Edit wiki/research-notes/signal-processing-workflow.md — Work Reflections

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# EEG/signal processing approach at a neurotech startup

over a summer internship at a neurotech startup, i went from knowing nothing about EEG to running signal processing pipelines, debugging hardware, and designing experiments. this is the workflow that emerged — not textbook-clean, but real.

## the problem space

we were trying to detect and measure brain responses to stimuli — visual evoked potentials (VEPs), event-related potentials (ERPs), and various other signals. the core challenge: EEG data is incredibly noisy, and the signals we cared about are tiny.

key signal types:
- **VEPs**: brain response to visual stimuli (checkerboard patterns, flashes). key peaks at N75, P100, N145.
- **ERPs**: brain response to any stimulus (visual, audio, motor). P300 was the easiest to detect.
- **alpha waves**: oscillations in the 8-13Hz range, measurable with eyes closed.
- **frontal alpha asymmetry**: difference in alpha power between left and right frontal regions.

## the processing pipeline

### filtering
- bandpass filter: 1-100Hz (sometimes 0.1-10Hz or 0.8-100Hz depending on what we were looking for)
- notch filter at 60Hz for electrical noise
- tried both hand-coded and library implementations (MNE)

### artifact handling
- blinks and movements were the biggest contaminants
- MNE had artifact rejection tools but getting them working was non-trivial
- environmental noise: footsteps, electrical devices, even heartbeats were picked up
- "others' movements — it picks up on footsteps. your movements — move your head slightly and it messes up the data"

### epoch averaging
- average over 100-200 trials per stimulus to improve signal-to-noise
- "if our data were perfect we wouldn't have to do this but it's chill"
- considered z-score averaging to preserve relative magnitude in spiking

### plotting and analysis
- filtered data overlaid with stimulus timestamps
- FFT / power spectral density
- averaged responses over epochs
- peak detection (automated and manual)

## what i actually learned

### implementation approaches (ranked by usefulness)
1. **library + vibe coded (MNE)**: most robust, best artifact handling
2. **hand coded**: bin average, rolling average — good for understanding what's happening
3. **fully vibe coded**: interpolation lines, rolling averages — sometimes produced plausible but wrong results

### the LLM trap
"data analysis is hard because: big data, for robustness can't really use LLMs that much, every time you use them a lot you fall into a pitfall that makes you cooked."

this was a hard lesson. LLMs are great for boilerplate code but terrible for signal processing decisions. they'll confidently suggest parameters that produce clean-looking but meaningless results. had to learn to [[read papers|reading-papers]] and understand the actual science.

### the iteration loop
"lots of iteration. lots of failure. graphs that don't look right, code that doesn't work. need to get correct times. interpreting graphs — sometimes good but looks bad, sometimes bad but looks good."

the workflow was: write script → run → look at output → realize something is wrong → research → fix → repeat. "writing script robustly actually takes time" — couldn't just hack something together and move on.

## the current testing procedure (by the end)

- conditions: one eye, fixate at center, dark room
- data processing: bandpass from [0.1, 0.8]Hz to [10, 100]Hz, notch at 60Hz
- plot: filtered data with stimuli, FFT, averaged over epochs
- validation: alpha band testing, self-testing with flashing

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*see also: [[debugging hardware|debugging-hardware]], [[experiment design|experiment-design]], [[reading papers|reading-papers]]*