{"id":"W4416961126","doi":"10.1109/embc58623.2025.11254459","title":"Using Generative Adversarial Networks to eliminate RF and gradient interference in neurophysiology signals recorded simultaneously with functional MRI","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Neurophysiology; Artificial neural network; Pattern recognition (psychology); Artifact (error); SIGNAL (programming language); Masking (illustration); Residual; Process (computing); Signal processing; Modality (human–computer interaction)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001263652,0.001165585,0.0006155308,0.0004049603,0.0002335635,0.0005146862,0.0008351373,0.0009375433,0.0008146989],"category_scores_gemma":[0.002754746,0.0003903098,0.0006820207,0.0003031491,0.000907842,0.0005843841,0.001004701,0.001530054,0.0002736556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000544721,"about_ca_system_score_gemma":0.0004939121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002950463,"about_ca_topic_score_gemma":0.003307774,"domain_scores_codex":[0.999594,0.0001602571,0.00001361895,0.00008936219,0.0001011187,0.00004155641],"domain_scores_gemma":[0.9986411,0.0009877757,0.0001341165,0.00009482718,0.0001054696,0.00003665039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005535383,0.00002363577,0.0003969712,0.00002587414,0.00004337593,0.00007043778,0.00002602957,0.9707714,0.003331984,0.002980607,0.0005719928,0.0217023],"study_design_scores_gemma":[0.000001959887,0.00001050954,0.00006901295,0.000002414126,0.000004585804,0.00001988657,0.000001785233,0.9978702,0.0007463815,0.001135306,0.0001346475,0.000003250632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02458379,0.0002697877,0.9731405,0.0002270751,0.00003843619,0.00002675881,0.0000377027,0.0003633605,0.001312664],"genre_scores_gemma":[0.8471982,0.0003924906,0.1468141,0.0003978533,0.00008266814,0.00009918153,0.0002575585,0.0001457186,0.004612073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002950463,"threshold_uncertainty_score":0.006682873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02834279669012476,"score_gpt":0.3132247055385737,"score_spread":0.2848819088484489,"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."}}