Meet the Team

Our story began when we crossed paths at the University of Waterloo. Our team featured a variety of different skills which came together over the project. The team featured:

Jake Malliaros - The unifying mind

Eli Cavan - The theoretical mind

James Ro - The technical mind

Griffin Barnicutt - The strategical mind

Meet Neo!

Neo is a Python-based platform that bridges the gap between the human brain and smart home devices. In a broad sense, Neo’s input is a user’s EEG data (a measurement of the electrical activity in the brain) from the Muse device; and outputs commands that can be executed by the smart home device. In the context of our Hackathon demo, Neo gave us the ability to control the frequency of a blinking light on the Google AIY voice kit. Neo then proceeded to give active feedback on the control results. The muse allowed us to separate and measure the different brain waves (alpha, beta, gamma, delta, theta); which provides us with information on the user. In the context of our demo, the measurement of the beta waves corresponded to the ability of the user to concentrate. Using the demo, we showed that the frequency of the blinking light increased as the user lost concentration. The user was then able to decrease the frequency of the light by focusing (which increases the concentration of beta waves).

Built With...

Google AIY Voice Kit: A Google assistant device powered by a Raspberry Pi.

Muse EEG Headband: A wearable device that measures EEG data of the user. Connects to a mobile app via Bluetooth to relay data.


Neo was made with a generational problem in mind: mental health and accessibility. One of the obvious benefits of the platform is that a user with accessibility issues can still command the power of these smart home devices. For example, a user who is hearing impaired or speaking impaired would be hard pressed to use a smart home device. With Neo, it is now feasible for those individuals to use a smart home device; and not even have to be in the room when using it. Neo’s functionality does not end there. Since the platform is built upon the Muse device, Neo can be used to train the user to more easily achieve a focused state of mind. Such a state would correspond to a lower frequency of blinks as compared to an unfocused state. In this matter, Neo improves upon the Muse device. Whereas with the Muse you are required to look at a screen, (your phone or desktop) and infer results from live data; Neo gives you instant feedback on your mental state. It’s as simple as watching the light blink faster/slower.

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