Can I use this skill for Heart Rate Variability (HRV) analysis?

Yes, the skill provides functions for calculating HRV indices across time, frequency, and nonlinear domains from cardiac signals.

What complexity measures are available through this skill?

It supports a vast range of nonlinear measures including Shannon entropy, fractal dimensions (Katz, Higuchi), and detrended fluctuation analysis (DFA).

What types of physiological signals can NeuroKit2 handle?

NeuroKit2 supports a wide array of signals including ECG, EEG, EDA (GSR), PPG, RSP (respiration), EMG, and EOG.

Can NeuroKit2 process multiple types of signals at once?

Yes, the bio_module capability allows for the integration and synchronized processing of multiple physiological streams simultaneously.

Does it support event-related analysis for experimental trials?

Absolutely. It includes dedicated workflows for creating epochs around stimulus markers and analyzing event-related physiological responses.

NeuroKit2 Biosignal Analysis

Name: NeuroKit2 Biosignal Analysis
Author: Microck

byMicrock

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Data Science & ML

Processes and analyzes physiological signals like ECG, EEG, and EDA using the NeuroKit2 Python library.

About

This skill integrates the NeuroKit2 toolkit into Claude, providing a specialized framework for comprehensive biosignal processing and physiological data analysis. It empowers researchers and developers to handle cardiac, neural, autonomic, and muscular signals with ease, facilitating complex tasks such as heart rate variability (HRV) analysis, EEG microstate segmentation, and electrodermal activity (EDA) decomposition. Whether used for psychophysiology research, clinical applications, or human-computer interaction studies, this skill provides the patterns and functions needed to extract meaningful insights from multi-modal physiological data.

Key Features

Advanced nonlinear dynamics metrics including entropy, fractals, and Lyapunov exponents
Comprehensive cardiac processing for ECG/PPG and multi-domain HRV analysis
Autonomic assessment via skin conductance (EDA) and respiratory pattern (RSP) analysis
Unified multi-modal integration for processing multiple biosignals simultaneously
Neural signal analysis including EEG frequency bands, microstates, and complexity
81 GitHub stars

Use Cases

Analyzing psychophysiological datasets for research in stress, emotion, or cognition
Processing experimental event-related potentials and stimulus-locked physiological responses
Developing health-monitoring applications that require robust heart rate and breathing analysis

About

Key Features

Advanced nonlinear dynamics metrics including entropy, fractals, and Lyapunov exponents
Comprehensive cardiac processing for ECG/PPG and multi-domain HRV analysis
Autonomic assessment via skin conductance (EDA) and respiratory pattern (RSP) analysis
Unified multi-modal integration for processing multiple biosignals simultaneously
Neural signal analysis including EEG frequency bands, microstates, and complexity
81 GitHub stars

Use Cases

Analyzing psychophysiological datasets for research in stress, emotion, or cognition
Processing experimental event-related potentials and stimulus-locked physiological responses
Developing health-monitoring applications that require robust heart rate and breathing analysis