Habilidades neurokit2
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neurokit2

Seguro ⚙️ Comandos externos🌐 Acesso à rede

Processar biossinais com NeuroKit2

Também disponível em: davila7

Analisar sinais fisiológicos incluindo ECG, EEG, EDA e EMG para aplicações de pesquisa e clínicas. NeuroKit2 oferece um toolkit Python unificado para processar dados do sistema cardiovascular, neural e nervoso autônomo com ferramentas abrangentes de visualização e análise.

Suporta: Claude Codex Code(CC)
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A utilizar "neurokit2". Processar este sinal ECG para extrair métricas de VFC

Resultado esperado:

  • Picos R detectados em intervalos consistentes com frequência cardíaca média de 68 BPM
  • VFC no domínio temporal: SDNN=45ms, RMSSD=32ms, pNN20=18%
  • VFC no domínio de frequência: VLF=320ms², LF=410ms², HF=280ms², razão LF/HF=1,46
  • Avaliação de qualidade do sinal: Boa - contaminação mínima por artefatos

A utilizar "neurokit2". Analisar dados EEG para experimento cognitivo

Resultado esperado:

  • Potência alfa dominante em eletrodos posteriores (8-12 Hz)
  • Potência teta aumentada em regiões frontais durante execução da tarefa
  • Potencial relacionado a evento: componente N200 visível em 280ms pós-estimulação
  • Segmentação de microestados: 4 microestados dominantes identificados

Auditoria de Segurança

Seguro
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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⚙️ Comandos externos (1054)
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🌐 Acesso à rede (5)
skill-report.json:6 SKILL.md:279 SKILL.md:351 SKILL.md:352 SKILL.md:353
Auditado por: claude Ver Histórico de Auditoria →

Pontuação de qualidade

45
Arquitetura
100
Manutenibilidade
87
Conteúdo
20
Comunidade
100
Segurança
91
Conformidade com especificações

O Que Você Pode Construir

Pesquisa cognitiva com EEG

Analisar padrões de atividade cerebral para estudos cognitivos, examinando bandas de frequência e respostas relacionadas a eventos.

Avaliação de saúde cardíaca

Processar sinais ECG para extrair métricas de variabilidade da frequência cardíaca para avaliação do sistema nervoso autônomo.

Medição de resposta ao estresse

Combinar análise de EDA e ECG para avaliar a ativação simpática durante tarefas psicológicas.

Tente Estes Prompts

Análise básica de ECG 
Use NeuroKit2 para processar este sinal ECG. Detectar picos R, calcular frequência cardíaca e fornecer análise de VFC incluindo métricas de domínio temporal e frequência.
Biossinal multimodal 
Processar sinais ECG e EDA juntos. Limpar os sinais, extrair recursos relevantes e fornecer uma análise combinada de atividade cardíaca e eletrodérmica.
Análise espectral de EEG 
Analisar este dado EEG para potência nas bandas de frequência delta, teta, alfa, beta e gama. Relatar distribuição de potência relativa entre os canais.
Avaliação de complexidade 
Computar medidas de complexidade para este sinal fisiológico. Incluir entropia aproximada, entropia de amostra e análise de dimensão fractal (DFA).

Melhores Práticas

  • Sempre especificar o parâmetro correto sampling_rate para cálculos precisos no domínio temporal
  • Usar modo de análise relacionada a eventos para experimentos inferiores a 10 segundos, relacionada a intervalos para gravações mais longas
  • Aplicar avaliação de qualidade do sinal antes da análise para identificar e manipular artefatos

Evitar

  • Não analisar sinais sem conhecer a taxa de amostragem - os resultados serão sem sentido
  • Evitar misturar sinais com diferentes taxas de amostragem em bio_process() sem reamostragem
  • Não confiar apenas na detecção automatizada de picos sem inspeção visual dos resultados

Perguntas Frequentes

Quais sinais o NeuroKit2 suporta?
NeuroKit2 suporta sinais ECG, EEG, EDA, EMG, EOG, PPG e respiratórios para análise fisiológica abrangente.
Como instalo o NeuroKit2?
Use uv pip install neurokit2 ou pip install neurokit2. A biblioteca está disponível no PyPI.
Qual é a diferença entre análise relacionada a eventos e relacionada a intervalos?
A análise relacionada a eventos processa gravações curtas (<10s) em torno de eventos de estímulo. A análise relacionada a intervalos manipula gravações contínuas mais longas para caracterização de padrões.
Posso processar múltiplos sinais juntos?
Sim, bio_process() manipula ECG, RSP, EDA, EMG, PPG e EOG simultaneamente para análise integrada multimodal.
O NeuroKit2 manipula remoção de artefatos?
Limpeza básica de sinais está incluída para melhoria de qualidade. Artefatos graves podem requerer ferramentas de pré-processamento especializadas.
Quais capacidades de visualização estão disponíveis?
Funções de plotagem integradas para ECG, EEG, EDA e outros sinais incluem visualização de sinal, resultados de detecção de picos e displays de média de épocas.

Detalhes do Desenvolvedor

Licença

MIT license

Repositório

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

Referência

main

Estrutura de arquivos

📁 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