neurokit2

NeuroKit2

Overview

NeuroKit2 is a comprehensive Python toolkit for processing and analyzing physiological signals (biosignals). Use this skill to process cardiovascular, neural, autonomic, respiratory, and muscular signals for psychophysiology research, clinical applications, and human-computer interaction studies.

When to Use This Skill

Apply this skill when working with:

• Cardiac signals: ECG, PPG, heart rate variability (HRV), pulse analysis
• Brain signals: EEG frequency bands, microstates, complexity, source localization
• Autonomic signals: Electrodermal activity (EDA/GSR), skin conductance responses (SCR)
• Respiratory signals: Breathing rate, respiratory variability (RRV), volume per time
• Muscular signals: EMG amplitude, muscle activation detection
• Eye tracking: EOG, blink detection and analysis
• Multi-modal integration: Processing multiple physiological signals simultaneously
• Complexity analysis: Entropy measures, fractal dimensions, nonlinear dynamics

Core Capabilities

1. Cardiac Signal Processing (ECG/PPG)

Process electrocardiogram and photoplethysmography signals for cardiovascular analysis. See references/ecg_cardiac.md for detailed workflows.

Primary workflows:

• ECG processing pipeline: cleaning → R-peak detection → delineation → quality assessment
• HRV analysis across time, frequency, and nonlinear domains
• PPG pulse analysis and quality assessment
• ECG-derived respiration extraction

Key functions:

import neurokit2 as nk

# Complete ECG processing pipeline
signals, info = nk.ecg_process(ecg_signal, sampling_rate=1000)

# Analyze ECG data (event-related or interval-related)
analysis = nk.ecg_analyze(signals, sampling_rate=1000)

# Comprehensive HRV analysis
hrv = nk.hrv(peaks, sampling_rate=1000)  # Time, frequency, nonlinear domains

2. Heart Rate Variability Analysis

Compute comprehensive HRV metrics from cardiac signals. See references/hrv.md for all indices and domain-specific analysis.