neurokit2

Process and analyze multi-modal biosignals with a unified Python toolkit.

22|4|Updated May 25, 2026

Search Tags:#multimodal #rsa #biosignals #hrv #eeg #neurokit2

Ready to run

npx skills add https://github.com/crazymsn/academic-skills --skill neurokit2-crazymsn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: neurokit2
Source: https://github.com/crazymsn/academic-skills/tree/main/academic-skills/neurokit2
Command: npx skills add https://github.com/crazymsn/academic-skills --skill neurokit2-crazymsn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

NeuroKit2 provides a comprehensive toolkit to preprocess, analyze, and integrate multiple biosignals (ECG, EEG, EDA, RSP, EMG, EOG, and PPG) for research-grade insights across domains such as psychophysiology, neuroscience, and clinical science.

Core Features & Use Cases

Unified multi-modal processing: harmonizes signals, computes cross-signal metrics (e.g., RSA, HRV) and supports event-related and interval analyses.
Rich reference and workflow support: extensive reference documentation and tutorials guide end-to-end analyses from raw data to interpretable results.
Flexible integration: supports optional scripts/references/assets and adapts to various sampling rates and modalities for robust research pipelines.

Quick Start

Process multi-modal biosignals by loading your data and running the NeuroKit2 workflow to obtain integrated metrics.

Frequently Asked Questions about neurokit2

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

FAQPage Schema

How do I process multimodal biosignals like ECG and EEG together in Python?▼

Cross-signal metrics like RSA and HRV are computed by harmonizing multiple biosignals within a unified Python pipeline, supporting both event-related and interval-related analyses to extract integrated physiological indices.

Can I compute RSA and HRV from synchronized ECG and respiration signals?▼

What is the best way to analyze psychophysiology data across different sampling rates?▼

Event-related and interval-related biosignal analyses are both supported, allowing researchers to segment raw multimodal data into epochs and extract interpretable physiological features from specific experimental events or continuous intervals.

Does this biosignal processing approach support event-related and interval-related analyses?▼

Do I need specific physiological data formats to extract features from EDA and PPG signals?▼

You need raw physiological data from supported modalities like EDA and PPG, which the toolkit preprocesses to extract synchronized features, adapting to various sampling rates without requiring a strictly predefined data format.