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bio-neurokit2

Process and analyze physiological signals with the neurokit2 Python package.

Updated Mar 13, 2026
Search Tags:#biosignal processing#EEG analysis#HRV analysis#neurokit2#physiological data#EDA analysis
One-click install
npx skills add https://github.com/biomaps-infra/blender-opencode --skill bio-neurokit2
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-neurokit2
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/bio-neurokit2
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill bio-neurokit2

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires neurokit2, and includes references (resource) components.

What problem does it solve?

This Skill automates the complex processing and analysis of physiological biosignals, making advanced physiological data analysis accessible.

Core Features & Use Cases

  • Comprehensive Signal Processing: Handles ECG, EEG, EDA, RSP, PPG, EMG, EOG signals.
  • Advanced Analysis: Includes HRV, microstate analysis, complexity measures, and event-related analysis.
  • Use Case: Analyze a multi-channel physiological recording from a psychophysiology experiment to extract heart rate variability, electrodermal responses, and brain activity patterns related to specific stimuli.

Quick Start

Use the bio-neurokit2 skill to process an ECG signal and analyze its heart rate variability.

Frequently Asked Questions about bio-neurokit2

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

FAQPage Schema
How do I process and analyze physiological biosignals like ECG and EEG?▼

You can process physiological biosignals by applying signal-specific algorithms to clean, filter, and extract features from ECG, EEG, EDA, RSP, PPG, EMG, and EOG data for comprehensive physiological insights.

What is the best way to calculate heart rate variability from ECG recordings?▼

Heart rate variability is calculated by detecting R-peaks in ECG signals and computing time-domain, frequency-domain, and non-linear complexity measures to assess autonomic nervous system activity.

Can I extract event-related potentials and identify microstates from EEG data?▼

EEG analysis supports event-related potential extraction and microstate identification by segmenting continuous brain signals into epochs time-locked to experimental stimuli for detailed neural pattern analysis.

Does neurokit2 support simultaneous analysis of electrodermal activity and respiration?▼

Yes, neurokit2 supports simultaneous analysis of electrodermal activity and respiration by processing multiple physiological signal types within the same workflow to derive synchronized psychophysiological metrics.

What Python environment do I need to run advanced biosignal processing and complexity measures?▼

You need a Python environment with the neurokit2 package installed, as it provides the underlying functions required for advanced biosignal processing, complexity measures, and signal-specific feature extraction.

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