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 β R-peak detection β delineation β 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: PoincarΓ© 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)
```
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