---
name: neurokit2
description: Process and analyze physiological signals including ECG, EEG, EDA, RSP, PPG, EMG, and EOG using Python.
license: MIT license
---

# 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:**
```python
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.

**Supported domains:**
- **Time domain**: SDNN, RMSSD, pNN50, SDSD, and derived metrics
- **Frequency domain**: ULF, VLF, LF, HF, VHF power and ratios
- **Nonlinear domain**: Poincaré plot (SD1/SD2), entropy measures, fractal dimensions
- **Specialized**: Respiratory sinus arrhythmia (RSA), recurrence quantification analysis (RQA)

**Key functions:**
```python
# All HRV indices at once
hrv_indices = nk.hrv(peaks, sampling_rate=1000)

# Domain-specific analysis
hrv_time = nk.hrv_time(peaks)
hrv_freq = nk.hrv_frequency(peaks, sampling_rate=1000)
hrv_nonlinear = nk.hrv_nonlinear(peaks, sampling_rate=1000)
hrv_rsa = nk.hrv_rsa(peaks, rsp_signal, sampling_rate=1000)
```

### 3. Brain Signal Analysis (EEG)

Analyze electroencephalography signals for frequency power, complexity, and microstate patterns. See `references/eeg.md` for detailed workflows and MNE integration.

**Primary capabilities:**
- Frequency band power analysis (Delta, Theta, Alpha, Beta, Gamma)
- Channel quality assessment and re-referencing
- Source localization (sLORETA, MNE)
- Microstate segmentation and transition dynamics
- Global field power and dissimilarity measures

**Key functions:**
```python
# Power analysis across frequency bands
power = nk.eeg_power(eeg_data, sampling_rate=250, channels=['Fz', 'Cz', 'Pz'])

# Microstate analysis
microstates = nk.microstates_segment(eeg_data, n_microstates=4, method='kmod')
static = nk.microstates_static(microstates)
dynamic = nk.microstates_dynamic(microstates)
```

### 4. Electrodermal Activity (EDA)

Process skin conductance signals for autonomic nervous system assessment. See `references/eda.md` for detailed workflows.

**Primary workflows:**
- Signal decomposition into tonic and phasic components
- Skin conductance response (SCR) detection and analysis
- Sympathetic nervous system index calculation
- Autocorrelation and changepoint detection

**Key functions:**
```python
# Complete EDA processing
signals, info = nk.eda_process(eda_signal, sampling_rate=100)

# Analyze EDA data
analysis = nk.eda_analyze(signals, sampling_rate=100)

# Sympathetic nervous system activity
sympathetic = nk.eda_sympathetic(signals, sampling_rate=100)
```

### 5. Respiratory Signal Processing (RSP)

Analyze breathing patterns and respiratory variability. See `references/rsp.md` for detailed workflows.

**Primary capabilities:**
- Respiratory rate calculation and variability analysis
- Breathing amplitude and symmetry assessment
- Respiratory volume per time (fMRI applications)
- Respiratory amplitude variability (RAV)

**Key functions:**
```python
# Complete RSP processing
signals, info = nk.rsp_process(rsp_signal, sampling_rate=100)

# Respiratory rate variability
rrv = nk.rsp_rrv(signals, sampling_rate=100)

# Respiratory volume per time
rvt = nk.rsp_rvt(signals, sampling_rate=100)
```

### 6. Electromyography (EMG)

Process muscle activity signals for activation detection and amplitude analysis. See `references/emg.md` for workflows.

**Key functions:**
```python
# Complete EMG processing
signals, info = nk.emg_process(emg_signal, sampling_rate=1000)

# Muscle activation detection
activation = nk.emg_activation(signals, sampling_rate=1000, method='threshold')
```

### 7. Electrooculography (EOG)

Analyze eye movement and blink patterns. See `references/eog.md` for workflows.

**Key functions:**
```python
# Complete EOG processing
signals, info = nk.eog_process(eog_signal, sampling_rate=500)

# Extract blink features
features = nk.eog_features(signals, sampling_rate=500)
```

### 8. General Signal Processing

Apply filtering, decomposition, and transformation operations to any signal. See `references/signal_processing.md` for comprehensive utilities.

**Key operations:**
- Filtering (lowpass, highpass, bandpass, bandstop)
- Decomposition (EMD, SSA, wavelet)
- Peak detection and correction
- Power spectral density estimation
- Signal interpolation and resampling
- Autocorrelation and synchrony analysis

**Key functions:**
```python
# Filtering
filtered = nk.signal_filter(signal, sampling_rate=1000, lowcut=0.5, highcut=40)

