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neurokit2

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Обработка биосигналов с помощью NeuroKit2

Также доступно от: davila7

Анализируйте физиологические сигналы, включая ЭКГ, ЭЭГ, ЭДА и ЭМГ, для исследовательских и клинических применений. NeuroKit2 предоставляет унифицированный инструментарий Python для обработки данных сердечно-сосудистой, нервной и вегетативной нервной системы с комплексными инструментами визуализации и анализа.

Поддерживает: Claude Codex Code(CC)
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Использование «neurokit2». Process this ECG signal to extract HRV metrics

Ожидаемый результат:

  • R-пики обнаружены с постоянными интервалами со средней частотой сердечных сокращений 68 уд/мин
  • ВСР во временной области: SDNN=45 мс, RMSSD=32 мс, pNN20=18%
  • ВСР в частотной области: ОНЧ=320 мс², НЧ=410 мс², ВЧ=280 мс², отношение НЧ/ВЧ=1.46
  • Оценка качества сигнала: Хорошее - минимальное загрязнение артефактами

Использование «neurokit2». Analyze EEG data for cognitive experiment

Ожидаемый результат:

  • Альфа-мощность доминирует в задних электродах (8-12 Гц)
  • Тета-мощность повышена в лобных областях во время выполнения задачи
  • Вызванный потенциал: компонент N200 виден через 280 мс после стимула
  • Сегментация микросостояний: идентифицировано 4 доминирующих микросостояния

Аудит безопасности

Безопасно
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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⚙️ Внешние команды (1054)
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🌐 Доступ к сети (5)
skill-report.json:6 SKILL.md:279 SKILL.md:351 SKILL.md:352 SKILL.md:353
Проверено: claude Посмотреть историю аудитов →

Оценка качества

45
Архитектура
100
Сопровождаемость
87
Контент
20
Сообщество
100
Безопасность
91
Соответствие спецификации

Что вы можете построить

Когнитивные исследования ЭЭГ

Анализируйте паттерны мозговой активности для когнитивных исследований, исследуя частотные диапазоны и вызванные ответы.

Оценка сердечного здоровья

Обрабатывайте сигналы ЭКГ для извлечения показателей вариабельности сердечного ритма для оценки вегетативной нервной системы.

Измерение стрессовой реакции

Комбинируйте анализ ЭДА и ЭКГ для оценки симпатической активации во время психологических задач.

Попробуйте эти промпты

Базовый анализ ЭКГ 
Используйте NeuroKit2 для обработки этого сигнала ЭКГ. Обнаружьте R-пики, рассчитайте частоту сердечных сокращений и предоставьте анализ ВСР, включая показатели во временной и частотной области.
Мультимодальный биосигнал 
Обрабатывайте сигналы ЭКГ и ЭДА вместе. Очистите сигналы, извлеките соответствующие признаки и предоставьте комбинированный анализ сердечной и электродермальной активности.
Спектральный анализ ЭЭГ 
Проанализируйте эти данные ЭЭГ на мощность в частотных диапазонах дельта, тета, альфа, бета и гамма. Сообщите относительное распределение мощности по каналам.
Оценка сложности 
Вычислите показатели сложности для этого физиологического сигнала. Включите приблизительную энтропию, выборочную энтропию и анализ фрактальной размерности (DFA).

Лучшие практики

  • Всегда указывайте правильный параметр sampling_rate для точных вычислений во временной области
  • Используйте режим анализа, связанного с событиями, для экспериментов менее 10 секунд, связанного с интервалами - для более длительных записей
  • Применяйте оценку качества сигнала перед анализом для выявления и обработки артефактов

Избегать

  • Не анализируйте сигналы без знания частоты дискретизации - результаты будут бессмысленными
  • Избегайте смешивания сигналов с разной частотой дискретизации в bio_process() без передискретизации
  • Не полагайтесь исключительно на автоматическое обнаружение пиков без визуальной проверки результатов

Часто задаваемые вопросы

What signals does NeuroKit2 support?
NeuroKit2 поддерживает сигналы ЭКГ, ЭЭГ, ЭДА, ЭМГ, ЭОГ, ФПГ и дыхания для комплексного физиологического анализа.
How do I install NeuroKit2?
Используйте uv pip install neurokit2 или pip install neurokit2. Библиотека доступна на PyPI.
What is the difference between event-related and interval-related analysis?
Анализ, связанный с событиями, обрабатывает короткие записи (<10 с) вокруг стимульных событий. Анализ, связанный с интервалами, обрабатывает более длительные непрерывные записи для характеристики паттернов.
Can I process multiple signals together?
Да, bio_process() одновременно обрабатывает ЭКГ, РСП, ЭДА, ЭМГ, ФПГ и ЭОГ для интегрированного мультимодального анализа.
Does NeuroKit2 handle artifact removal?
Базовая очистка сигналов включена для улучшения качества. Сильные артефакты могут потребовать специализированных инструментов предварительной обработки.
What visualization capabilities are available?
Встроенные функции построения графиков для ЭКГ, ЭЭГ, ЭДА и других сигналов включают визуализацию сигналов, результаты обнаружения пиков и отображения усреднения эпох.

Сведения для разработчиков

Автор

K-Dense-AI

Лицензия

MIT license

Репозиторий

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

Ссылка

main

Структура файлов

📁 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