Neurokit2
Comprehensive biosignal processing toolkit for analyzing physiological data including...
Install
npx skills add synthetic-sciences/openscience --skill neurokit2 -g -a claude-code -y- Repository
- synthetic-sciences/openscience4.3k
- Category
- Writing
- Updated
- 5d ago
- License
- MIT license
- Context cost
- Mediumabout 2,854 tokens
- Runs scripts
- No, instructions only
What it does
Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.
NeuroKit2
Overview
NeuroKit2 is a comprehensive Python toolkit for processing and analyzing physiological signals (biosignals). Use this skill to process cardiovascular, neural, autonomic, respiratory, and muscular signals for psychophysiology research, clinical applications, and human-computer interaction studies.
When to Use This Skill
Apply this skill when working with:
- Cardiac signals: ECG, PPG, heart rate variability (HRV), pulse analysis
- Brain signals: EEG frequency bands, microstates, complexity, source localization
- Autonomic signals: Electrodermal activity (EDA/GSR), skin conductance responses (SCR)
- Respiratory signals: Breathing rate, respiratory variability (RRV), volume per time
- Muscular signals: EMG amplitude, muscle activation detection
- Eye tracking: EOG, blink detection and analysis
- Multi-modal integration: Processing multiple physiological signals simultaneously
- Complexity analysis: Entropy measures, fractal dimensions, nonlinear dynamics
Core Capabilities
1. Cardiac Signal Processing (ECG/PPG)
Process electrocardiogram and photoplethysmography signals for cardiovascular analysis. See references/ecg_cardiac.md for detailed workflows.
Primary workflows:
- ECG processing pipeline: cleaning → R-peak detection → delineation → quality assessment
- HRV analysis across time, frequency, and nonlinear domains
- PPG pulse analysis and quality assessment
- ECG-derived respiration extraction
Key functions:
import neurokit2 as nk
# Complete ECG processing pipeline
signals, info = nk.ecg_process(ecg_signal, sampling_rate=1000)
# Analyze ECG data (event-related or interval-related)
analysis = nk.ecg_analyze(signals, sampling_rate=1000)
# Comprehensive HRV analysis
hrv = nk.hrv(peaks, sampling_rate=1000) # Time, frequency, nonlinear domains
