{ "name": "muniverse-dynamic-v2", "version": "1.0", "description": "Non-stationary dynamic EMG benchmark for motor unit decomposition. 336 recordings spanning 3 pool-size tiers (26-113 MUs), 5 subjects, 7 muscle types, 8 conditions (sinusoid/triangular angle × 2 difficulties × 2 SNR).", "source_package": "muniverse", "source_repo_url": "https://github.com/dfarinagroup/muniverse", "generation_pipeline": [ "Step 1: Generate MUAP caches via NeuroMotion+BioMime container (one per subject × muscle at Flexion-Extension DOF, 130 angle steps)", "Step 2: Analyze pairwise MUAP cosine similarity, select clean MU subsets via greedy filtering at given threshold", "Step 3: Write clean caches (same format as original, fewer MUs)", "Step 4: Generate recordings by running container on clean caches with specified conditions", "Step 5: (bug fix) Regenerate triangular-angle recordings with symmetric triangle patch" ], "muap_generation": { "subjects": [ {"seed": 0, "fibre_density": 194.0, "muscle_motor_unit_counts": [206, 210, 181, 187, 141, 486, 154]}, {"seed": 1, "fibre_density": 187.0, "muscle_motor_unit_counts": [176, 176, 208, 167, 168, 340, 135]}, {"seed": 2, "fibre_density": 190.0, "muscle_motor_unit_counts": [186, 217, 164, 178, 153, 437, 192]}, {"seed": 3, "fibre_density": 174.0, "muscle_motor_unit_counts": [167, 224, 198, 186, 157, 420, 189]}, {"seed": 4, "fibre_density": 196.0, "muscle_motor_unit_counts": [194, 205, 198, 205, 187, 408, 163]} ], "muscles": ["PL", "FDSI", "EDI", "ECU", "ECRB", "ECRL", "FCU_h", "FCU_u"], "movement_dof": "Flexion-Extension", "total_caches": 40, "container_image": "pranavm19/muniverse-test:neuromotion", "muscle_label_slot_notes": { "FCU_u": "Replaces FCU slot in MuscleLabels list (custom)", "FCU_h": "Replaces FCU slot in MuscleLabels list (custom)", "standard_labels": ["ECRB", "ECRL", "ECU", "EDI", "PL", "FCU", "FDSI"] } }, "crosstalk_filtering": { "method": "greedy_henneman_order", "similarity": "cosine_similarity at mid-pose (step 65), flattened over channels × time", "description": "Starting with MU 0 (smallest), add MU i if max(sim(i, j)) < threshold for all j already selected" }, "combos": [ {"tier": "small", "subject_seed": 0, "muscle": "PL", "threshold": 0.85, "clean_N": 27}, {"tier": "small", "subject_seed": 2, "muscle": "PL", "threshold": 0.85, "clean_N": 26}, {"tier": "small", "subject_seed": 4, "muscle": "PL", "threshold": 0.85, "clean_N": 30}, {"tier": "small", "subject_seed": 4, "muscle": "FCU_h", "threshold": 0.85, "clean_N": 36}, {"tier": "small", "subject_seed": 3, "muscle": "ECRL", "threshold": 0.85, "clean_N": 39}, {"tier": "small", "subject_seed": 0, "muscle": "FDSI", "threshold": 0.85, "clean_N": 45}, {"tier": "medium", "subject_seed": 2, "muscle": "ECU", "threshold": 0.85, "clean_N": 47}, {"tier": "medium", "subject_seed": 3, "muscle": "FDSI", "threshold": 0.85, "clean_N": 49}, {"tier": "medium", "subject_seed": 2, "muscle": "FDSI", "threshold": 0.85, "clean_N": 53}, {"tier": "medium", "subject_seed": 3, "muscle": "ECU", "threshold": 0.85, "clean_N": 55}, {"tier": "medium", "subject_seed": 