EE

EEGFrontier – A compact open-source EEG board using ADS1299

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EEGFrontier – A compact open-source EEG board using ADS1299

Hi HN, I built EEGFrontier, a compact open-source EEG acquisition board based on the ADS1299 and an RP2040. The goal was to design a low-cost board that works with dry electrodes while exposing the full EEG signal chain — no abstractions, no closed firmware. What surprised me most during this project were the practical issues that datasheets don’t really prepare you for: grounding (REF/BIAS), noise coupling from digital lines, routing constraints, and how small layout decisions drastically affect signal quality. The repository includes full KiCad files, firmware, a BOM with cost references, and documentation images. This is a V1 board and already works, but I’m actively iterating on shielding and noise mitigation. I’d really appreciate feedback from people with experience in EEG, biosignals, or analog front-end design — especially criticism.

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: using, open · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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