zig-syrup-bci Skill | Agent Skills

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 ✓

PR

https://github.com/plurigrid/zig-syrup/pull/2

Agent Skills: zig-syrup-bci

Install this agent skill to your local

Skill Files