zig-syrup-bci
Multimodal brain-computer interface pipeline for zig-syrup. Parses, processes, and classifies signals from EEG, fNIRS, eye tracking, and body pose modalities with GF(3) trit conservation.
Modules
| Module | File | Trit | Purpose |
|---|---|---|---|
dsi24_parser |
src/dsi24_parser.zig |
0 | Wearable Sensing DSI-24 24ch dry EEG (84-byte packets, ADS1299, 300Hz) |
fnirs_processor |
src/fnirs_processor.zig |
+1 | Modified Beer-Lambert Law: raw optical → HbO/HbR/HbT concentrations |
eyetracking |
src/eyetracking.zig |
-1 | IVT fixation/saccade classifier, pupillometry, blink detection |
lsl_inlet |
src/lsl_inlet.zig |
0 | Lab Streaming Layer C FFI + software-only fallback, StreamSynchronizer |
pose_bridge |
src/pose_bridge.zig |
0 | Body tracking joint angles → movement trit (tremor detection) |
edf_writer |
src/edf_writer.zig |
0 | EDF+ format writer for EEG archival (MNE/EEGLAB compatible) |
edf_reader |
src/edf_reader.zig |
0 | EDF/EDF+ parser validated against PhysioNet BCI2000 (65ch, 160Hz) |
bci_receiver |
src/bci_receiver.zig |
0 | Universal 9-modality receiver (nRF5340 target) |
erc |
src/erc.zig |
0 | Ensemble Reservoir Computing: ensemble averaging, NLMS online learning → trit |
fft_bands |
src/fft_bands.zig |
0 | Comptime-memoized FFT, Welch PSD, EEG band extraction |
GF(3) Conservation
eeg(0) + fnirs(+1) + eye(-1) = 0 mod 3 ✓
Verified across module boundaries in bci_integration_test.zig (16 tests).
Quick Start
# Run all BCI tests
zig build test-bci
# With real PhysioNet data (downloads 1.2MB EDF)
curl -sL -o testdata/S001R01.edf \
"https://physionet.org/files/eegmmidb/1.0.0/S001/S001R01.edf"
zig build test-bci
Test Fixtures
| File | Size | Source | Tests |
|---|---|---|---|
src/testdata/fixture_2ch.edf |
800B | Synthetic | EDF round-trip, basic parsing |
src/testdata/subsecond_starttime.edf |
17KB | MNE testing | 4ch EDF+C, subsecond timestamps |
src/testdata/test_utf8_annotations.edf |
48KB | MNE testing | 12ch synthetic waveforms |
testdata/S001R01.edf |
1.2MB | PhysioNet BCI2000 | 65ch real EEG (gitignored) |
testdata/minimal.xdf |
2KB | xdf-modules | XDF reference (2 LSL streams) |
testdata/minimum_example.snirf |
14KB | fNIRS/snirf-samples | SNIRF HDF5 reference |
Key APIs
DSI-24 Parser
const sample = try dsi24.parseDSI24Packet(&packet_84bytes);
// sample.eeg_channels[0..21] — µV values
// sample.aux_channels[0..3]
// sample.sample_counter, .timestamp_us
fNIRS Modified Beer-Lambert
const config = fnirs.WavelengthPair.plux(); // 660/860nm, DPF 6.51/5.60
const hemo = fnirs.beerLambert(delta_od1, delta_od2, config);
// hemo.hbo, .hbr, .hbt — µmol/L concentration changes
const reading = fnirs.FNIRSReading.fromConcentration(hemo, timestamp_ms, threshold);
// reading.trit — .plus (activation), .zero (baseline), .minus (deactivation)
Eye Tracking IVT
const result = eye.classifyIVT(current_gaze, prev_gaze, .{});
// result.event — .fixation, .saccade, .blink
// result.velocity — degrees/second
// result.event.toTrit() — .zero (fixation), .plus (saccade), .minus (blink)
EDF Reader
const edf = try edf_reader.EDFFile.parse(file_bytes);
// edf.n_channels, .n_records, .record_duration
