Elevating Human Performance Through Neurofeedback
PRODUCT
Neuphoria
INDUSTRY
HealthTech
PRODUCT TYPE
Digital Neurotech Platform (Wearable EEG + Brain Data Insights + Personalized Neurofeedback)
PLATFORM
Web & Mobile App
— OVERVIEW
PROJECT
summary
Neuphoria is a wearable brain-feedback device that uses real-time EEG technology to measure brain activity, interpreting brainwaves to understand what's happening inside your mind.
— THE HURDLES
CORE CHALLENGES & my approach
Challenges
Many people struggle with focus, stress, creativity, productivity, and overall mental performance because these challenges originate in the brain before any actions or feelings occur.
Users lack visibility into real‑time brain activity and have no reliable way to measure, analyze, or improve their cognitive states using objective data.
My Approach
Neuphoria uses real-time EEG brainwave monitoring to help users understand their mental states (e.g., focus vs. distraction) before behaviors happen.
Neuphoria enables users to train their brain toward optimal states like flow, calm focus, and creativity through personalized neurofeedback.
Neuphoria allows users to measure real progress with data instead of guessing whether mental performance is improving.
— Features
SOLUTION I provided
We built an end-to-end system connecting the device, mobile app, backend, and web dashboard, designed for reliability and scalability.
1.MOBILE APPLICATTIONS ( REACT NATIVE)
Enabled seamless sleep tracking through smart hardware. We used BL653 development & testing kit. Key capabilities included:
Built a single React Native codebase for iOS and Android- Integrated Bluetooth Low Energy (BLE) to connect with EEG wearable devices
- Streamed real‑time brainwave data (Alpha, Beta, Gamma, Theta, Delta, etc.)
- Handled session lifecycle, device connectivity, buffering, and retries
2.BACKEND DATA PROCESSING (NODE.JS)
Handled and organized large volumes of health data securely and efficiently. Key capabilities included:
- Designed scalable Node.js APIs to ingest high‑frequency EEG streams
- Performed data validation, normalization, and enrichment
- Orchestrated ML inference and AI report generation pipelines
3.MACHINE LEARNING PIPELINE
Implemented a dedicated Python FastAPI-based ML service for model inference and experimentation
- Implemented a dedicated Python FastAPI-based ML service for model inference and experimentation
- Designed and trained custom ML models to classify ADHD vs Non-ADHD patterns
- Algorithms used:Logistic Regression, Random Forest Classifier, Gradient Boosting Classifier
- Deployed the FastAPI ML service on Google Vertex AI for scalable, production-grade inference
4.DATA STORAGE & ANALYTICS
Ensured users stayed informed while maintaining platform stability. Key capabilities included:
- Stored raw and processed EEG data in Google BigQuery
- Enabled large‑scale analytical queries across sessions and users
- Powered dashboards and cognitive metrics to visualize trends, comparisons, and improvements over time
5.AI GENERATED INSIGHTS
Provided a secure and smooth experience for device purchases and transactions. Key capabilities included:
- Integrated OpenAI and Google Gemini for natural‑language analysis
- Used LangChain to orchestrate prompt pipelines and context injection
- Generated human‑readable reports summarizing: Session quality, Brainwave balance, Cognitive strengths and anomalies, Longitudinal performance insights
— Features
SYSTEM ARCHITECTURE &
scalability
MACHINE LEARNING
Python, Scikit-learn
Accurate, data-driven predictions
BACKEND
Node.js
Scalable, reliable APIs
ML DEPLOYMENT
Google Vertex AI
Scalable, production-ready inference
DATA WAREHOUSE
Google BigQuery
Fast, large-scale analytics
AI/LLMS
OpenAI, Google Gemini, LangChain
Intelligent, actionable insights
MOBILE
React Native (iOS & Android), BLE
Performance, and smooth experience
CLOUD
Google Cloud Platform (GCP)
Secure, scalable, globally accessible
— DIAGRAM
ARCHITECTURE diagram
This architecture diagram gives a high-level view of how the app's frontend, backend, and integrations work together. It shows the flow of data between users, services, and infrastructure for better clarity and understanding.
— OUTCOMES
RESULTS & impact
End-to-End Development
Built a production‑grade neurofeedback platform from zero to deployment
Real-Time Analysis
Enabled real‑time brainwave streaming and analysis at scale
Performance Insights
Delivered objective, data‑driven mental performance insights
— FEEDBACK
CLIENT testimonial
Habib led the full architecture and development of our neurofeedback platform from the ground up. He built a highly scalable backend in Node.js, a responsive React frontend, and designed a robust data pipeline leveraging PostgreSQL and BigQuery. Beyond full-stack engineering, he successfully developed and deployed our machine learning workflows in Python, transforming complex EEG brain data into meaningful, real-world insights for our users. His ability to seamlessly combine system architecture, data engineering, and applied AI into a production-ready platform was truly exceptional.
Neuphoria Client
— HealthTech Industry
