---
license: gpl-3.0
task_categories:
  - other
language:
  - en
tags:
  - electromyography
  - emg
  - motor-unit-decomposition
  - blind-source-separation
  - dynamic-contraction
  - synthetic
  - neuromuscular
size_categories:
  - n<1K
pretty_name: MUniverse Dynamic EMG Benchmark v2
---

# MUniverse Dynamic EMG Benchmark v2

**416 synthetic non-stationary surface EMG recordings** for benchmarking motor-unit decomposition algorithms under dynamic joint motion. Split into **336 train** (subjects 0–4, 21 MU pools) and **80 held-out val** (subjects 5–9, 5 MU pools) — different simulated subjects, so the val set is a clean generalisation test.

Both splits share the same 8 conditions (sinusoidal/triangular wrist Flexion–Extension at two amplitudes × two SNR levels) and ship at 70-ch and 320-ch electrode configurations.

## Files

| File | Size | Purpose |
|---|---|---|
| `recordings.tar.gz` | 4.7 GB | **Train**: 336 `.npz` recordings, subjects 0–4, 21 MU pools (`benchmark_v2/{ch070,ch320}/`) |
| `recordings_val.tar.gz` | 1.2 GB | **Val**: 80 `.npz` recordings, subjects 5–9, 5 MU pools (`benchmark_v2_val/{ch070,ch320}/`) |
| `reproducibility_bundle.zip` | 36 KB | Scripts, manifests, NeuroMotion patch, PBS templates |
| `benchmark_spec.json` | 7 KB | Declarative spec: subjects, muscles, conditions, combos |
| `README.md` | this page | (also inside the bundle, as the canonical reproduction guide) |

SHA256:
- `recordings.tar.gz`: `715458efd70ca8932e1e45a642698fa11a4446e0352dbe1840e0a7e251544f56`
- `recordings_val.tar.gz`: `e4a14c291f4087a90ee10a376b57c359e3507ba4cdd68bb94b6f7b9aa6cc3756`
- `reproducibility_bundle.zip`: `f4e034726a5c2afd60f408d3e32d63be074a586235316c38926feafd3d3e0988`

## Splits

| Split | Subjects | Pools | Recordings (per ch config) | Recordings total |
|---|---|---|---|---|
| Train | seeds 0–4 (5 subjects) | 21 (6 small + 8 medium + 7 large) | 168 | **336** |
| Val | seeds 5–9 (5 subjects) | 5 (1 small + 2 medium + 2 large) | 40 | **80** |

Subjects 5–9 are completely disjoint from training subjects, sampled from the same NeuroMotion+BioMime generator with the same fiber-density distribution. Val combos:

| Tier | Subject | Muscle | Threshold | N MUs |
|---|---|---|---|---|
| small | sub5 | PL | 0.85 | 29 |
| medium | sub6 | ECU | 0.85 | 50 |
| medium | sub7 | EDI | 0.85 | 62 |
| large | sub8 | FCU_u | 0.85 | 79 |
| large | sub9 | ECRB | 0.90 | 92 |

## Quick load

```python
import numpy as np

# Train recording
d = np.load("benchmark_v2/ch320/SA10-hi_ECU_sub03_N055.npz", allow_pickle=True)

# Val recording
d = np.load("benchmark_v2_val/ch320/SA10-hi_ECU_sub06_N050.npz", allow_pickle=True)

emg            = d["emg"]              # (T, M) float32
spike_mu_0     = d["spike_mu_0"]       # (n_spikes,) int64 — ground-truth indices
angle_profile  = d["angle_profile"]    # (T,) joint angle in degrees
effort_profile = d["effort_profile"]   # (T,) effort, fraction MVC
fs             = int(d["fs"])          # 2048
```

See `benchmark_spec.json` for the full parameter grid.

## Reproduction

The dataset is fully reproducible from open code: NeuroMotion + BioMime → MUAP libraries → crosstalk filtering → NeuroMotion synthesis. Stage-by-stage scripts and PBS templates are inside `reproducibility_bundle.zip`. The val split was generated with the same pipeline using seeds 5–9 instead of 0–4. The original README inside the bundle walks through all five stages.


---

## Original reproducibility README

_Below is the canonical reproduction guide as it appears inside `reproducibility_bundle.zip`._


Complete configuration, scripts, and metadata to regenerate the **v2 benchmark**: 336 non-stationary EMG recordings across 21 motor-unit pool combinations (26–113 MUs), 5 subjects, 7 muscle types, and 8 conditions (sinusoid/triangular angle × 2 difficulties × 2 SNR).

