EN — Data-collection tools (separate from Monkey Run). Export JSON for another AI.
ES — Herramientas para agarrar datos (aparte de Monkey Run). Exporta JSON para otra AI.
🎨 Stimuli: simple shapes + flat colors (no gradient on the figure).
🌫️ Backgrounds: noise / film grain so the stimulus stays readable.
| URL | 🇺🇸 English | 🇪🇸 Español |
|---|---|---|
/attention.html |
🎯 Personal Attention threshold | Umbral Attention personal |
/calibrate.html |
👁️ Blink + color + shape + L/R | Blink + color + forma + izq/der |
/portal.html |
⚽ Attention arms · blink shoots · catch | Portería + catch no-go |
/wave.html |
📈 Continuous monitor | Monitor continuo |
npm run serial -- COM18 115200
npm run attention # or: calibrate / portal / waves| Script | Opens |
|---|---|
npm run attention |
🎯 Attention cal |
npm run calibrate |
👁️ Blink study |
npm run portal |
⚽ Portería |
npm run waves |
📈 Monitor |
Each study has Export JSON (AI).
The file includes:
study,hypothesis,promptHint- trials with
colorId,side,shape,att,med,signal,blink,latencyMs,outcome
Paste the JSON into another AI using promptHint.
- 🎯
attention.html→ saves threshold tolocalStorage - ⚽
portal.html→ uses that threshold - 👁️
calibrate.html→ more color / side / shape data - 📤 Export all three JSONs → analyze together
We keep color + L/R labels on purpose.
With a 1-channel MindWave you cannot prove perceptual decoding from EEG alone — but you can hunt for correlations (Attention / latency × stimulus). Ask the AI to separate those clearly.
📚 Also see: github-neurosky-landscape.md · ../function-vectors.md · ../tasks.md
| File | Notes |
|---|---|
study/mindwave-exam-*.json (local) |
Full waveLog — gitignored (too large) |
mindwave-exam-20260724-1803.stats.json |
Stats for math AI |
mindwave-exam-analysis-2026-07-24.md |
Deducción: att lectura≫figuras; color/lado/forma no explican RT |
blink-color-session-2026-07-24.json |
Blink+color n=24 |
blink-color-analysis-2026-07-24.md |
Análisis blink+color |
🥑 aoxilus