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neurokit2

Process raw physiological signals into cleaned biosignal features for ECG, EEG, EDA, RSP, PPG, EMG, and EOG.

Updated Jul 1, 2026
Search Tags:#eda#biosignals#hrv#ecg#eeg#psychophysiology#physiological signals
One-click install
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill neurokit2-jasrajtulsi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: neurokit2
Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/neurokit2
Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill neurokit2-jasrajtulsi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

NeuroKit2 reduces the manual burden of cleaning, segmenting, and interpreting physiological recordings by turning raw biosignals into analysis-ready measures.

Core Features & Use Cases

  • Cardiovascular workflows for ECG and PPG, including heart rate, HRV, and respiratory sinus arrhythmia.
  • Brain and autonomic analysis for EEG microstates, EDA arousal, respiration, EMG activation, and EOG blink tracking.
  • Multi-signal research use cases such as psychophysiology experiments, event-related studies, clinical monitoring, and wearable data analysis.

Quick Start

Ask the neurokit2 skill to process the attached physiological recording, clean the signals, detect peaks or events, and return the key summary metrics for my study.

Frequently Asked Questions about neurokit2

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

FAQPage Schema
How do I process raw ECG and EDA signals for heart rate variability analysis?▼

To process raw ECG and EDA signals for heart rate variability analysis, apply signal-specific preprocessing, peak detection, and artifact correction to extract reliable HRV and arousal metrics from your physiological recordings.

What steps are needed to extract EEG microstates and EOG blink events from biosignals?▼

Extracting EEG microstates and EOG blink events requires setting proper sampling rates, cleaning raw physiological signals, and applying event-related analysis with artifact correction to produce analyzable biosignal features.

Can I use this skill for multimodal psychophysiology experiments and wearable data?▼

Yes, this skill supports multimodal psychophysiology experiments and wearable data analysis by processing cardiovascular, brain, and autonomic signals like ECG, EEG, EDA, RSP, PPG, EMG, and EOG into analysis-ready measures.

What is the best way to clean physiological signals and detect peaks for a research study?▼

The best way to clean physiological signals and detect peaks is to use structured workflows that handle signal-specific preprocessing, epoching, and artifact correction, turning raw biosignals into analysis-ready summary metrics.

Why does my physiological signal analysis require proper sampling rates and epoching?▼

Physiological signal analysis requires proper sampling rates and epoching because accurate frequency metrics, nonlinear measures, and peak detection depend on correctly segmented and cleaned data to yield reliable results.

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