Does NeuroKit2 support multi-modal data integration?

Yes, the skill includes a bio-module specifically designed to process and analyze multiple physiological signals simultaneously with unified output.

Does it include nonlinear dynamics and complexity measures?

Yes, it features a robust complexity module for calculating various entropy measures, fractal dimensions, and other nonlinear dynamics indices.

Can this skill perform Heart Rate Variability (HRV) analysis?

Yes, it provides comprehensive HRV analysis across time, frequency, and nonlinear domains, including metrics like SDNN, RMSSD, and Poincaré plots.

Is it suitable for event-related analysis in experimental psychology?

Absolutely. It includes tools for finding events, creating epochs around stimuli, and performing event-related averaging for potentials and responses.

What types of physiological signals can NeuroKit2 handle?

NeuroKit2 supports a wide range of signals including ECG (Heart), EEG (Brain), EDA/GSR (Skin), RSP (Respiration), PPG (Pulse), EMG (Muscle), and EOG (Eyes).

NeuroKit2 Biosignal Analysis

Name: NeuroKit2 Biosignal Analysis
Author: Microck

byMicrock

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Data Science & ML

Processes and analyzes complex physiological signals including ECG, EEG, and EDA using a comprehensive Python toolkit.

NeuroKit2 is a sophisticated Python library integrated as a Claude Code Skill to facilitate the high-level processing and analysis of physiological signals (biosignals). It provides automated pipelines for cardiovascular signals (ECG, PPG), neural activity (EEG), autonomic responses (EDA/GSR), and respiratory patterns. This skill is indispensable for researchers and developers in psychophysiology, clinical diagnostics, and human-computer interaction who require robust, standardized tools for signal cleaning, feature extraction, heart rate variability (HRV) analysis, and multi-modal signal integration.

Key Features

01Advanced EEG analysis for frequency bands, microstates, and source localization

02Complete cardiovascular processing for ECG/PPG including R-peak detection and HRV indices

03EDA decomposition into tonic and phasic components for autonomic assessment

04Respiratory (RSP) and muscular (EMG) signal analysis with specialized feature extraction

0581 GitHub stars

06Nonlinear complexity measures including entropy, fractal dimensions, and Lempel-Ziv complexity

Use Cases

01Analyzing heart rate variability and autonomic nervous system activity in psychophysiological research

02Processing multi-modal physiological data for human-computer interaction (HCI) and affective computing

03Automating EEG frequency band analysis and microstate segmentation for neuroscience studies

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add microck/ordinary-claude-skills neurokit2

For use in Claude.ai and ChatGPT

Download Skill