Seenapse

Seenapse is a thought-first interface: EEG + smart glasses that turn quiet intent into real-world action—an OS for the world you see, not the screen you touch.

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Inspiration

We wanted to redefine spatial assistance in a way that is discreet, convenient, and inclusive. Traditional systems rely on auditory or manual input, which can be limiting. By combining neural input with Meta Ray Bands, we saw an opportunity to build something more seamless and empowering.

What it does

Seenapse replaces auditory and manual input with neural input, creating a more natural interaction. The device features a silent SOS with location and photo, visual translation, and mood-based audio feedback—all designed to enhance independence and accessibility.

How we built it

We integrated EEG signals from the Numerosity Crown with Meta Ray-Ban smart glasses. Using Python for processing and computer vision libraries for media handling, we linked EEG inputs to control functions in real time. This allowed us to layer brain-driven control over smart eyewear capabilities. Also API's such as Twillo API, Request

Challenges we ran into

The hardest part was getting the EEG signals and Meta Ray-Bans to communicate reliably. Signal noise, calibration, and syncing across devices made the integration especially tough. Ensuring the system was both accurate and user-friendly required extensive iteration.

Accomplishments that we're proud of

We created a unique combination of Meta Ray-Bans and the Numerosity Crown, demonstrating a functional EEG-driven wearable system. This integration is something we haven’t seen done before, and it validated our vision of bridging neural input with smart glasses.

What we learned

We learned how to work with EEG signals, process them effectively, and integrate them into real-world devices. We also gained experience in handling the limitations of consumer hardware and turning those limitations into workable solutions.

What's next for Seenapse

We want to improve EEG signal reliability, refine the user experience, and explore broader use cases like accessibility tools, real-time language translation, and emotional interaction with media. The long-term goal is to develop a truly universal neural interface for everyday assistance.

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