How does NeuroKit2 handle multi-modal data integration?

It features a dedicated bio_module that allows for the simultaneous processing and unified analysis of multiple signal types (e.g., combining ECG, RSP, and EDA) in a single workflow.

What types of physiological signals can NeuroKit2 handle?

NeuroKit2 is designed to process a wide variety of biosignals including ECG (heart), EEG (brain), EDA/GSR (skin conductance), RSP (respiration), PPG (pulse), EMG (muscle), and EOG (eye movement).

Does this skill support Heart Rate Variability (HRV) calculation?

Yes, it provides a comprehensive suite for HRV analysis across time-domain, frequency-domain, and nonlinear domains, including metrics like RMSSD, SDNN, and LF/HF ratios.

Can I use NeuroKit2 for event-related potential (ERP) analysis?

Absolutely. The skill includes tools for creating epochs around stimulus markers, baseline correction, and event-related averaging for various signal types.

NeuroKit2 Biosignal Processing

Name: NeuroKit2 Biosignal Processing
Author: Microck

byMicrock

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Ciencia de Datos y ML

Processes and analyzes complex physiological signals including ECG, EEG, EDA, and respiratory data for scientific research and clinical applications.

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NeuroKit2 is a comprehensive Python toolkit integrated as a Claude Skill to facilitate the advanced analysis of biosignals. It provides standardized, production-grade workflows for cleaning, processing, and extracting features from cardiovascular, neural, autonomic, and muscular signals. This skill is particularly valuable for researchers and developers working in psychophysiology, human-computer interaction, and medical data science, offering everything from heart rate variability (HRV) indices to EEG microstate segmentation and nonlinear complexity measures.

Características Principales

Autonomic nervous system assessment via EDA/GSR and respiratory pattern analysis
81 GitHub stars
Advanced EEG processing for frequency power bands, microstates, and source localization
Unified multi-modal integration for processing multiple physiological signals simultaneously
Nonlinear dynamics and complexity measures including entropy and fractal dimensions
Comprehensive cardiac analysis for ECG and PPG including R-peak detection and HRV metrics

Casos de Uso

Processing electrodermal activity (EDA) to detect sympathetic nervous system responses in psychophysiology
Analyzing heart rate variability (HRV) and respiratory sinus arrhythmia (RSA) for stress research
Extracting event-related potentials and microstate transitions from neural EEG data

Acerca de

Características Principales

Autonomic nervous system assessment via EDA/GSR and respiratory pattern analysis
81 GitHub stars
Advanced EEG processing for frequency power bands, microstates, and source localization
Unified multi-modal integration for processing multiple physiological signals simultaneously
Nonlinear dynamics and complexity measures including entropy and fractal dimensions
Comprehensive cardiac analysis for ECG and PPG including R-peak detection and HRV metrics

Casos de Uso

Processing electrodermal activity (EDA) to detect sympathetic nervous system responses in psychophysiology
Analyzing heart rate variability (HRV) and respiratory sinus arrhythmia (RSA) for stress research
Extracting event-related potentials and microstate transitions from neural EEG data