Inspiration Mental health issues often go undetected until they significantly impact a person's life. As neuroscientists and engineers, we wanted to leverage real-time brain data to enable early detection and timely care—turning passive health tracking into proactive mental wellness.
What it does MindWave is a cross-platform mobile app that monitors EEG signals (Alpha, Beta, Gamma waves) from wearable devices to compute an anxiety score in real-time. When the score crosses a personalized threshold, users receive a warning and can book sessions with licensed psychologists directly through the app.
How we built it Frontend: Built using React Native for cross-platform support (iOS & Android) with a modular component design.
Backend: Supabase handles authentication (email/password) and data storage (user info, EEG logs, psychologist availability).
EEG Simulation: We used mock EEG data streams in JSON format to simulate real-time input for Alpha, Beta, and Gamma waves.
Anxiety Score: Calculated using a simple ratio of Beta to Alpha, then normalized to a 0–100 scale.
Booking System: Psychologist availability is mocked in a Supabase table, with a basic calendar-style interface for scheduling.
Challenges we ran into Designing a meaningful anxiety score from EEG ratios while keeping it intuitive for users.
Simulating real-time EEG data and ensuring smooth streaming without performance lag.
Balancing simplicity for patients with functionality for clinicians in one app.
Accomplishments that we're proud of Building a fully functional prototype that delivers real-time brainwave visualization and actionable insights.
Creating a clean, accessible UI for users of varying technical backgrounds.
Implementing a scalable architecture ready for future device integration and AI-driven diagnostics.
What we learned How to translate EEG data into meaningful mental health metrics.
The importance of UX design in mental health tools—users need clarity, not complexity.
How powerful and flexible Supabase can be as a backend for rapid prototyping.
What's next for MindWave Integrate with actual wearable EEG devices (e.g., Muse, OpenBCI).
Train ML models for more accurate anxiety and burnout prediction.
Add a journaling and mood tracking feature to correlate subjective and objective data.
Expand booking to include therapists, coaches, and peer support.
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