neurokit2

To extract heart rate variability metrics, you can run the ECG or PPG processing functions to compute HRV features from raw signals. The toolkit processes raw biosignals and outputs structured feature data containing HRV metrics ready for statistical analysis.

Yes, you can analyze multiple biosignals together to compute cross-modal metrics like respiratory sinus arrhythmia and cardiorespiratory coupling. The toolkit supports multi-modal integration of EEG, EDA, RSP, EMG, and EOG signals for combined physiological assessment.

Yes, you need Python with NumPy and SciPy installed to run the NeuroKit2 package for biosignal processing. These dependencies provide the numerical and scientific computing foundation required to execute the signal processing pipelines and output structured feature data.

The best way to separate phasic and tonic components for stress assessment is to use the built-in EDA processing function. It decomposes the raw electrodermal activity signal into its phasic and tonic components, yielding structured metrics applicable to psychophysiology research.

You can run event-related analysis by feeding your biosignal data and event markers into the interval and event-related analysis pipelines. This processes ECG, EEG, or EOG signals around specific triggers to extract physiological metrics for neurophysiological experiments.

You can load standard biosignal data formats for sleep studies directly into the processing pipelines. The toolkit processes respiration, ECG, and EEG signals to extract sleep-related metrics, outputting structured feature data compatible with downstream statistical workflows.