-related analysis modes based on your data duration.

Can I analyze multiple physiological signals simultaneously for psychophysiology research?▼

Yes, you can analyze multiple physiological signals simultaneously. The skill supports multi-modal integration, allowing you to process combinations of ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals with unified output for comprehensive psychophysiology research.

What Python environment do I need for physiological signal processing?▼

You need a Python environment with the neurokit2 library installed, which requires various dependencies for signal processing and analysis. Installation is straightforward via pip, and the skill handles various data formats and processing workflows.

How does EEG signal analysis handle frequency power and complexity?▼

EEG signal analysis handles frequency power and complexity by computing frequency power bands, nonlinear dynamics, fractal dimensions, and information-theoretic measures. This allows detailed assessment of brain signal microstate patterns and overall signal complexity.

Does autonomic nervous system assessment work with EDA signals?▼

Yes, autonomic nervous system assessment works with EDA signals. The skill analyzes electrodermal activity to assess autonomic activity by calculating sympathetic indices, making it suitable for clinical applications and human-computer interaction studies.

How do I create epochs around stimulus events for event-related analysis?▼

To create epochs around stimulus events for event-related analysis, the skill extracts physiological responses from your continuous data based on stimulus markers. It automatically determines whether to apply event-related or interval-related analysis based on the duration of your data.

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