The benchmark was generated using **MUniverse as-is** — no modifications to the core package except a single small patch to NeuroMotion's Triangular angle-profile generator (included in `scripts/patches/`). The story of this bundle is:

1. Generate 40 MUAP libraries using NeuroMotion+BioMime (5 subjects × 8 muscles at Flex-Ext DOF).
2. Analyze pairwise MUAP similarity, filter each library to a "crosstalk-clean" subset.
3. Use NeuroMotion to synthesize 336 EMG recordings from those clean subsets, each exposed to different rotation profiles and noise levels.

## Contents

```
experiments/v2/
├── README.md                              # This file
├── benchmark_spec.json                    # Declarative specification (reviewer-readable)
├── combos.json                            # The 21 subject-muscle-threshold combos used
├── scripts/                               # Scripts used, in execution order
│   ├── 01_generate_muap_caches.py         # Stage 1: Run BioMime to make 40 caches (GPU)
│   ├── 02_analyze_crosstalk.py            # Stage 2: Pairwise similarity analysis
│   ├── 03_create_clean_caches.py          # Stage 3: Filter each cache to a clean subset
│   ├── 04_generate_recordings.py          # Stage 4: Run NeuroMotion on clean caches
│   ├── 05_fix_triangular.py               # Stage 5: Bug fix, regenerate triangular only
│   ├── build_metadata.py                  # (meta) Generates the manifest CSVs below
│   └── patches/
│       └── neuromotion_triangular_symmetric.patch    # NeuroMotion fix
└── metadata/
    ├── muap_caches_manifest.csv           # 40 caches with shapes, fiber density, sha256
    ├── combos_manifest.csv                # 21 clean combo pools
    ├── conditions_manifest.csv            # 8 condition definitions
    └── recordings_manifest.csv            # 336 recordings with full parameters
```

## Prerequisites

1. **MUniverse** — tested against `dynamic_zi_resampling` branch (see `git log`). Clone the repo and install:
   ```bash
   git clone https://github.com/dfarinagroup/muniverse muniverse-live
   cd muniverse-live
   git checkout dynamic_zi_resampling
   pip install -e .
   ```

2. **NeuroMotion Singularity/Docker image** — `pranavm19/muniverse-test:neuromotion`:
   ```bash
   cd src/environment
   singularity pull muniverse-test_neuromotion.sif docker://pranavm19/muniverse-test:neuromotion
   ```

3. **NeuroMotion triangular patch** — apply to `src/muniverse/data_generation/_run_neuromotion.py`:
   ```bash
   cd muniverse-live
   git apply experiments/v2/scripts/patches/neuromotion_triangular_symmetric.patch
   ```
   (Or just copy the `Triangular` branch from that file into your `_run_neuromotion.py` line ~434.)

4. **Hardware**:
   - Stage 1 (MUAP generation): **GPU** — each of 40 caches takes ~2-3 min on A40 / RTX6000.
   - Stages 2–5: CPU only.

5. **Disk**:
   - MUAP caches: ~95 GB permanent
   - Clean caches: ~8 GB
   - Recordings: ~5 GB
   - Raw outputs + configs: ~10 GB ephemeral (safe to delete after gen)

## Reproduction Steps

### Stage 1: Generate 40 MUAP caches (GPU, ~1.5 hours)

Each cache is a `(num_mus, 130, 10, 32, 96)` numpy array — MUAPs over 130 angle steps for each MU on a 10×32 electrode grid.

```bash
# Generate all 40 (5 subjects × 8 muscles at Flex-Ext DOF)
python experiments/v2/scripts/01_generate_muap_caches.py
```

The script edits these hardcoded paths — adjust before running:
- `CACHE_ROOT`: where to write the caches
- `RAW_ROOT`: scratch dir for container outputs (ephemeral is fine)
- `SIF_PATH`: path to the NeuroMotion Singularity image

Output: `<CACHE_ROOT>/cache_sub-simXX/subject_X_{MUSCLE}_Flexion-Extension_muaps.npy` + `_metadata.json` + `_mn_properties.csv`.

