Process and analyze physiological signals like ECG and EEG with Python.
npx skills add https://github.com/sanand0/scientific-research --skill neurokit2-sanand0
Please help me install this Agent Skill. Skill: neurokit2 Source: https://github.com/sanand0/scientific-research/tree/main/.claude/skills/neurokit2 Command: npx skills add https://github.com/sanand0/scientific-research --skill neurokit2-sanand0
This Skill streamlines the complex process of analyzing physiological signals, making advanced biosignal processing accessible for research and clinical applications.
Use the neurokit2 skill to process an ECG signal and compute its heart rate variability metrics.
High-intent search queries and answers about installing and using this skill.
To analyze ECG signals and calculate heart rate variability, you can process the raw physiological data to automatically extract HRV metrics. This allows researchers to quantify autonomic nervous system activity from multi-modal recordings.
Processing multi-modal physiological recordings involves analyzing simultaneous ECG, respiration, and EDA signals to extract respective metrics. This quantifies physiological reactions by isolating heart rate variability, respiratory patterns, and electrodermal responses.
Yes, EEG source localization is supported through integration with MNE-Python. The toolkit facilitates advanced EEG analytics including microstate identification and event-related responses to map neural dynamics.
Yes, biosignal processing includes simulation capabilities for testing and validation. Researchers can generate synthetic physiological signals to establish baselines and validate processing pipelines before applying them to empirical data.
Event-related response analysis supports ECG, EEG, EDA, RSP, EMG, EOG, and PPG physiological signals. This comprehensive coverage allows detailed examination of how different bodily systems react to specific stimuli.
No, advanced complexity measures and biosignal processing are accessible directly via a Python toolkit. This streamlines complex physiological signal analysis for research and clinical applications without requiring specialized hardware environments.
RamanEbrahimiAnalyze ECG, EEG, EDA, RSP, PPG, EMG and EOG signals with Python using NeuroKit2.
youyinnnProcess and analyze physiological signals like ECG, EEG, and EDA with Python.
pur3v4d3rProcess and analyze multichannel biosignals to extract HRV, EEG, EDA, and respiration metrics.
AlterLab-IEUPreprocesses and analyzes ECG, EEG, EDA, EMG, EOG, PPG and respiration data with NeuroKit2 in Python for cross-signal metrics like HRV and RSA.