0, "muscle": "EDI", "threshold": 0.85, "clean_N": 56}, {"tier": "medium", "subject_seed": 3, "muscle": "EDI", "threshold": 0.85, "clean_N": 62}, {"tier": "medium", "subject_seed": 0, "muscle": "ECRB", "threshold": 0.85, "clean_N": 71}, {"tier": "medium", "subject_seed": 0, "muscle": "FCU_u", "threshold": 0.85, "clean_N": 74}, {"tier": "large", "subject_seed": 3, "muscle": "FCU_u", "threshold": 0.85, "clean_N": 77}, {"tier": "large", "subject_seed": 2, "muscle": "EDI", "threshold": 0.90, "clean_N": 80}, {"tier": "large", "subject_seed": 1, "muscle": "ECU", "threshold": 0.90, "clean_N": 84}, {"tier": "large", "subject_seed": 4, "muscle": "EDI", "threshold": 0.90, "clean_N": 93}, {"tier": "large", "subject_seed": 2, "muscle": "ECRB", "threshold": 0.90, "clean_N": 96}, {"tier": "large", "subject_seed": 3, "muscle": "FCU_u", "threshold": 0.90, "clean_N": 112}, {"tier": "large", "subject_seed": 1, "muscle": "FCU_u", "threshold": 0.90, "clean_N": 113} ], "conditions": { "SA05-hi": {"angle_profile": "Sinusoid", "af": 0.5, "sin_amplitude_deg": 32.5, "noise_level_db": 25}, "SA05-lo": {"angle_profile": "Sinusoid", "af": 0.5, "sin_amplitude_deg": 32.5, "noise_level_db": 20}, "SA10-hi": {"angle_profile": "Sinusoid", "af": 1.0, "sin_amplitude_deg": 65.0, "noise_level_db": 25}, "SA10-lo": {"angle_profile": "Sinusoid", "af": 1.0, "sin_amplitude_deg": 65.0, "noise_level_db": 20}, "TA05-hi": {"angle_profile": "Triangular", "af": 0.5, "sin_amplitude_deg": 32.5, "noise_level_db": 25}, "TA05-lo": {"angle_profile": "Triangular", "af": 0.5, "sin_amplitude_deg": 32.5, "noise_level_db": 20}, "TA10-hi": {"angle_profile": "Triangular", "af": 1.0, "sin_amplitude_deg": 65.0, "noise_level_db": 25}, "TA10-lo": {"angle_profile": "Triangular", "af": 1.0, "sin_amplitude_deg": 65.0, "noise_level_db": 20} }, "fixed_recording_parameters": { "effort_level_pct": 50, "effort_profile": "Constant", "sin_frequency_hz": 0.3, "duration_s": 10, "sampling_frequency_hz": 2048, "target_angle_deg": 0, "n_electrodes": 320, "electrode_grid": "10 rows × 32 cols", "inter_electrode_distance_mm": 8, "filter": {"type": "Butterworth", "cutoff_hz": 800, "order": 4}, "noise_model": "amplitude-based additive Gaussian, std = std(emg) * 10^(-snr_db/20)" }, "channel_configs": { "ch070": {"n_cols": 7, "n_channels": 70, "description": "7 columns × 10 rows centered on max-RMS column"}, "ch320": {"n_cols": 32, "n_channels": 320, "description": "Full grid, no subsampling"} }, "recording_count": { "combos": 21, "conditions_per_combo": 8, "channel_configs": 2, "total": 336 }, "data_format": "See BENCHMARK_DATA_FORMAT.md — npz with emg (T, M), spike_mu_{i} arrays, metadata scalars", "known_issues_fixed": [ "Initial v2 generation had a bug in NeuroMotion's Triangular angle profile (TargetAngle=0, RampDuration=0 yielded flat 0° signal). Fixed via scripts/patches/neuromotion_triangular_symmetric.patch which replaces the Triangular branch with a symmetric triangle wave using SinAmplitude + SinFrequency (matching Sinusoid's range and period). 168 triangular recordings regenerated." ] }