How do I install the required Python library?

You can install the necessary library by running 'uv pip install neurokit2' in your terminal environment.

What signals can NeuroKit2 process?

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

How does this skill help with HRV analysis?

It provides comprehensive functions for time-domain, frequency-domain, and nonlinear heart rate variability metrics using standardized scientific methods.

Does it support EEG microstate analysis?

Yes, the skill includes capabilities for EEG microstate segmentation, as well as static and dynamic analysis of brain state transitions.

Can I use this for event-related experimental data?

Yes, the skill includes specific capabilities for finding events, creating epochs, and performing stimulus-locked averaging.

NeuroKit2 Biosignal Analysis

Name: NeuroKit2 Biosignal Analysis
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Processes and analyzes complex physiological signals including ECG, EEG, EDA, and more using the NeuroKit2 Python library.

About

NeuroKit2 is a powerful Python toolkit designed for the comprehensive processing and analysis of biosignals such as cardiovascular, neural, and autonomic data. This skill enables Claude to guide researchers and developers through complex physiological signal workflows, from cleaning and R-peak detection to heart rate variability (HRV) analysis, EEG microstate segmentation, and event-related potential extraction. It is an essential tool for psychophysiology research, clinical data analysis, and human-computer interaction studies requiring robust, peer-reviewed signal processing methods.

Key Features

Comprehensive cardiac analysis including ECG, PPG, and HRV metrics
3 GitHub stars
Advanced brain signal processing for EEG power bands and microstates
Nonlinear complexity analysis using entropy and fractal dimensions
Autonomic nervous system assessment via EDA and respiratory patterns
Automated event-related and interval-related analysis modes

Use Cases

Extracting skin conductance responses (EDA) for emotion and stress monitoring
Processing EEG data for cognitive neuroscience and brain-computer interface studies
Analyzing heart rate variability and respiratory patterns in psychophysiology research

About

Key Features

Comprehensive cardiac analysis including ECG, PPG, and HRV metrics
3 GitHub stars
Advanced brain signal processing for EEG power bands and microstates
Nonlinear complexity analysis using entropy and fractal dimensions
Autonomic nervous system assessment via EDA and respiratory patterns
Automated event-related and interval-related analysis modes

Use Cases

Extracting skin conductance responses (EDA) for emotion and stress monitoring
Processing EEG data for cognitive neuroscience and brain-computer interface studies
Analyzing heart rate variability and respiratory patterns in psychophysiology research