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

Yes, it provides comprehensive HRV metrics across time, frequency, and nonlinear domains, including specialized indices like RSA.

Is this skill suitable for clinical or research applications?

It is designed specifically for psychophysiology research and clinical data analysis using validated pipelines published in peer-reviewed literature.

What types of signals can NeuroKit2 handle?

It supports a wide range of biosignals including ECG (heart), EEG (brain), EDA/GSR (skin conductance), RSP (respiration), EMG (muscles), EOG (eyes), and PPG (pulse).

Does it support multi-modal data integration?

Yes, the skill includes specialized modules for the unified processing and analysis of multiple physiological signals simultaneously.

NeuroKit2 Biosignal Analysis

Name: NeuroKit2 Biosignal Analysis
Author: Microck

byMicrock

•

Data Science & ML

Processes and analyzes complex physiological signals including ECG, EEG, EDA, and respiratory patterns for psychophysiological research.

About

NeuroKit2 is a specialized Claude Code skill designed for researchers, clinicians, and developers working with human physiological data. It provides a comprehensive suite of tools for processing cardiovascular, neural, autonomic, and muscular signals, offering advanced capabilities for signal cleaning, peak detection, and feature extraction. Whether you are conducting heart rate variability (HRV) analysis, studying brain microstates via EEG, or analyzing skin conductance in psychophysiology experiments, this skill automates the implementation of validated processing pipelines and complex nonlinear dynamics to provide deep insights into the human body's activity.

Key Features

Nonlinear dynamics analysis including entropy measures and fractal dimensions
Advanced brain signal processing for EEG frequency bands, microstates, and complexity
81 GitHub stars
Comprehensive cardiac analysis including ECG, PPG, and multi-domain HRV metrics
Event-related analysis for stimulus-locked physiological responses
Autonomic assessment through Electrodermal Activity (EDA) and respiratory pattern analysis

Use Cases

Analyzing multi-modal physiological responses in human-computer interaction (HCI) studies
Conducting heart rate variability (HRV) studies for stress, health, or fitness monitoring
Processing EEG data to identify brain microstates and event-related potentials in research

About

Key Features

Nonlinear dynamics analysis including entropy measures and fractal dimensions
Advanced brain signal processing for EEG frequency bands, microstates, and complexity
81 GitHub stars
Comprehensive cardiac analysis including ECG, PPG, and multi-domain HRV metrics
Event-related analysis for stimulus-locked physiological responses
Autonomic assessment through Electrodermal Activity (EDA) and respiratory pattern analysis

Use Cases

Analyzing multi-modal physiological responses in human-computer interaction (HCI) studies
Conducting heart rate variability (HRV) studies for stress, health, or fitness monitoring
Processing EEG data to identify brain microstates and event-related potentials in research