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neurokit2

Preprocess ECG, EEG, EDA, RSP, EMG, and EOG signals with NeuroKit2.

1|Updated Apr 8, 2026
Search Tags:#multimodal#eda#biosignals#hrv#eeg#neurokit2#rsp
One-click install
npx skills add https://github.com/brainworkup/skills --skill neurokit2-brainworkup
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: neurokit2
Source: https://github.com/brainworkup/skills/tree/main/neuropsych-reports/references/luria-related-complement-skills/neurokit2
Command: npx skills add https://github.com/brainworkup/skills --skill neurokit2-brainworkup

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

NeuroKit2 provides a unified toolkit to streamline biosignal preprocessing, feature extraction, and analysis across ECG, EEG, EDA, RSP, PPG, EMG, and EOG, saving researchers time and reducing manual scripting.

Core Features & Use Cases

  • Multimodal signal processing: preprocess, detect peaks, compute HRV, SCR, respiration metrics, and microstate analyses in a single workflow.
  • Extensive documentation: access comprehensive reference materials for ECG, HRV, EEG, EDA, RSP, EMG, EOG, and related modules.
  • Use Case: in a psychophysiology study, process ECG, EDA, and respiration data together to derive RSA and SCR metrics.

Quick Start

Process a sample multi-modal recording to obtain cleaned signals and basic metrics.

Frequently Asked Questions about neurokit2

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

FAQPage Schema
How do I preprocess multimodal biosignals like ECG and EDA in Python?▼

You can preprocess multimodal biosignals by orchestrating signal cleaning, peak detection, and feature extraction across ECG, EDA, RSP, EEG, EMG, and EOG data to derive clean data and key physiological metrics using Python.

What is the best way to compute HRV and SCR metrics for psychophysiology research?▼

The best way to compute HRV and SCR metrics is through a unified workflow that processes ECG and EDA signals together, extracting heart rate variability and electrodermal activity features for clinical studies and resting-state analysis.

How does cross-signal coupling work between EEG and respiration data?▼

Cross-signal coupling works by integrating cleaned EEG and RSP data within a single workflow, allowing researchers to analyze interactions between brain activity and respiration metrics during event-related experiments and multimodal data integration.

Can I use NeuroKit2 for event-related experiments and resting-state analysis?▼

Yes, NeuroKit2 supports event-related experiments and resting-state analysis by providing workflows that derive clean physiological signals and key metrics applicable to psychophysiology research and human-computer interaction tasks.

Do I need a specific Python environment to run biosignal processing workflows?▼

Yes, you need a Python environment with NeuroKit2 installed to run biosignal processing workflows, which orchestrate signal cleaning, peak detection, HRV and EDA analysis, and cross-signal coupling across multiple physiological signal types.

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