Is NeuroKit2 compatible with other Python data libraries?

Yes, it is built on the Python scientific stack and is designed to integrate easily with NumPy, Pandas, and specialized tools like MNE for EEG analysis.

What types of physiological signals can this skill process?

NeuroKit2 supports a wide array of signals including ECG (heart), EEG (brain), EDA/GSR (skin conductance), RSP (respiration), PPG (pulse), EMG (muscle), and EOG (eye movements).

Can I use this for stimulus-response experiments?

Absolutely. The skill includes dedicated workflows for event-related analysis, allowing you to create epochs around specific triggers and analyze physiological reactions.

How does it handle long-term versus short-term data?

It automatically switches between Event-related analysis (for data under 10 seconds) and Interval-related analysis (for data over 10 seconds) to ensure the most appropriate metrics are calculated.

Does the skill support Heart Rate Variability (HRV) metrics?

Yes, it provides a comprehensive suite of HRV indices across time-domain, frequency-domain, and nonlinear-domain analysis.

NeuroKit2 Biosignal Analysis

Name: NeuroKit2 Biosignal Analysis
Author: Activer007

byActiver007

Data Science & ML

Analyzes and processes complex physiological signals like ECG, EEG, and EDA using the comprehensive NeuroKit2 Python library.

About

NeuroKit2 is a powerful Claude Code skill designed for researchers and developers working with physiological data. It provides a standardized interface for processing cardiac, neural, autonomic, respiratory, and muscular signals, offering advanced features like heart rate variability (HRV) analysis, EEG microstate segmentation, and electrodermal activity (EDA) decomposition. This skill simplifies the transition from raw biosignal data to meaningful psychophysiological insights by automating cleaning, peak detection, and metric calculation, making it an essential tool for clinical research, human-computer interaction studies, and health-tech development.

Key Features

Advanced complexity measures including entropy, fractal dimensions, and nonlinear dynamics
Comprehensive cardiac processing for ECG/PPG and multi-domain HRV analysis
Multi-modal integration for simultaneous processing of respiratory, muscular, and eye-tracking data
0 GitHub stars
Neural signal analysis including EEG frequency bands and microstate dynamics
Autonomic nervous system assessment via EDA/GSR tonic and phasic decomposition

Use Cases

Processing wearable sensor data for real-time stress and autonomic arousal monitoring
Automating the extraction of heart rate variability metrics for psychophysiological studies
Analyzing event-related potentials and physiological responses in experimental psychology paradigms

About

Key Features

Advanced complexity measures including entropy, fractal dimensions, and nonlinear dynamics
Comprehensive cardiac processing for ECG/PPG and multi-domain HRV analysis
Multi-modal integration for simultaneous processing of respiratory, muscular, and eye-tracking data
0 GitHub stars
Neural signal analysis including EEG frequency bands and microstate dynamics
Autonomic nervous system assessment via EDA/GSR tonic and phasic decomposition

Use Cases

Processing wearable sensor data for real-time stress and autonomic arousal monitoring
Automating the extraction of heart rate variability metrics for psychophysiological studies
Analyzing event-related potentials and physiological responses in experimental psychology paradigms