neurokit2

To compute HRV and RSA from ECG and respiratory signals, you need a multi-modal integration pipeline that detects ECG R-peaks and respiratory phases to derive cross-signal metrics like respiratory sinus arrhythmia.

Extracting tonic-phasic components and SCRs from EDA data requires a signal processing pipeline that cleans the raw electrodermal activity and applies event detection to separate slow-changing tonic baseline from rapid phasic skin conductance responses.

Computing EEG microstates and nonlinear complexity indices in Python involves applying entropy and fractal dimension algorithms to cleaned neural signals to characterize brain state dynamics and signal complexity.

Accurate ECG R-peak and EOG blink detection requires appropriate sampling rates configured within the signal processing pipeline to ensure temporal resolution is sufficient for precise event and peak identification.

Multi-modal integration for cardiovascular and neural features works by running unified processing across synchronized biosignal streams to compute interpretable cross-signal physiological metrics such as RSA from combined ECG and respiration data.

Computing respiratory peak and phase metrics from experimental recordings is limited by signal noise, artifact presence, and the need for precise event detection, requiring cleaned signals and appropriate sampling rates to yield valid epoch-related results.