scientific-biosignal-processing

To compute HRV and EEG band powers from biosignal data, this pipeline detects R-peaks and calculates multi-channel spectrograms using Python. It processes raw signals to generate time-domain, frequency-domain, and visual feature outputs.

ERP analysis processes EEG signals to measure brain responses to stimuli. This Skill extracts ERPs alongside multi-channel band powers and spectrograms, enabling cognitive experiments and sleep studies through Python-based preprocessing.

Yes, scipy and numpy are required for signal processing, alongside matplotlib for visualization. These Python packages enable the preprocessing, feature extraction, and plotting pipelines for ECG, EEG, and EMG data analysis.

Yes, a single biosignal pipeline can detect EMG bursts and respiratory RSA. This Skill processes EMG envelope estimation and respiration metrics, enabling gait experiments and cross-disciplinary research through reproducible Python workflows.

Analyzing wearable biosignal data for clinical-like research is best handled by a reproducible pipeline for preprocessing and feature extraction. This Skill supports wearable-data pipelines for cardiac studies, sleep analysis, and cognitive experiments.

Manual biosignal analysis is slow due to isolated preprocessing and feature extraction steps. You can automate it using this pipeline, which reduces manual analysis time by enabling reproducible workflows for ECG, EEG, and EMG visualization.