Does it handle multi-modal data?

Absolutely. The skill includes workflows for the 'bio' module which processes and analyzes multiple different physiological signals simultaneously with unified output.

Can I perform Heart Rate Variability (HRV) analysis?

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

What types of physiological signals does this skill support?

This skill supports a wide range of biosignals including ECG (heart), EEG (brain), EDA (skin conductance), RSP (respiration), PPG (pulse), EMG (muscle), and EOG (eye movements).

What kind of complexity analysis can I perform?

The skill can calculate various entropy measures (Shannon, Sample, Fuzzy), fractal dimensions, and nonlinear dynamics like Lyapunov exponents.

NeuroKit2 Biosignal Processing

Name: NeuroKit2 Biosignal Processing
Author: pur3v4d3r

bypur3v4d3r

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데이터 과학 및 ML

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

소개

NeuroKit2 is a powerful tool for processing complex physiological signals (biosignals) within your coding environment. This skill enables developers and researchers to perform advanced analysis on cardiovascular, neural, autonomic, and muscular data, providing specialized workflows for heart rate variability (HRV), EEG microstate segmentation, and sympathetic nervous system assessment. Whether you are working on psychophysiology research, clinical applications, or human-computer interaction studies, this skill streamlines the processing of multi-modal physiological data into actionable insights.

주요 기능

1 GitHub stars
Neural signal processing for EEG frequency bands and microstate patterns
Unified multi-modal integration for processing multiple signals simultaneously
Advanced complexity and entropy measures for nonlinear dynamics
Autonomic activity assessment through EDA and skin conductance analysis
Comprehensive cardiac analysis for ECG and PPG including HRV metrics

사용 사례

Processing EEG data for neuroscientific studies and brain-state classification
Analyzing heart rate variability and respiratory sinus arrhythmia for stress research
Quantifying autonomic responses via skin conductance and electromyography

소개

주요 기능

1 GitHub stars
Neural signal processing for EEG frequency bands and microstate patterns
Unified multi-modal integration for processing multiple signals simultaneously
Advanced complexity and entropy measures for nonlinear dynamics
Autonomic activity assessment through EDA and skin conductance analysis
Comprehensive cardiac analysis for ECG and PPG including HRV metrics

사용 사례

Processing EEG data for neuroscientific studies and brain-state classification
Analyzing heart rate variability and respiratory sinus arrhythmia for stress research
Quantifying autonomic responses via skin conductance and electromyography