Computes cleaned physiological biosignals and derives metrics for ECG, EEG, EDA, RSP, EMG, EOG, PPG analysis.
npx skills add https://github.com/CRAG666/dotfiles --skill neurokit2-crag666
Please help me install this Agent Skill. Skill: neurokit2 Source: https://github.com/CRAG666/dotfiles/tree/main/skills/neurokit2 Command: npx skills add https://github.com/CRAG666/dotfiles --skill neurokit2-crag666
NeuroKit2 streamlines the processing of physiological biosignals into clean signals and interpretable metrics, eliminating the repetitive, error-prone work of writing custom pipelines for ECG, EEG, EDA, RSP, EMG, and EOG analysis.
Use Case: You record synchronized ECG and respiration during an experiment and need heart rate variability plus cardiorespiratory coupling metrics; you can process both signals and extract RSA and HRV indices in a reproducible workflow.
Use the skill to analyze the uploaded biosignal file(s) by running NeuroKit2 processing to produce cleaned signals, detected peaks/events, and a summary table of computed metrics.
High-intent search queries and answers about installing and using this skill.
Heart rate variability metrics are computed by cleaning ECG signals, detecting R-peaks, and deriving time, frequency, and nonlinear domain indices. This process uses a consistent sampling-rate workflow to generate a reproducible summary table of HRV features.
Event-related analysis is performed by creating epochs around stimulus events for time-locked comparisons across multi-modal biosignals. You process synchronized signals like ECG and respiration to extract integrated metrics such as respiratory sinus arrhythmia.
Yes, EDA and EMG signals can both be cleaned and processed for feature extraction in the same workflow. The pipeline supports autonomic and muscular modalities, detecting key events and peaks to compute domain-specific metrics for each signal type.
Biosignal processing for ECG and EEG analysis requires a consistent sampling-rate workflow to accurately clean signals and extract features. Maintaining a uniform sampling rate ensures correct peak detection, event timing, and interval-related physiological analysis.
Cleaning physiological biosignals is necessary to remove noise and artifacts before extracting accurate domain-specific metrics. This step ensures that detected peaks and computed features for ECG, EEG, EDA, and EMG reliably represent the underlying physiological activity.
jasrajtulsiProcess raw physiological signals into cleaned biosignal features for ECG, EEG, EDA, RSP, PPG, EMG, and EOG.
Estrella-231Extract, clean, and analyze physiological biosignals into cardiovascular and neural features.
Zeyuyang-0420Process and analyze physiological signals including ECG, EEG, EDA, RSP, PPG, EMG, and EOG.
dralkhProcess physiological biosignals into cleaned metrics for ECG, EEG, EDA, and respiration.
