neurokit2

To calculate heart rate variability, you can process ECG and EDA signals by applying automated cleaning, peak detection, and feature extraction algorithms. This provides simultaneous autonomic arousal metrics like skin conductance responses and HRV.

Analyzing EEG microstates and source localization for psychophysiology research involves using a comprehensive biosignal processing toolkit to decompose complex physiological data and identify neural dynamic patterns.

Yes, you can use this Python-based toolkit for advanced biosignal processing of EMG and PPG data, enabling robust signal cleaning and feature extraction for human-computer interaction and clinical applications.

Yes, this physiological signal analysis toolkit supports RSP and EOG signals. It provides a unified environment for simultaneous processing and analysis across multiple biosignal modalities.

To extract complexity measures from physiological signals, you apply specialized decomposition and feature extraction algorithms to the raw data, yielding quantitative metrics for clinical and psychophysiology applications.

Automated peak detection in ECG analysis requires raw signal data and a Python environment. The toolkit handles the underlying signal cleaning and decomposition needed to accurately identify physiological peaks.