// edf.channels[i].labelStr(), .unitStr(), .samples_per_record
const digital = try edf.getSample(record, channel, sample_idx);
const physical_uv = edf.toPhysical(channel, digital);
ERC (Ensemble Reservoir Computing)
var reservoir = erc.Cyton.init(.entropy_weighted);
const result = reservoir.processFromBandPowers(all_bands);
// result.trit, .confidence, .logits[3], .ensemble_entropy
// Online adaptation (NLMS — learning rate independent of feature scale)
const config = erc.LearningConfig{ .learning_rate = 0.5 };
const mse = reservoir.adaptFromBandPowers(all_bands, .plus, config);
// mse → convergence monitor; weights adapt to real EEG data
// Propagator integration
const cv = reservoir.toCellValue(); // → CellValue(f32)
LSL StreamSynchronizer
var sync = lsl.StreamSynchronizer.init();
const eeg_id = try sync.addStream(.{ .stream_type = .eeg, .nominal_rate = 300.0, ... });
// StreamType.trit(): eeg→0, fnirs→+1, eye_tracking→-1
SDF Verification (Ch1-Ch8)
| SDF Chapter | Score | Evidence |
|---|---|---|
| Ch1 Combinators (+1) | ★★★ | Composable parse→scale→classify pipeline |
| Ch2 DSL (-1) | ★★☆ | DSI-24 packet DSL, EDF header grammar |
| Ch3 Generic Arithmetic (0) | ★★☆ | Trit type generic across all modalities |
| Ch4 Pattern Matching (+1) | ★★★ | Packet type dispatch, IVT event classification |
| Ch6 Layering (+1) | ★★☆ | Physical/digital layers in EDF, metadata in LSL |
| Ch7 Propagators (0) | ★★★ | Full EEG→FFT→Cell→neurofeedback_gate pipeline, lattice contradiction detection |
| Ch8 Degeneracy (-1) | ★★★ | LSL software fallback, pose threshold redundancy |
Additive Design: ✓
New modalities added without modifying existing modules. Each sensor is a SensorConfig struct registered in UniversalReceiver.init().
Abstraction Barriers: ✓
Three clear layers: acquisition (parsers) → processing (mBLL/IVT/FFT) → classification (trit).
gx10 Deployment
Validated on 4x NVIDIA GB10 nodes (aarch64-linux, 128GB unified memory each):
# Install zig on gx10 node
curl -sL -o /tmp/zig.tar.xz 'https://ziglang.org/download/0.15.2/zig-aarch64-linux-0.15.2.tar.xz'
mkdir -p ~/.local && tar xf /tmp/zig.tar.xz -C ~/.local/
ln -sf ~/.local/zig-aarch64-linux-0.15.2/zig ~/.local/bin/zig
# Clone and test
git clone -b feat/bci-multimodal-pipeline https://github.com/plurigrid/zig-syrup.git
cd zig-syrup && zig build test-bci
gx10 BCI Use Cases
- Headless BCI acquisition server: Run LSL bridge + EDF writer on idle nodes
- Cross-compile target: Native aarch64 build, same arch as embedded targets
- Parallel dataset processing: Parse/classify large EDF archives across 4 nodes
- LoLa integration: BCI trit streams as input features for autoencoder training
Related Skills
| Skill | Trit | Relation |
|---|---|---|
sdf |
-1 | SDF verification framework |
zig |
-1 | Zig ecosystem patterns |
zig-syrup-propagator-interleave |
-1 | Propagator network bridge |
reafference-corollary-discharge |
+1 | Corollary discharge → neurofeedback gate |
bci-colored-operad |
+1 | Operadic composition of BCI channels |
sheaf-cohomology-bci |
0 | Sheaf-theoretic BCI signal fusion |
GF(3) Triads
zig-syrup-bci(0) ⊗ sdf(-1) ⊗ bci-colored-operad(+1) = 0 ✓
zig-syrup-bci(0) ⊗ edf-reader(-1) ⊗ fnirs-processor(+1) = 0 ✓