Fähigkeiten neurokit2
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neurokit2

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Biosignale mit NeuroKit2 verarbeiten

Auch verfügbar von: davila7

Analysieren Sie physiologische Signale einschließlich ECG, EEG, EDA und EMG für Forschungs- und klinische Anwendungen. NeuroKit2 bietet ein einheitliches Python-Toolkit zur Verarbeitung von kardiovaskulären, neuronalen und autonomen Nervensystemdaten mit umfassenden Visualisierungs- und Analysetools.

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Verwendung von "neurokit2". Verarbeiten Sie dieses ECG-Signal, um HRV-Metriken zu extrahieren

Erwartetes Ergebnis:

  • R-Peaks in konsistenten Intervallen mit einer durchschnittlichen Herzfrequenz von 68 BPM erkannt
  • Zeitbereichs-HRV: SDNN=45ms, RMSSD=32ms, pNN20=18%
  • Frequenzbereichs-HRV: VLF=320ms², LF=410ms², HF=280ms², LF/HF-Verhältnis=1,46
  • Signalqualitätsbewertung: Gut – minimale Artefaktkontamination

Verwendung von "neurokit2". Analysieren Sie EEG-Daten für kognitive Experimente

Erwartetes Ergebnis:

  • Alpha-Leistung dominant in posterioren Elektroden (8-12 Hz)
  • Theta-Leistung während der Aufgabendurchführung in frontalen Regionen erhöht
  • Ereigniskorreliertes Potential: N200-Komponente bei 280ms nach Stimulus sichtbar
  • Mikrozustandssegmentierung: 4 dominante Mikrozustände identifiziert

Sicherheitsaudit

Sicher
v4 • 1/17/2026

This skill contains documentation for the NeuroKit2 Python library including code examples and API references. All 1152 static findings are false positives triggered by markdown documentation patterns: Python code blocks flagged as shell commands, academic DOIs flagged as cryptographic algorithms, and scientific terminology flagged as C2 indicators. No actual security risks exist - this is legitimate scientific documentation.

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⚙️ Externe Befehle (1054)
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Qualitätsbewertung

45
Architektur
100
Wartbarkeit
87
Inhalt
20
Community
100
Sicherheit
91
Spezifikationskonformität

Was du bauen kannst

EEG-Kognitionsforschung

Analysieren Sie Hirnaktivitätsmuster für kognitive Studien, untersuchen Sie Frequenzbänder und ereigniskorrelierte Reaktionen.

Herzgesundheitsbewertung

Verarbeiten Sie ECG-Signale, um Herzfrequenzvariabilitätsmetriken für die Bewertung des autonomen Nervensystems zu extrahieren.

Stressreaktionsmessung

Kombinieren Sie EDA- und ECG-Analyse, um die sympathische Aktivierung während psychologischer Aufgaben zu bewerten.

Probiere diese Prompts

Grundlegende ECG-Analyse 
Verarbeiten Sie dieses ECG-Signal mit NeuroKit2. Erkennen Sie R-Peaks, berechnen Sie die Herzfrequenz und liefern Sie eine HRV-Analyse einschließlich Zeit- und Frequenzbereichsmetriken.
Multimodales Biosignal 
Verarbeiten Sie sowohl ECG- als auch EDA-Signale gemeinsam. Bereinigen Sie die Signale, extrahieren Sie relevante Merkmale und liefern Sie eine kombinierte Analyse der kardialen und elektrodermalen Aktivität.
EEG-Spektralanalyse 
Analysieren Sie diese EEG-Daten auf Leistung in Delta-, Theta-, Alpha-, Beta- und Gamma-Frequenzbändern. Berichten Sie die relative Leistungsverteilung über die Kanäle.
Komplexitätsbewertung 
Berechnen Sie Komplexitätsmaße für dieses physiologische Signal. Fügen Sie approximative Entropie, Stichprobenentropie und Fraktaldimensionen (DFA) ein.

Bewährte Verfahren

  • Geben Sie immer den korrekten sampling_rate-Parameter für genaue Zeitbereichsberechnungen an
  • Verwenden Sie den ereignisbezogenen Analysemodus für Experimente unter 10 Sekunden, den intervallbezogenen für längere Aufnahmen
  • Wenden Sie eine Signalqualitätsbewertung vor der Analyse an, um Artefakte zu identifizieren und zu behandeln

Vermeiden

  • Analysieren Sie keine Signale ohne Kenntnis der Abtastrate – die Ergebnisse werden bedeutungslos sein
  • Vermeiden Sie es, Signale mit unterschiedlichen Abtastraten in bio_process() ohne Resampling zu mischen
  • Verlassen Sie sich nicht ausschließlich auf automatisierte Peak-Erkennung ohne visuelle Inspektion der Ergebnisse

Häufig gestellte Fragen

Welche Signale unterstützt NeuroKit2?
NeuroKit2 unterstützt ECG, EEG, EDA, EMG, EOG, PPG und Atemsignale für umfassende physiologische Analysen.
Wie installiere ich NeuroKit2?
Verwenden Sie uv pip install neurokit2 oder pip install neurokit2. Die Bibliothek ist auf PyPI verfügbar.
Was ist der Unterschied zwischen ereignisbezogener und intervallbezogener Analyse?
Die ereignisbezogene Analyse verarbeitet kurze Aufnahmen (<10s) um Stimulus-Ereignisse. Die intervallbezogene Analyse behandelt längere kontinuierliche Aufnahmen zur Mustercharakterisierung.
Kann ich mehrere Signale gemeinsam verarbeiten?
Ja, bio_process() handhabt ECG, RSP, EDA, EMG, PPG und EOG gleichzeitig für integrierte multimodale Analyse.
Behandelt NeuroKit2 Artefaktentfernung?
Grundlegende Signalbereinigung ist für die Qualitätsverbesserung enthalten. Schwerwiegende Artefakte erfordern möglicherweise spezialisierte Vorverarbeitungstools.
Welche Visualisierungsmöglichkeiten sind verfügbar?
Integrierte Plotting-Funktionen für ECG, EEG, EDA und andere Signale umfassen Signalvisualisierung, Peak-Erkennungsergebnisse und Epochendurchschnittsanzeigen.

Entwicklerdetails

Lizenz

MIT license

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/neurokit2

Ref

main

Dateistruktur

📁 references/

📄 bio_module.md

📄 complexity.md

📄 ecg_cardiac.md

📄 eda.md

📄 eeg.md

📄 emg.md

📄 eog.md

📄 epochs_events.md

📄 hrv.md

📄 ppg.md

📄 rsp.md

📄 signal_processing.md

📄 SKILL.md