neurokit2

To compute HRV metrics from raw ECG signals, you can use processing functions like ecg_process to clean the data and detect peaks, then extract time, frequency, and nonlinear metrics. This handles the entire cardiovascular workflow without requiring bespoke pipelines.

Yes, you can measure respiratory sinus arrhythmia by coordinating synchronized ECG and respiration signals. The tool computes cross-signal integration features like RSA to assess autonomic nervous system activity directly from the coupled physiological data.

Yes, this biosignal processing tool supports EEG microstates and band power analysis. It extracts neurophysiology features from raw EEG time series, allowing you to compute neural metrics for event-related or interval-related analysis pipelines.

The best way to process EDA signals for skin conductance response analysis is using a dedicated eda_process function. This cleans the raw electrodermal activity time series and extracts autonomic metrics like SCR peaks to quantify physiological arousal.

Yes, you need raw physiological recordings in time series format to extract event-related biosignal features. The tool requires raw ECG, EEG, EDA, or respiratory signals to clean data, detect epochs, and compute interval-related or event-specific metrics.