### Stage 2: Crosstalk analysis (CPU, ~5 min)

```bash
python experiments/v2/scripts/02_analyze_crosstalk.py
```

Discovers all Flex-Ext caches, computes pairwise cosine similarity at mid-pose, and writes clean-subset selections at thresholds 0.75 / 0.80 / 0.85 / 0.90 to:
`experiments/results/crosstalk_v3/exp_crosstalk_clean_subsets.json`

### Stage 3: Create 21 clean caches (CPU, ~1 min)

```bash
python experiments/v2/scripts/03_create_clean_caches.py
```

Reads the crosstalk JSON and writes 21 filtered caches to `experiments/clean_caches_v2/` (organized as `{tier}/sub{N}_{muscle}_thr{XX}/...`). Also writes `combos.json` — the master list consumed by Stage 4.

### Stage 4: Generate 336 recordings (CPU, ~55 min)

```bash
python experiments/v2/scripts/04_generate_recordings.py \
    --config experiments/clean_caches_v2/combos.json
```

For each combo, runs the container 8 times (one per condition), post-processes into 2 channel configs (ch070 + ch320), and writes:
`<BENCHMARK_ROOT>/ch{070,320}/{cond}_{muscle}_sub{N}_N{K}.npz`

### Stage 5 (only if you hit the bug): Fix triangular recordings

If you run Stage 4 **without** the NeuroMotion patch applied, the 168 triangular recordings will have flat 0° angle profiles. To detect: check any `TA*.npz` file — `angle_profile` should oscillate ±sin_amplitude.

If buggy, patch NeuroMotion and regenerate just the triangular recordings:

```bash
# Submit as array job (21 combos × ~3 min each)
qsub experiments/v2/run_regenerate_triangular.pbs

# Or run interactively, one combo at a time
for i in {0..20}; do
    python experiments/v2/scripts/05_fix_triangular.py --combo-idx $i
done
```

### Rebuild metadata CSVs

```bash
python experiments/v2/scripts/build_metadata.py
```

Regenerates `metadata/*.csv` from the actual files on disk (useful for validation).

## Data Format

Each recording is a `.npz` file following `/BENCHMARK_DATA_FORMAT.md` with:

| Key | Shape | Description |
|-----|-------|-------------|
| `emg` | `(T, M)` float32 | EMG samples-first |
| `spike_mu_{i}` | `(n_spikes,)` int64 | GT spike sample indices per MU (empty for inactive) |
| `angle_profile` | `(T,)` | Joint angle over time (deg) |
| `effort_profile` | `(T,)` | Effort over time (fraction MVC) |
| `fs`, `n_channels`, `n_samples`, `muscle`, `subject_seed`, `af`, ... | scalars | Full parameters |

## Conditions Summary

All 8 conditions use **constant effort at 50% MVC** with **0.3 Hz** sinusoid/triangle. Duration 10s, fs=2048.

| ID | Angle Profile | af (fraction of ±65°) | ±deg | SNR (dB) |
|----|---------------|----------------------|------|----------|
| SA05-hi | Sinusoid   | 0.5 | ±32.5 | 25 |
| SA05-lo | Sinusoid   | 0.5 | ±32.5 | 20 |
| SA10-hi | Sinusoid   | 1.0 | ±65.0 | 25 |
| SA10-lo | Sinusoid   | 1.0 | ±65.0 | 20 |
| TA05-hi | Triangular | 0.5 | ±32.5 | 25 |
| TA05-lo | Triangular | 0.5 | ±32.5 | 20 |
| TA10-hi | Triangular | 1.0 | ±65.0 | 25 |
| TA10-lo | Triangular | 1.0 | ±65.0 | 20 |

## Combo Summary

21 combos grouped into 3 pool-size tiers:

| Tier | # Combos | Pool range | Typical recording |
|------|:-:|:-:|---|
| **Small**  | 6 | 26–45 MUs | PL × 3 subjects + FCU_h + ECRL + FDSI |
| **Medium** | 8 | 47–74 MUs | ECU, FDSI, EDI, ECRB, FCU_u |
| **Large**  | 7 | 77–113 MUs | EDI, ECU, ECRB, FCU_u at threshold 0.90 |

See `combos.json` / `metadata/combos_manifest.csv` for the exact list.

## Verification

After reproduction, verify the dataset is correct:

```bash
# Compare against recordings_manifest.csv (336 rows)
python experiments/v2/scripts/build_metadata.py
diff <(sort experiments/v2/metadata/recordings_manifest.csv) <(sort <your-regenerated>/recordings_manifest.csv)
```

MUAP cache SHA256 hashes (first 16 hex chars) are in `metadata/muap_caches_manifest.csv` for integrity checking of the Stage 1 outputs.

## Citation

```
<pending — paper in prep>
```

## License

Same as MUniverse core (GPL-3.0).