opennirscap-build
Build an OpenNIRScap 24-channel fNIRS brain cap from open-source hardware — Altium conversion, BOM sourcing, PCB fab, assembly, firmware flash
Best use case
opennirscap-build is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Build an OpenNIRScap 24-channel fNIRS brain cap from open-source hardware — Altium conversion, BOM sourcing, PCB fab, assembly, firmware flash
Teams using opennirscap-build should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/opennirscap-build/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How opennirscap-build Compares
| Feature / Agent | opennirscap-build | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Build an OpenNIRScap 24-channel fNIRS brain cap from open-source hardware — Altium conversion, BOM sourcing, PCB fab, assembly, firmware flash
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# OpenNIRScap Build Open-source 24-channel fNIRS wearable brain cap. Kim et al. 2025, University of Toronto. ## Source - Repo: https://github.com/tonykim07/fNIRS - Paper: https://arxiv.org/abs/2505.20509 - License: BSD-3-Clause - Hardware: Altium Designer files (PcbDoc, SchDoc) - Firmware: STM32 C (STM32L476RET6) - Software: Python Flask-SocketIO GUI ## Architecture - 24 detector modules (VBPW34S photodiode + AD8618 TIA, 25mm circular PCB) - 8 dual-wavelength source emitters (VSMD66694: 660nm + 940nm) - 1 ECU (STM32L476 + 8x TMUX1104 analog MUX + PCA9685 PWM, 112x112mm PCB) - 35mm source-detector separation (cortical depth sensitivity) - 1 kHz effective sampling, 52.3 dB SNR, 12-bit ADC - USB isolated (ADUM4160), LiPo powered (1100mAh, >5hr) ## Blocker: Altium to Gerber Conversion Repo has Altium .PcbDoc source files, no exported gerbers or CSV BOMs. ### PCBs to convert | Project | File | Description | |---------|------|-------------| | fNIRS_sensor_module | SensorModulePCB.PcbDoc | 25mm circular, combined emitter+detector | | fNIRS_ECU | fNIRS_PCB.PcbDoc | 112x112mm main control board | | DetectorOptode | DetectorOptode.PcbDoc | Detector-only variant | | SourceOptode | SourceOptode.PcbDoc | Source-only variant | | STLINK_Breakout | STLINK_Breakout.PcbDoc | Programming adapter | ### Conversion approaches (in order of preference) 1. **KiCad 8 built-in importer** — File > Import > Altium .PcbDoc. Best fidelity. Needs KiCad installed. 2. **altium2kicad** (Perl, 933 stars) — `perl convertpcb.pl file.PcbDoc`. Repo: thesourcerer8/altium2kicad. Needs `unpack.pl` first. 3. **Altium 365 Viewer** (free web) — View online, manual export. No CLI. 4. **GitHub Issue** — Ask authors to export gerbers. Repo is active (last push 2026-03). ### Attempted and failed - `altium-cli` (Rust, akiselev) — broken workspace deps, won't build - `pyaltium` — crashes on Python 3.14 (missing dateutil dep) - `altium2kicad` unpack.pl worked but convertpcb.pl couldn't find unpacked files ### Next step Install KiCad 8 (`brew install --cask kicad` or `nix shell nixpkgs#kicad`), import PcbDoc, export gerbers + BOM + CPL from KiCad. ## BOM — Semiconductors (DigiKey) | Part | MPN | Qty | DigiKey URL | |------|-----|-----|-------------| | Photodiode | VBPW34S | 24 | https://www.digikey.com/en/products/detail/vishay-semiconductor-opto-division/VBPW34S/4073399 | | Quad op-amp | AD8618ARUZ | 12 | https://www.digikey.com/en/products/detail/analog-devices-inc/AD8618ARUZ/1993937 | | Dual LED 660/940nm | VSMD66694 | 8 | https://www.digikey.com/en/products/detail/vishay-semiconductor-opto-division/VSMD66694/7681025 | | N-ch MOSFET | BSD840NH6327XTSA1 | 16 | https://www.digikey.com/en/products/detail/infineon-technologies/BSD840NH6327XTSA1/5409567 | | MCU | STM32L476RET6 | 1 | https://www.digikey.com/en/products/detail/stmicroelectronics/STM32L476RET6/5177041 | | Analog MUX | TMUX1104DBVR | 8 | https://www.digikey.com/en/products/detail/texas-instruments/TMUX1104DBVR/9685876 | | PWM/IO | PCA9685PW,118 | 3 | https://www.digikey.com/en/products/detail/nxp-usa-inc/PCA9685PW-118/2034325 | | USB isolator | ADUM4160BRWZ | 1 | https://www.digikey.com/en/products/detail/analog-devices-inc/ADUM4160BRWZ/1861340 | ## BOM — Passives (0603 SMD, from schematic) Core known values (exact values need Altium schematic export): - 60.4k 1% x24 (TIA feedback) - 200pF x24 (TIA bandwidth, fc~13kHz) - 100nF x60 (bypass, 2 per board + ECU) - 4.7k x4 (I2C pullups) - LED current limit resistors x16 ## BOM — Amazon | Item | URL | |------|-----| | 3.7V 1100mAh LiPo JST | https://www.amazon.com/Qimoo-Battery-Rechargeable-Connector-Electronic/dp/B0CNLNGBT4 | | ST-LINK V2 programmer | https://www.amazon.com/ST-Link-Programming-Emulator-Downloader-Random/dp/B08YZ4K3Z5 | | 8-pin ribbon cable IDC | https://www.amazon.com/DMiotech-Ribbon-Digital-Cameras-Computers/dp/B0CJYV768J | | PLA filament 1.75mm | https://www.amazon.com/ELEGOO-PLA-Filament-1-75mm-Printers/dp/B0C14PXRZH | | Velcro strips | https://www.amazon.com/VELCRO-Brand-Sticky-Fasteners-Perfect/dp/B00006IC2L | | 3M Micropore tape | https://www.amazon.com/Micropore-Medical-Dispenser-Friendly-NonSterile/dp/B00PZ2F1Q6 | | Neoprene headband | https://www.amazon.com/Headband-Neoprene-Hairband-Non-Slip-Snorkeling/dp/B0FB8Y968R | ## BOM — PCBs (JLCPCB, after gerber export) - 24x sensor module (25mm circular) - 1x ECU (112x112mm, 4-layer) - 1x STLINK breakout ## Cost Summary | Category | Cost | |----------|------| | Semiconductors | ~$130 | | Passives | ~$15 | | PCBs | ~$13 | | Amazon | ~$150 | | **Total** | **~$310-420** | ## Assembly 1. Export gerbers from KiCad (after Altium import) 2. Order PCBs from JLCPCB 3. Order DigiKey + Amazon parts 4. Solder sensor modules (24x, SMD 0603 + photodiode + LED) 5. Solder ECU (QFP-64 MCU, SOT-23 MUXes, TSSOP PWM) 6. Wire cable harnesses (8 groups of 3 sensors) 7. 3D print sensor capsules (STL from repo or design in OpenSCAD) 8. Flash firmware via SWD (ST-LINK) 9. Connect USB, run Python GUI 10. Calibrate on forehead, verify heart rate waveform first ## bci.horse Integration - Tree: bcf-0036 in plurigrid/horse - Inventory ID: planned, not yet ordered - Role: walk-up fNIRS fleet ($419/unit vs $50k NIRSport 2) - Calibrate against Artinis Brite Lite (if purchased) ## Firmware Located in `firmware/STM32/fNIRS/`. STM32CubeIDE project. - MCU: STM32L476RET6, ARM Cortex-M4 80MHz - ADC: 12-bit, DMA, 5kHz raw sampling, multiplexed to 1kHz effective - USB: CDC serial via ADUM4160 isolator - PWM: PCA9685 drives LED sources, alternating 660/940nm - SD: SDMMC logging ## Software Located in `software/`. Python 3. - Flask-SocketIO backend for real-time streaming - Web GUI: Plotly.js + BrainNet Viewer 3D brain mesh - Modified Beer-Lambert Law processing (NIRSimple library) - Bandpass 0.01-0.5 Hz for cortical hemodynamics - Correlation-based signal improvement for HbO/HbR ## Related Skills - `forester` — bci.horse forest publishing (bcf-0036 is this device's tree) - `reverse-engineering` — MCP servers for Ghidra/radare2/IDA if binary analysis needed - `binary-triage` — systematic binary survey workflow - `performing-firmware-extraction-with-binwalk` — firmware