Can I use this for event-related experimental designs?

Absolutely. The skill includes dedicated workflows for event-related analysis, allowing you to create epochs around stimulus markers and calculate grand averages.

Which physiological signals can NeuroKit2 process?

NeuroKit2 supports a wide range of signals including ECG (heart), EEG (brain), EDA/GSR (skin conductance), RSP (respiration), EMG (muscle), and EOG (eye movement).

Does NeuroKit2 handle signal cleaning and preprocessing?

Yes, it includes automated pipelines for filtering, artifact removal, peak detection, and quality assessment for various biosignal types.

Does this skill support Heart Rate Variability (HRV) analysis?

Yes, it provides comprehensive HRV metrics across time, frequency, and nonlinear domains, including indices like SDNN, RMSSD, and Poincaré plots.

NeuroKit2 Biosignal Analysis

Name: NeuroKit2 Biosignal Analysis
Author: Activer007

byActiver007

데이터 과학 및 ML

Processes and analyzes physiological biosignals including cardiac, neural, and autonomic data for psychophysiology and clinical research.

소개

NeuroKit2 provides an advanced Python-based toolkit for the end-to-end processing of physiological signals, enabling researchers and developers to extract meaningful features from ECG, EEG, EDA, and more. It automates complex workflows like R-peak detection, HRV calculation, and microstate analysis, making it an essential tool for psychophysiology research, human-computer interaction studies, and clinical data analysis where multi-modal signal integration and nonlinear complexity measures are required.

주요 기능

Nonlinear complexity analysis using entropy, fractals, and Lyapunov exponents
Autonomic assessment through Electrodermal Activity (EDA) and Respiration (RSP)
0 GitHub stars
Comprehensive cardiac analysis (ECG/PPG) including HRV and pulse detection
Automated event-related and interval-related physiological analysis pipelines
Advanced brain signal processing (EEG) for frequency bands and microstates

사용 사례

Extracting clinical features from EEG and EMG signals for medical research
Processing multi-modal sensor data for human-computer interaction applications
Analyzing heart rate variability and autonomic responses in psychophysiology studies

소개

주요 기능

Nonlinear complexity analysis using entropy, fractals, and Lyapunov exponents
Autonomic assessment through Electrodermal Activity (EDA) and Respiration (RSP)
0 GitHub stars
Comprehensive cardiac analysis (ECG/PPG) including HRV and pulse detection
Automated event-related and interval-related physiological analysis pipelines
Advanced brain signal processing (EEG) for frequency bands and microstates

사용 사례

Extracting clinical features from EEG and EMG signals for medical research
Processing multi-modal sensor data for human-computer interaction applications
Analyzing heart rate variability and autonomic responses in psychophysiology studies