{"id":"W2522619451","doi":"10.1109/tbme.2016.2603463","title":"Muscle Activity Map Reconstruction from High Density Surface EMG Signals With Missing Channels Using Image Inpainting and Surface Reconstruction Methods","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Isfahan; Universitat Politècnica de Catalunya; Aalborg Universitet; McGill University","keywords":"Inpainting; Iterative reconstruction; Mean squared error; Artificial intelligence; Surface reconstruction; Mathematics; Outlier; Poisson's equation; Computer vision; Image quality; Pattern recognition (psychology); Computer science; Surface (topology); Image (mathematics); Geometry; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003235176,0.0003460871,0.0003875839,0.000235787,0.0002512535,0.00007096254,0.00007856012,0.0001984409,0.00006094253],"category_scores_gemma":[0.00001783992,0.0002885205,0.00008574205,0.0004885257,0.0001737638,0.0005536593,0.000003307695,0.0003648957,0.000001811429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001945554,"about_ca_system_score_gemma":0.00002074918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002082314,"about_ca_topic_score_gemma":0.00001050563,"domain_scores_codex":[0.9984935,0.00008586264,0.0003146279,0.0004253388,0.0002393532,0.0004413015],"domain_scores_gemma":[0.9989999,0.0004394593,0.00007380508,0.0002062662,0.00006579934,0.0002147798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001897733,0.00001726912,0.00001583799,0.00003765505,0.0001293728,0.000002197571,0.00005783255,0.02104809,0.5987221,9.203208e-7,0.000003311519,0.3799465],"study_design_scores_gemma":[0.0008322725,0.00007995823,0.001201091,0.0005462791,0.00008624276,0.00006488023,0.00008317646,0.2549329,0.7414576,0.00006152601,0.0001182628,0.0005358325],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4870276,0.00004071059,0.5116749,0.0001313392,0.0006916723,0.00009367872,0.00001504059,0.0003197257,0.000005421245],"genre_scores_gemma":[0.8634022,0.0001293658,0.1362906,0.00001009519,0.00009946975,0.00000631584,0.000001290736,0.00005444989,0.000006215222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3794106,"threshold_uncertainty_score":0.9999567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01279835384615479,"score_gpt":0.2316967743417724,"score_spread":0.2188984204956176,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}