extraction from embedded devices - `protocol-reverse-engineering` — reverse engineering communication protocols - `radare2-hatchery` — radare2 ecosystem tools ## Altium Conversion Tools - `altium2kicad` (Perl, 933 stars): https://github.com/thesourcerer8/altium2kicad - KiCad 8 built-in Altium importer: https://www.kicad.org/blog/2020/04/Development-Highlight-Altium-Pcb-Importer/ - `altium-cli` (Rust, broken): https://github.com/akiselev/altium-cli - KiCad Forum thread on CLI import: https://forum.kicad.info/t/how-to-import-altium-pcbdoc-via-commandline-tools/50893 ## Why This Build ### The problem Research-grade fNIRS (NIRx NIRSport 2) costs $50k-150k. PLUX biosignalsplux fNIRS sensor is $789 for 1 channel with short source-detector separation — measures superficial scalp hemodynamics, not cortical activity. ### Source-detector separation is everything Light follows a banana-shaped path through tissue. Separation controls measurement depth: - <10mm: scalp blood flow (artifact) - 30mm: gray matter (cortical hemodynamics — what we want) - >50mm: too much attenuation Per Strangman et al. (PLOS ONE 2013): every 10mm increase up to ~45mm adds ~4% gray matter sensitivity. Depth sensitivity decays as S = 0.75 * 0.85^depth. PLUX has emitter+detector in one housing = short path = scalp oximetry. NIRSport 2 uses separate optodes at 30mm = cortical imaging = $50k+. OpenNIRScap uses separate optodes at 35mm = cortical imaging = $419. ### Why OpenNIRScap specifically - 24 channels (vs PLUX 1ch, vs DIY-fNIRS headband 4ch) - 35mm separation (proper cortical depth) - 52.3 dB SNR, 1 kHz sampling - Validated hemodynamic response during cognitive tasks (paper Fig. 10) - $419 total BOM from off-the-shelf parts - Fully open source (BSD-3, hardware + firmware + software) - Published May 2025, repo active March 2026 - Comparison from paper (Table II): 24ch/$419/open vs 4ch/$215/open vs 1ch/$789/proprietary ### The strategy for bci.horse Build 3x OpenNIRScap ($1,257) as the walk-up fNIRS fleet. If/when a commercial reference is acquired (Artinis Brite Lite ~$6k or Cortivision Spectrum C23), use it to validate OpenNIRScap signal quality. The AFE4404 in the earlier DIY spec (bcf-0012) uses the same modified Beer-Lambert deconvolution as NIRx's proprietary pipeline — the physics is identical, only the engineering precision differs. ### Key references - OpenNIRScap paper: https://arxiv.org/abs/2505.20509 - OpenNIRScap repo: https://github.com/tonykim07/fNIRS - ninjaNIRS (200 optodes, openfnirs.org): https://openfnirs.org/hardware/ninjanirs/ - Strangman depth sensitivity: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0066319 - NIRDuino (Arduino fNIRS): https://www.medrxiv.org/content/10.1101/2024.12.20.24318425v1 - FlexNIRS (open-source): https://www.sciencedirect.com/science/article/pii/S1053811922003408 - DIY-fNIRS headband: https://www.hardware-x.com/article/S2468-0672(21)00033-X/fulltext - Lieberman Lab UCLA (NIRx NIRSport 2 user): Miao, Lieberman, Pluta 2025 hyperscanning review - Artinis Brite Lite: https://www.artinis.com/brite-lite - Cortivision Spectrum C23: https://www.cortivision.com/spectrum-23/ - PLUX fNIRS sensor datasheet: https://support.pluxbiosignals.com/wp-content/uploads/2021/10/biosignalsplux-FNIRS-Datasheet.pdf - bci.horse source-detector physics tree: bcf-0034 in plurigrid/horse - bci.horse fNIRS comparison: bcf-0032 (NIRSport), bcf-0035 (Brite Lite), bcf-0036 (OpenNIRScap)
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