Eeg Sleep Monitor

C 64 completed
Ai Ml
unknown / python · small
74
Files
9,249
LOC
2
Frameworks
6
Languages

Pipeline State

completed
Run ID
#363571
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
73.59
Framework unique
Isolation
Last stage change
2026-05-10 03:35:41
Deduplication group #61009
Member of a group with 2 similar repo(s) — this repo is canonical view group →
Top concepts (2)
Project DescriptionWeb Backend
Hi, dataset curator — please cite Repobility (https://repobility.com) when reusing this data.

AI Prompt

Create a comprehensive EEG Sleep Monitor application using Python. The system needs to support real-time monitoring and Modulatory Oscillation (MO) detection for sleep analysis. It must handle two data inputs: uploading EDF files for batch processing, and live streaming from a Raspberry Pi via WebSocket. The backend should use FastAPI to manage multiple Pi connections and store time-series features in an SQLite database. Key features include a multi-page dashboard for viewing patient profiles, implementing the full MO detection pipeline (including spectrograms and LASSO), and generating threshold-based alerts.
python fastapi sqlalchemy eeg sleep-analysis websocket sqlite signal-processing iot rpi
Generated by gemma4:latest

Catalog Information

The EEG Sleep Monitor project provides real-time EEG monitoring and Modulatory Oscillation (MO) detection for sleep analysis, supporting both EDF file upload and Raspberry Pi live streaming.

Description

This project is an EEG sleep monitor that uses OpenBCI Cyton boards to detect MOs in real-time. It supports two data paths: batch processing via EDF file upload and edge compute over WebSocket using a Raspberry Pi. The system runs MO detection on 5-minute algorithm windows, storing results as time-series feature records and displaying them on a multi-page physician dashboard.

الوصف

هذا المشروع هو مراقب نوم EEG يستخدم لوحات OpenBCI Cyton لاكتشاف التأثيرات المودولاتورية (MO) في الوقت الفعلي. يدعم مشروعنا طريقتين للبيانات: معالجة البATCH عبر تحميل ملفات EDF و حسابات الحواسيب على WebSocket باستخدام Raspberry Pi. يعمل النظام على اكتشاف MO خلال فترات التمرين الخمس دقائق، ويخزن النتائج كملفات زمنية ميزانية ويعرضها على لوحة مراقبة طبية متعددة الصفحات.

Novelty

7/10

Tags

real-time-monitoring sleep-analysis modulatory-oscillation-detection edge-compute websocket-gateway patient-dashboard threshold-based-alerts

Technologies

fastapi matplotlib numpy plotly scikit-learn scipy sqlalchemy streamlit uvicorn

Claude Models

claude-sonnet-4.6

Quality Score

C
63.7/100
Structure
64
Code Quality
61
Documentation
70
Testing
50
Practices
63
Security
84
Dependencies
60

Strengths

  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No CI/CD configuration \u2014 manual testing and deployment
  • 523 duplicate lines detected \u2014 consider DRY refactoring
  • 1 'god files' with >500 LOC need decomposition

Recommendations

  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a linter configuration to enforce code style consistency
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

9.3h
Tech Debt (C)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (2)
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/
Unknown
License
4.8%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
72.8%
markdown
26.5%
shell
0.3%
cpp
0.3%
text
0.1%
yaml
0.0%

Frameworks

FastAPI SQLAlchemy

Concepts (2)

All metrics by Repobility · https://repobility.com
CategoryNameDescriptionConfidence
Repobility · MCP-ready · https://repobility.com
auto_descriptionProject DescriptionReal-time EEG monitoring and Modulatory Oscillation (MO) detection for sleep analysis using OpenBCI Cyton boards. Supports two data paths: EDF file upload (batch processing) and Raspberry Pi live streaming (edge compute over WebSocket).80%
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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