neurokit2

Multi-modal biosignal processing for ECG, EEG, EDA, RSP, PPG, EMG, and EOG is handled through unified pipelines that deliver cleaned data, extracted features, and synchronized reports. It applies standard scientific libraries to streamline multi-modal integration and reduce manual scripting overhead.

HRV metrics are extracted from ECG data using built-in event-related and interval-related analysis pipelines. The system cleans raw ECG signals and computes HRV features to support clinical and psychophysiology workflows directly within the Python toolkit.

Synchronizing and analyzing EDA and RSP data during stress tasks is achieved through multi-modal integration utilities that derive SCR and breathing metrics. The toolkit generates synchronized reports by applying quality-control utilities and robust error handling across the biosignals.

The biosignal processing toolkit requires standard scientific libraries and domain-specific packages to function properly. It is designed for research and clinical contexts, providing robust error handling and quality-control utilities to ensure platform-ready pipelines.

ERP analysis on EEG signals is fully supported through dedicated event-related analysis workflows. The toolkit processes raw EEG data to extract event-related features, delivering cleaned signals and quality-controlled outputs for research applications.