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alterlab-neurokit2

Preprocesses and analyzes ECG, EEG, EDA, EMG, EOG, PPG and respiration data with NeuroKit2 in Python for cross-signal metrics like HRV and RSA.

58|9|Updated Mar 16, 2026
Search Tags:#multimodal#eda#biosignals#hrv#eeg#neurokit2#rsp
One-click install
npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-neurokit2
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: alterlab-neurokit2
Source: https://github.com/AlterLab-IEU/AlterLab-Academic-Skills/tree/main/skills/clinical-research/alterlab-neurokit2
Command: npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-neurokit2

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

NeuroKit2 streamlines the messy, multi-step workflow of processing and analyzing diverse physiological signals by providing a cohesive Python toolkit with comprehensive preprocessing, feature extraction, and multi-modal integration capabilities for research.

Core Features & Use Cases

  • Unified multi-signal pipelines: preprocess, analyze, and integrate ECG, EEG, EDA, EMG, EOG, PPG, and respiration data in a single framework.
  • Rich analytics library: compute HRV, EEG power, microstates, SCRs, respiration metrics, and cross-signal indices like RSA.
  • Use Case: In a psychophysiology study, process a 5-minute recording with ECG, EDA, and respiration to derive HRV, RSA, and arousal indicators for condition comparisons.

Quick Start

Process a sample ECG and respiration dataset to generate HRV and RSA metrics and visualize the results.

Frequently Asked Questions about alterlab-neurokit2

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

FAQPage Schema
How do I process ECG, EEG, and EDA signals together in a single Python workflow?▼

To process multimodal biosignals, use a unified Python framework that applies modular pipelines to clean, analyze, and integrate ECG, EEG, and EDA data, enabling end-to-end physiological workflows and cross-signal analytics.

What is the best way to compute HRV and RSA metrics from respiration and ECG data?▼

Computing HRV and RSA metrics requires a multi-signal pipeline that processes ECG and respiration data to derive heart rate variability and respiratory sinus arrhythmia indices for psychophysiology condition comparisons.

Can I perform event-related analysis on EEG and EDA recordings for neuroscience research?▼

Event-related biosignal analysis is supported by creating epochs from EEG and EDA recordings, allowing you to extract features like EEG power and skin conductance responses for neuroscience and psychology research workflows.

Does this biosignal framework require specific Python dependencies for EMG and PPG preprocessing?▼

This biosignal framework operates in Python without requiring external dependencies, providing built-in modular pipelines to preprocess, analyze, and extract features from EMG and PPG data natively.

What are the limitations of unified physiological signal processing for multi-modal integration?▼

Unified physiological signal processing requires properly synchronized multi-signal recordings and structured epoch creation, meaning misaligned timestamps or noisy data may limit the accuracy of cross-signal metrics and analytics.

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