스킬 neurokit2
🧠
심전도(ECG), 뇌전도(EEG), 피부전도(EDA), 근전도(EMG)를 포함한 생리학적 신호를 연구 및 임상 응용을 위해 분석합니다. NeuroKit2는 심혈관, 신경, 자율신경계 데이터를 처리하기 위한 통합 Python 툴킷으로, 포괄적인 시각화 및 분석 도구를 제공합니다.
지원: Claude Codex Code(CC)
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스킬 ZIP 다운로드
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Claude에서 업로드
설정 → 기능 → 스킬 → 스킬 업로드로 이동
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토글을 켜고 사용 시작
테스트해 보기
"neurokit2" 사용 중입니다. 이 ECG 신호를 처리하여 HRV 지표 추출
예상 결과:
- 일정한 간격으로 탐지된 R-피크, 평균 심박수 68 BPM
- 시간 도메인 HRV: SDNN=45ms, RMSSD=32ms, pNN20=18%
- 주파수 도메인 HRV: VLF=320ms², LF=410ms², HF=280ms², LF/HF 비율=1.46
- 신호 품질 평가: 양호 - 최소한의 아티팩트 오염
"neurokit2" 사용 중입니다. 인지 실험을 위한 EEG 데이터 분석
예상 결과:
- 후두부 전극에서 지배적인 알파 파워 (8-12 Hz)
- 과제 수행 중 전두부 영역에서 증가된 세타 파워
- 사건 관련 전위: 자극 후 280ms에서 N200 성분 관찰
- 마이크로상태 분할: 4개의 지배적 마이크로상태 식별
보안 감사
안전v4 • 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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분석된 줄 수
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발견 사항
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총 감사 수
위험 요인
⚙️ 외부 명령어 (1054)
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🌐 네트워크 접근 (5)
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품질 점수
45
아키텍처
100
유지보수성
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콘텐츠
20
커뮤니티
100
보안
91
사양 준수
만들 수 있는 것
EEG 인지 연구
인지 연구를 위해 뇌 활동 패턴을 분석하고, 주파수 대역과 사건 관련 반응을 조사합니다.
심장 건강 평가
자율신경계 평가를 위해 심박변이도 지표를 추출하기 위해 ECG 신호를 처리합니다.
스트레스 반응 측정
심리학적 과제 동안의 교감신경 활성화를 평가하기 위해 EDA 및 ECG 분석을 결합합니다.
이 프롬프트를 사용해 보세요
기본 ECG 분석
NeuroKit2를 사용하여 이 ECG 신호를 처리합니다. R-피크를 탐지하고 심박수를 계산하며 시간 및 주파수 도메인 지표를 포함한 HRV 분석을 제공합니다.
다중모달 생체신호
ECG 및 EDA 신호를 함께 처리합니다. 신호를 정제하고 관련 특징을 추출하며 심장 활동과 피부전도 활동의 결합된 분석을 제공합니다.
EEG 스펙트럼 분석
이 EEG 데이터에서 델타, 세타, 알파, 베타, 감마 주파수 대역의 파워를 분석합니다. 채널 간 상대적 파워 분포를 보고합니다.
복잡도 평가
이 생리학적 신호에 대한 복잡도 측정값을 계산합니다. 근사 엔트로피, 표본 엔트로피, 프랙탈 차원(DFA) 분석을 포함합니다.
모범 사례
- 정확한 시간 도메인 계산을 위해 올바른 sampling_rate 매개변수를 항상 지정하세요
- 10초 미만의 실험에는 사건 관련 분석 모드를, 더 긴 녹음에는 구간 관련 분석을 사용하세요
- 분석 전에 신호 품질 평가를 적용하여 아티팩트를 식별하고 처리하세요
피하기
- 샘플링 레이트를 모르는 상태에서 신호를 분석하지 마세요 - 결과가 의미없게 됩니다
- 다중 샘플링 레이트의 신호를 리샘플링 없이 bio_process()에서 혼합하지 마세요
- 시각적 검사 없이 자동 피크 탐지만 의존하지 마세요
자주 묻는 질문
NeuroKit2는 어떤 신호를 지원합니까?
NeuroKit2는 포괄적인 생리학적 분석을 위해 ECG, EEG, EDA, EMG, EOG, PPG, 호흡 신호를 지원합니다.
NeuroKit2를 어떻게 설치합니까?
uv pip install neurokit2 또는 pip install neurokit2를 사용하세요. 이 라이브러리는 PyPI에서 이용 가능합니다.
사건 관련 분석과 구간 관련 분석의 차이점은 무엇입니까?
사건 관련 분석은 자극 사건 주변의 짧은 녹음(<10초)을 처리합니다. 구간 관련 분석은 패턴 특성을 위해 더 긴 연속 녹음을 처리합니다.
여러 신호를 함께 처리할 수 있습니까?
네, bio_process()는 ECG, RSP, EDA, EMG, PPG, EOG를 동시에 처리하여 통합된 다중모달 분석을 수행합니다.
NeuroKit2가 아티팩트 제거를 처리합니까?
품질 향상을 위한 기본적인 신호 정제가 포함되어 있습니다. 심각한 아티팩트에는 특수화된 전처리 도구가 필요할 수 있습니다.
어떤 시각화 기능이 제공됩니까?
ECG, EEG, EDA 및 기타 신호에 대한 내장 플로팅 함수는 신호 시각화, 피크 탐지 결과, 에포크 평균화 표시를 포함합니다.