# Peak detection
peaks = nk.signal_findpeaks(signal)

# Power spectral density
psd = nk.signal_psd(signal, sampling_rate=1000)
```

### 9. Complexity and Entropy Analysis

Compute nonlinear dynamics, fractal dimensions, and information-theoretic measures. See `references/complexity.md` for all available metrics.

**Available measures:**
- **Entropy**: Shannon, approximate, sample, permutation, spectral, fuzzy, multiscale
- **Fractal dimensions**: Katz, Higuchi, Petrosian, Sevcik, correlation dimension
- **Nonlinear dynamics**: Lyapunov exponents, Lempel-Ziv complexity, recurrence quantification
- **DFA**: Detrended fluctuation analysis, multifractal DFA
- **Information theory**: Fisher information, mutual information

**Key functions:**
```python
# Multiple complexity metrics at once
complexity_indices = nk.complexity(signal, sampling_rate=1000)

# Specific measures
apen = nk.entropy_approximate(signal)
dfa = nk.fractal_dfa(signal)
lyap = nk.complexity_lyapunov(signal, sampling_rate=1000)
```

### 10. Event-Related Analysis

Create epochs around stimulus events and analyze physiological responses. See `references/epochs_events.md` for workflows.

**Primary capabilities:**
- Epoch creation from event markers
- Event-related averaging and visualization
- Baseline correction options
- Grand average computation with confidence intervals

**Key functions:**
```python
# Find events in signal
events = nk.events_find(trigger_signal, threshold=0.5)

# Create epochs around events
epochs = nk.epochs_create(signals, events, sampling_rate=1000,
                          epochs_start=-0.5, epochs_end=2.0)

# Average across epochs
grand_average = nk.epochs_average(epochs)
```

### 11. Multi-Signal Integration

Process multiple physiological signals simultaneously with unified output. See `references/bio_module.md` for integration workflows.

**Key functions:**
```python
# Process multiple signals at once
bio_signals, bio_info = nk.bio_process(
    ecg=ecg_signal,
    rsp=rsp_signal,
    eda=eda_signal,
    emg=emg_signal,
    sampling_rate=1000
)

# Analyze all processed signals
bio_analysis = nk.bio_analyze(bio_signals, sampling_rate=1000)
```

## Analysis Modes

NeuroKit2 automatically selects between two analysis modes based on data duration:

**Event-related analysis** (< 10 seconds):
- Analyzes stimulus-locked responses
- Epoch-based segmentation
- Suitable for experimental paradigms with discrete trials

**Interval-related analysis** (≥ 10 seconds):
- Characterizes physiological patterns over extended periods
- Resting state or continuous activities
- Suitable for baseline measurements and long-term monitoring

Most `*_analyze()` functions automatically choose the appropriate mode.

## Installation

```bash
uv pip install neurokit2
```

For development version:
```bash
uv pip install https://github.com/neuropsychology/NeuroKit/zipball/dev
```

## Common Workflows

### Quick Start: ECG Analysis
```python
import neurokit2 as nk

# Load example data
ecg = nk.ecg_simulate(duration=60, sampling_rate=1000)

# Process ECG
signals, info = nk.ecg_process(ecg, sampling_rate=1000)

# Analyze HRV
hrv = nk.hrv(info['ECG_R_Peaks'], sampling_rate=1000)

# Visualize
nk.ecg_plot(signals, info)
```

### Multi-Modal Analysis
```python
# Process multiple signals
bio_signals, bio_info = nk.bio_process(
    ecg=ecg_signal,
    rsp=rsp_signal,
    eda=eda_signal,
    sampling_rate=1000
)

# Analyze all signals
results = nk.bio_analyze(bio_signals, sampling_rate=1000)
```

### Event-Related Potential
```python
# Find events
events = nk.events_find(trigger_channel, threshold=0.5)

# Create epochs
epochs = nk.epochs_create(processed_signals, events,
                          sampling_rate=1000,
                          epochs_start=-0.5, epochs_end=2.0)

# Event-related analysis for each signal type
ecg_epochs = nk.ecg_eventrelated(epochs)
eda_epochs = nk.eda_eventrelated(epochs)
```

## References

This skill includes comprehensive reference documentation organized by signal type and analysis method:

- **ecg_cardiac.md**: ECG/PPG processing, R-peak detection, delineation, quality assessment
- **hrv.md**: Heart rate variability indices across all domains
- **eeg.md**: EEG analysis, frequency bands, microstates, source localization
- **eda.md**: Electrodermal activity processing and SCR analysis
- **rsp.md**: Respiratory signal processing and variability
- **ppg.md**: Photoplethysmography signal analysis
- **emg.md**: Electromyography processing and activation detection
- **eog.md**: Electrooculography and blink analysis
- **signal_processing.md**: General signal utilities and transformations
- **complexity.md**: Entropy, fractal, and nonlinear measures
- **epochs_events.md**: Event-related analysis and epoch creation
- **bio_module.md**: Multi-signal integration workflows

Load specific reference files as needed using the Read tool to access detailed function documentation and parameters.

## Additional Resources

- Official Documentation: https://neuropsychology.github.io/NeuroKit/
- GitHub Repository: https://github.com/neuropsychology/NeuroKit
- Publication: Makowski et al. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods. https://doi.org/10.3758/s13428-020-01516-y