ML-Powered EEG Analysis

Detect ADHD with
Brain Signal AI

NeuroSakhi analyzes EEG brainwave patterns using machine learning to assist in ADHD screening — fast, private, and clinician-ready.

LIVE EEG SIGNAL SIMULATION
EEG Signal Analysis

Upload your EEG recording in CSV or EDF format. Our ML pipeline extracts 15+ neurological features and runs your sample through a trained classifier in seconds.

ðŸ“Ą
Drop your EEG file here
or click to browse from your device
.CSV .EDF .TXT
✓ File loaded and ready for analysis
📁
File Upload
⚙ïļ
Signal Processing
ðŸ§Ū
Feature Extraction
ðŸĪ–
ML Prediction

PREDICTION RESULT
ADHD DETECTED

Elevated theta/beta ratio and reduced alpha coherence patterns are consistent with ADHD neurological signatures. Confidence: 91.3%. This result is for screening purposes only — consult a licensed clinician.

91% CONFIDENCE
THETA POWER
2.84 ΞVÂē
BETA POWER
0.91 ΞVÂē
THETA/BETA RATIO
3.12
ALPHA COHERENCE
0.43
SAMPLE ENTROPY
1.76
DELTA POWER
1.12 ΞVÂē
📊 Band Power Distribution
📈 Entropy Level Trend
🔍 Feature Insights Panel
THETA/BETA RATIO
3.12
HIGH ↑
ALPHA POWER
1.21 ΞVÂē
REDUCED ↓
SAMPLE ENTROPY
1.76
ELEVATED ↑
SPECTRAL EDGE
18.4 Hz
NORMAL
HJORTH MOBILITY
0.89
ELEVATED ↑
KURTOSIS
3.41
NORMAL
Processing Pipeline

From raw EEG upload to prediction result in under 10 seconds.

📁
EEG Upload
CSV / EDF
→
🔧
Preprocessing
Filter + Segment
→
⚗ïļ
Feature Extraction
15+ EEG Features
→
ðŸĪ–
ML Model
Scikit-learn
→
📊
Result Dashboard
Score + Insights
Tech Stack

Built with robust, open-source scientific Python libraries and deployable to modern cloud platforms.

🐍
Python
Core Language
🔎
Scikit-learn
ML Model
🌊
Streamlit
Web UI / Dashboard
🐞
Pandas
Data Processing
📐
NumPy
Signal Math
📈
Plotly
Visualizations
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MNE-Python
EEG Processing
☁ïļ
Render / HF
Cloud Deploy
🚀
AWS / GCP
Scale Infra
Privacy-First Design

Patient data protection is built into the architecture from day one — not an afterthought.

🗑ïļ
No Permanent Storage
Uploaded EEG files are processed in-memory and deleted immediately after prediction. Nothing is written to persistent storage.
✓ ACTIVE
ðŸ’ŧ
Local Processing Mode
Clinicians can run NeuroSakhi entirely offline on their own machine — no data ever leaves the hospital network.
✓ ACTIVE
🔐
Encrypted Transit
All data in transit is encrypted via TLS 1.3. No raw EEG data is logged or cached by proxies or load balancers.
✓ ACTIVE
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HIPAA Compliance
Roadmap includes full HIPAA compliance: audit logs, role-based access, BAA support for healthcare institutions.
âģ COMING SOON
🇊🇚
GDPR Ready
Right-to-erasure, data minimization, and consent management framework planned for international deployment.
âģ COMING SOON
ðŸ›Ąïļ
Anonymized Analytics
Only anonymized, aggregated model performance metrics are collected to improve the system. No PII ever.
✓ ACTIVE