Opennirscap Build

Blocker: Altium to Gerber Conversion

Repo has Altium .PcbDoc source files, no exported gerbers or CSV BOMs.

PCBs to convert

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)

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

BOM — PCBs (JLCPCB, after gerber export)

• 24x sensor module (25mm circular)
• 1x ECU (112x112mm, 4-layer)
• 1x STLINK breakout

Cost Summary

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: