Does it support multi-modal data?

Absolutely. The skill includes unified workflows for integrating and analyzing multiple physiological signals simultaneously using the bio_process module.

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

Yes, it computes comprehensive HRV metrics across time, frequency, and nonlinear domains, including SDNN, RMSSD, LF/HF ratios, and Poincaré plots.

How does it handle different data durations?

NeuroKit2 automatically switches between event-related analysis (for segments under 10 seconds) and interval-related analysis (for segments 10 seconds or longer).

Is it suitable for event-related research?

Yes, it can detect event markers, create epochs around stimulus events, and perform event-related averaging for trial-based experimental paradigms.

What types of physiological signals can NeuroKit2 handle?

It supports a wide range of signals including ECG (heart), EEG (brain), EDA/GSR (skin conductance), RSP (respiration), PPG (pulse), EMG (muscle), and EOG (eye movements).

NeuroKit2 Biosignal Analysis

Name: NeuroKit2 Biosignal Analysis
Author: jackspace

byjackspace

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Data Science & ML

Processes and analyzes complex physiological signals including ECG, EEG, and EDA for psychophysiology research and medical data science.

About

NeuroKit2 is a comprehensive Python toolkit designed for the advanced processing and analysis of physiological data (biosignals). This skill enables Claude to handle intricate workflows for cardiovascular, neural, autonomic, respiratory, and muscular signals, offering specialized capabilities for heart rate variability (HRV) analysis, EEG frequency bands, and nonlinear complexity measures. It is an essential tool for researchers and developers in psychophysiology, human-computer interaction, and clinical applications who need to automate signal cleaning, peak detection, and multi-modal integration.

Key Features

Autonomic nervous system assessment via EDA and skin conductance
Advanced neural signal processing including EEG microstates and power bands
Nonlinear dynamics and complexity measures like entropy and fractal dimensions
Comprehensive cardiovascular analysis (ECG, PPG, and HRV)
2 GitHub stars
Multi-modal integration for processing various signals simultaneously

Use Cases

Extracting event-related potentials and frequency domain features from brain activity
Processing multi-modal physiological data for human-computer interaction research
Analyzing heart rate variability and autonomic responses in psychophysiology studies

About

Key Features

Autonomic nervous system assessment via EDA and skin conductance
Advanced neural signal processing including EEG microstates and power bands
Nonlinear dynamics and complexity measures like entropy and fractal dimensions
Comprehensive cardiovascular analysis (ECG, PPG, and HRV)
2 GitHub stars
Multi-modal integration for processing various signals simultaneously

Use Cases

Extracting event-related potentials and frequency domain features from brain activity
Processing multi-modal physiological data for human-computer interaction research
Analyzing heart rate variability and autonomic responses in psychophysiology studies