senushidinara/neurosecure-africa

# NeuroSecure-Africa **🧠 NeuroSecure Africa — AI-Powered Neurodata Monitoring for Early Memory Loss Prediction** **Author:** `senushidinara` (Senushi Dinara) NeuroSecure Africa is a cutting-edge, AI-powered wearable system designed to predict early memory loss by analysing brain activity during sleep. The project is built around strong principles of **data privacy** (local/device-first processing) and **affordability** across Africa via integrated FinTech options (micropayments / insurance). --- ## 💡 Project Summary The system integrates EEG/MEG data acquisition, advanced signal processing, and machine learning to deliver personalized risk assessments and actionable interventions. Core principles: - **Privacy-first:** Sensitive neurodata is encrypted and processed primarily on-device. - **Affordable access:** FinTech integration to provide micro-payments and insurance-backed access. - **Clinical-minded:** REM fingerprinting and hippocampal-stress models to flag early memory-risk signatures. --- ## 🚀 How It Functions - **Data Acquisition:** Wearable EEG headband captures brainwave bands (gamma, beta, alpha, theta, delta) and REM / non-REM epochs. - **Data Preprocessing:** Noise filtering, normalization, and segmentation into sleep cycles. - **AI/ML Analysis:** REM fingerprinting and predictive models estimate hippocampal stress and memory-loss risk. - **Security & Encryption:** AES-256 for sensitive on-device data; secure sync for optional cloud ops. - **Mobile Integration:** iOS/Android app presents results, reports, and FinTech payment options. --- ## 📂 Repository Structure NeuroSecure-Africa/ ├─ data/ # EEG/MEG raw and preprocessed datasets (git-ignored; use git-lfs) ├─ models/ # Trained AI/ML models (e.g., rem_fingerprint.pkl) ├─ src/ # Source code │ ├─ preprocessing.py # Noise filtering, normalization, segmentation │ ├─ ai_analysis.py # Model inference, risk classification, alerts │ ├─ security.py …