The Fun System That Explains A Lot
Before the bigger neuroscience infrastructure work, I was already drawn to closed-loop systems: signals coming from the world, software transforming them, and physical feedback appearing in real time.
This record brings together a few related builds:
- A NeuroSky to Raspberry Pi to LED strip project for an ECE class at UCSD
- An implementation of the
aztronaut/dancyPi-audio-reactive-ledstyle of audio-reactive LED visualization - NanoLeaf-style lighting experiments
- An audio-reactive galaxy light setup
- Split audio routing so the same signal could drive lights and speakers without introducing distracting lag
Signal Flow
The core idea was to make the signal path physical and immediate.
For brain-signal visualization:
NeuroSky-style EEG input -> attention / meditation / signal features -> Raspberry Pi -> LED strip feedback
For audio-reactive lighting:
Audio output -> split / capture path -> Raspberry Pi processing -> LED strip / lighting response
In parallel:
Audio output -> speakers
That parallel routing mattered. If the lights depended on a slow or awkward capture chain, the system felt wrong. Splitting the audio stream let the music remain clean and low-latency while still feeding a processing path for synchronized visual effects.
What I Built Around
The system combined:
- Real-time signal acquisition
- Raspberry Pi control logic
- LED strip mapping
- Frequency and intensity extraction from audio
- Brain-signal-inspired feedback mappings
- Physical lighting composition across multiple devices
- Practical routing decisions for clean audio and responsive visuals
The point was not just to make lights blink. The interesting part was coordinating multiple feedback elements so the room behaved like one reactive interface.
Why It Belongs on a Professional Page
This is a playful project, but it is also a serious signal about how I think.
It shows:
- Comfort moving between hardware, software, sensors, and physical output
- Interest in real-time feedback loops
- Taste for embodied interfaces
- Practical debugging instincts around latency, routing, and synchronization
- A long-running curiosity about BCI and adaptive environments
That same instinct shows up later in my professional work: closed-loop neuroscience, behavioral boxes, DataJoint workflows, BCI middleware ideas, and real-time systems thinking.
What It Says About Working With Me
I like systems that make invisible signals visible. I like when infrastructure becomes experiential. And I tend to care about the last ten feet of a system: whether it feels responsive, whether the output makes sense, and whether the user can trust what they are seeing.