Is NeuroKit2 suitable for EEG microstate analysis?

Absolutely, it includes dedicated modules for microstate segmentation, transition dynamics, and global field power measures.

Does it handle event-related research paradigms?

Yes, it includes specialized functions for epoch creation, event finding, and stimulus-locked response analysis for experimental studies.

What signal types does NeuroKit2 support?

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

Can I use this for Heart Rate Variability (HRV) analysis?

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

NeuroKit2 Biosignal Processing

Name: NeuroKit2 Biosignal Processing
Author: Activer007

byActiver007

Data Science & ML

Processes and analyzes diverse physiological signals including heart rate, brain activity, and respiratory patterns for research and clinical applications.

About

NeuroKit2 is a comprehensive toolkit designed for the end-to-end analysis of biosignals such as ECG, EEG, and EDA. It provides researchers and developers with standardized pipelines for signal cleaning, feature extraction, and complex metrics like Heart Rate Variability (HRV) and event-related potentials. Whether conducting psychophysiological research or building human-computer interaction systems, this skill streamlines the processing of cardiovascular, neural, and autonomic data using robust, peer-reviewed methods and nonlinear complexity measures.

Key Features

Multi-modal signal processing for ECG, EEG, EDA, RSP, and EMG
EEG frequency band power and microstate segmentation
0 GitHub stars
Comprehensive Heart Rate Variability (HRV) analysis in all domains
Nonlinear dynamics and complexity measures including entropy and fractals
Automated event-related and interval-related analysis workflows

Use Cases

Analyzing autonomic nervous system responses in psychophysiology research
Developing human-computer interaction systems based on real-time physiological data
Processing neural signals for frequency analysis and microstate transitions

About

Key Features

Multi-modal signal processing for ECG, EEG, EDA, RSP, and EMG
EEG frequency band power and microstate segmentation
0 GitHub stars
Comprehensive Heart Rate Variability (HRV) analysis in all domains
Nonlinear dynamics and complexity measures including entropy and fractals
Automated event-related and interval-related analysis workflows

Use Cases

Analyzing autonomic nervous system responses in psychophysiology research
Developing human-computer interaction systems based on real-time physiological data
Processing neural signals for frequency analysis and microstate transitions