{"artifact_id":"L1-507","layer":"L1","title":"Joint MEG-EEG Source Imaging","domain":"Medical Imaging","sub_domain":"Multi-modal bioelectromagnetic neural source localization (multi-physics joint inverse)","physics_fingerprint":{"L_DAG":10.0,"title":"Joint MEG-EEG Source Imaging","domain":"Medical Imaging","carrier":"neural_current","primitives":["L.neural_source","L.cortical_mesh_constraint","L.head_volume_conductor","L.maxwell_magnetostatic","L.poisson_electrostatic","L.megsensor_detection","L.eegsensor_detection","int.spatial","int.temporal"],"sub_domain":"Multi-modal bioelectromagnetic neural source localization (multi-physics joint inverse)","noise_model":"gaussian","problem_class":"linear_inverse_underdetermined","solution_space":"5D_neural_source_spatiotemporal","difficulty_delta":5,"integration_axis":"spatial_temporal","sensing_mechanism":"joint_magnetic_and_electric_with_volume_conductor"},"observable_profile":{"metric":"PSNR_dB","regime":"Existence of recovered neural source distribution J(r, t) is guaranteed within the cortical-mesh constraint and the declared Omega bounds. Uniqueness is fundamentally conditional — the joint inverse problem is underdetermined (~15000 source dipoles with constrained orientation vs ~400 sensors typical), so unique solutions require regularization (minimum-norm, sLORETA, beamformer, sparse priors, dynamical / temporal constraints). Stability is moderately conditioned (kappa_eff ~ 100 after L2 regularization) — head_segmentation_error dominates source-localization bias; conductivity_uncertainty contributes scaling factor; sensor_position_error contributes a few-millimeter localization shift; source_orientation_assumption (free vs cortically-constrained vs surface-normal) contributes prior bias. Joint Hadamard well-posedness for the coupled MEG+EEG forward (with regularization) is established by Mosher et al. 1992, Hamalainen-Ilmoniemi 1994, Pascual-Marqui 2002 (sLORETA), Sharon et al. 2007, Henson et al. 2009, and Huang et al. 2014.","secondary":"spatial_localization_error_mm"},"size_tiers":{"center_spec":{"omega":{"SNR_dB":20,"N_dipoles":8000,"T_samples":1000,"N_EEG_channels":64,"N_MEG_channels":306,"head_layer_count":3,"sampling_rate_Hz":1000,"sensor_position_error":0.0,"head_segmentation_error":0.0,"conductivity_uncertainty":0.0,"reference_electrode_drift":0.0,"source_orientation_assumption":"cortically_constrained","magnetic_artifact_contamination":0.0},"input_format":"joint_meg_eeg_time_series","problem_class":"meg_eeg_joint_source_imaging","forward_operator":"meg_eeg_joint_forward","epsilon_fn_center":"20.0"},"omega_bounds":{"SNR_dB":[0.0,35.0],"N_dipoles":[1000,30000],"T_samples":[100,100000],"N_EEG_channels":[0,256],"N_MEG_channels":[0,306],"head_layer_count":[3,7],"sampling_rate_Hz":[256,5000],"sensor_position_error":[0.0,0.2],"head_segmentation_error":[0.0,0.3],"conductivity_uncertainty":[0.0,0.5],"reference_electrode_drift":[0.0,0.3],"magnetic_artifact_contamination":[0.0,0.4]},"epsilon_bounds":{"psnr_db":[3.0,38.0]},"allowed_problem_classes":["meg_eeg_joint_source_imaging","meg_eeg_dipole_localization","meg_eeg_distributed_inverse","meg_eeg_dynamic_causal_modeling"],"allowed_omega_dimensions":["N_dipoles","N_MEG_channels","N_EEG_channels","T_samples","sampling_rate_Hz","head_layer_count","SNR_dB","head_segmentation_error","conductivity_uncertainty","sensor_position_error","source_orientation_assumption","reference_electrode_drift","magnetic_artifact_contamination"],"allowed_forward_operators":["meg_eeg_joint_forward","meg_only_forward","eeg_only_forward","meg_eeg_with_co_estimated_conductivity"]},"hardness_fn":{"type":"epsilon_fn","delta":5,"kappa":1000,"metric":"PSNR_dB"},"initiator_dataset":[{"name":"primary","weight":1.0,"ipfs_cid":null,"license_hash":null}],"status":"testnet","staked_pwm":0.0,"chain_hash":"0xc5b5823cf02645ff781e015fc071cd01cb138c2ec39adf26564ee8f0716fc417","chain_tx_hash":"0xdebc47053da2a66ee606952ad7d1f1115aa01f0e65e5d73470794d4ce0f74263","chain_block":41553359}