neurokit2

Process and analyze ECG, EEG, EDA, RESP, EMG, EOG, and PPG biosignals.

21|1|Updated Mar 19, 2026
One-click install
npx skills add https://github.com/OwnLabAI/ownlab --skill neurokit2-ownlabai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: neurokit2
Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/neurokit2
Command: npx skills add https://github.com/OwnLabAI/ownlab --skill neurokit2-ownlabai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

NeuroKit2 provides a comprehensive, Python-based toolkit for processing, analyzing, and integrating a wide range of physiological signals (biosignals) such as ECG, EEG, EDA, RESP, EMG, EOG, and PPG, enabling researchers to extract meaningful metrics with reproducible workflows.

Core Features & Use Cases

  • Multi-signal processing and analysis across cardiac, neural, autonomic, respiratory, and muscular domains.
  • HRV, RSA, brain microstates, and cross-signal coupling for holistic psychophysiology research.
  • Modular workflows (ecg_process, rsp_process, eda_process, bio_process, eeg_power, microstates_segment, etc.) with MNE and NeuroKit2 integration.
  • Extensive reference documentation in references/ supporting education and replication.

Quick Start

Install NeuroKit2 and run a simple ECG workflow to see cleaned signals and HRV 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 biosignals together for multimodal analysis?▼

HRV analysis extracts heart rate variability metrics from ECG data using modular workflows like ecg_process. It cleans raw cardiac signals and computes HRV metrics, enabling reproducible psychophysiology and clinical research workflows.

Can I compute brain microstates and EEG power spectra using Python?▼

Yes, EEG power and microstate analysis compute spectral power and segment brain microstates from raw EEG data. Modular functions like eeg_power and microstates_segment integrate with MNE to support neuroscience research and clinical contexts.

Does this biosignal processing toolkit work with MNE?▼

Yes, multimodal biosignal processing integrates with MNE for EEG workflows. Modular pipelines like bio_process handle cardiac, neural, and autonomic signals, enabling cross-signal coupling and holistic psychophysiology research.

What is the best way to extract RSA and EDA metrics for psychophysiology research?▼

Extracting RSA and EDA metrics requires modular workflows like rsp_process and eda_process that clean raw respiratory and electrodermal signals. This computes autonomic metrics, enabling reproducible psychophysiology and human-computer interaction research.

Are there limitations when processing multimodal physiological signals for clinical research?▼

Biosignal processing for clinical research requires clean raw physiological data inputs for accurate HRV, RSA, and cross-signal coupling results. Signal artifacts and incomplete multimodal datasets limit accuracy, requiring careful preprocessing for reproducible workflows.