Clario

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App overview

Inspiration

Many of us on the team have experienced or witnessed the challenges of staying focused with learning disabilities like ADHD and dyslexia. We’ve seen how exhausting it can be to keep attention steady during tests or study sessions, where even small distractions can break focus. Traditional productivity tools often overlook the differences that make focus harder for some people. We wanted to build something that listens back, technology that adapts to the user rather than asking the user to adapt to the technology. Clario grew out of the belief that focus can be supported gently and personally, creating a calmer and more encouraging space for learning and work.

What it does

Clario helps people stay focused through adaptive audio feedback. After a quick one‑minute calibration using the Muse 2 headset (30 seconds of relaxation and 30 seconds of mental math), Clario’s MUSYNC engine reads moment-to-moment focus levels and shapes the accompanying sound in real time. The music adjusts graudally so that distractions fade and immersion becomes easier. A personal listening profile, built from a short questionnaire about sensitivity, energy level, and preferred task type, ensures Clario feels supportive and is more accurate rather than not.

How we built it

We started off attempting to connect our Muse 2 to our device using a Brainflow function, and then studied how to process and filter this EEG data we could now access. After using MNE to filter this data, the team collaboratively researched on how we could manipulate this data and manipulate it into a measure of focus. Following this, we applied our research to music, and its effect on focus, specifically in those with hyperactivity disorders; we created a reinforcement learning pipeline that could generate music depending on the focus measure we calculated before. Putting this all together and adding a couple quality of life features, Clario had been brought to life.

Challenges we ran into

Our biggest challenge was balancing responsiveness with smoothness. Instant feedback from EEG readings can make the sound feel jittery or artificial, so we made the reinforcement learning algorithm's rate of change in music more gradual rather than instant. Creating the reinforcement learning algorithm itself was a struggle, understanding how to read focus levels and continually adapt was a challenge.

Accomplishments that we're proud of

We are proud of creating a music generating engine that continually adapts to EEG data. Putting all the separate components together into a combined, finished product, that genuinely helps in focusing and studying tasks is something the whole team is proud of.

What we learned

We learned how individual differences in attention and sensory processing demand flexible design. EEG data isn’t just numbers, it reflects lived experience. We also found that subtle musical adjustments over time can have a surprisingly big impact on how people sustain effort over long tasks.

What's next for Clario

Next, we plan to expand MUSYNC’s adaptability to cover more types of cognitive work, integrate more headsets beyond Muse 2, and introduce guided focus sessions with real-time progress summaries. We also aim to gamify the product, to make the user more immersed and eager for progress. Alongside this, we want to make this a classroom tool, and thereby, add features that make accessibility easier for teachers, such as a lockdown mode for tests.