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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Ciencia de Datos y ML

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

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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.

Características Principales

Advanced EEG analysis for frequency bands, microstates, and source localization
Complete cardiovascular processing for ECG/PPG including R-peak detection and HRV indices
EDA decomposition into tonic and phasic components for autonomic assessment
Respiratory (RSP) and muscular (EMG) signal analysis with specialized feature extraction
81 GitHub stars
Nonlinear complexity measures including entropy, fractal dimensions, and Lempel-Ziv complexity

Casos de Uso

Analyzing heart rate variability and autonomic nervous system activity in psychophysiological research
Processing multi-modal physiological data for human-computer interaction (HCI) and affective computing
Automating EEG frequency band analysis and microstate segmentation for neuroscience studies

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Características Principales

Advanced EEG analysis for frequency bands, microstates, and source localization
Complete cardiovascular processing for ECG/PPG including R-peak detection and HRV indices
EDA decomposition into tonic and phasic components for autonomic assessment
Respiratory (RSP) and muscular (EMG) signal analysis with specialized feature extraction
81 GitHub stars
Nonlinear complexity measures including entropy, fractal dimensions, and Lempel-Ziv complexity

Casos de Uso

Analyzing heart rate variability and autonomic nervous system activity in psychophysiological research
Processing multi-modal physiological data for human-computer interaction (HCI) and affective computing
Automating EEG frequency band analysis and microstate segmentation for neuroscience studies