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neurokit2

Process and analyze physiological biosignals with the NeuroKit2 Python library.

298|27|Updated Feb 18, 2026
Search Tags:#EDA#HRV#EEG#EMG#neurophysiology#biosignal processing#ECG analysis
One-click install
npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill neurokit2-jaechang-hits
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: neurokit2
Source: https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/scientific-computing/neurokit2
Command: npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill neurokit2-jaechang-hits

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires neurokit2, numpy, pandas, matplotlib, scipy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies the complex task of processing and analyzing physiological signals like ECG, EDA, EMG, and EEG, making advanced biosignal analysis accessible.

Core Features & Use Cases

  • Signal Processing: Clean, filter, and extract features from various physiological signals.
  • Feature Extraction: Compute metrics like Heart Rate Variability (HRV), skin conductance responses (SCR), and muscle activation onsets.
  • Use Case: Analyze a 24-hour ECG recording to extract HRV features for assessing stress levels and cardiovascular health.

Quick Start

Use the neurokit2 skill to process an ECG signal and extract HRV time-domain features.

Frequently Asked Questions about neurokit2

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

FAQPage Schema
How do I extract heart rate variability features from an ECG signal in Python?▼

You can extract heart rate variability features from an ECG signal using the neurokit2 Python library, which computes time-domain metrics for cardiovascular health and stress assessment. It requires numpy and pandas for data manipulation.

What is the best way to process EDA signals and identify skin conductance responses?▼

Processing EDA signals to identify skin conductance responses is best handled by the neurokit2 library, which cleans and filters physiological data to extract neurophysiological features. It uses scipy for signal filtering and matplotlib for visualization.

Can I analyze EMG and EEG biosignals to detect muscle activation onsets?▼

You can analyze EMG and EEG biosignals to detect muscle activation onsets using the neurokit2 library for psychophysiological research. The Skill processes physiological signals and extracts relevant features using numpy and pandas.

Do I need matplotlib and pandas to perform biosignal processing for neurophysiological research?▼

You need matplotlib and pandas to perform biosignal processing because neurokit2 depends on them for data manipulation and visualization. The Skill also requires numpy and scipy to compute physiological metrics like heart rate variability.

Does neurokit2 support PPG signal analysis for psychophysiological research?▼

Neurokit2 supports PPG signal analysis for psychophysiological research alongside ECG, EDA, EMG, and EEG processing. It extracts physiological features and requires numpy, pandas, and matplotlib to manipulate and visualize the biosignal data.

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