{"id":"W1972771823","doi":"10.1002/mrm.20357","title":"Real‐time display of artifact‐free electroencephalography during functional magnetic resonance imaging and magnetic resonance spectroscopy in an animal model of epilepsy","year":2005,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Electroencephalography; Artifact (error); EEG-fMRI; Functional magnetic resonance imaging; Subtraction; Epilepsy; Magnetic resonance imaging; Computer science; Artificial intelligence; Neuroscience; Psychology; Medicine; Radiology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006795576,0.0005187854,0.0009104423,0.0007283039,0.0001023463,0.00003420753,0.0008753416,0.0001278868,0.000274622],"category_scores_gemma":[0.0003977595,0.0004800223,0.00008358556,0.001251021,0.001397433,0.0004793328,0.0002294417,0.0005856247,0.000005749951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000983625,"about_ca_system_score_gemma":0.00009052948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002124986,"about_ca_topic_score_gemma":0.0002910835,"domain_scores_codex":[0.9950727,0.0003043801,0.001387339,0.001294503,0.0009307244,0.001010332],"domain_scores_gemma":[0.9980476,0.0004525966,0.0002954069,0.0009069923,0.0001011652,0.0001962572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00101839,0.000304879,0.09366687,0.00009996738,6.595339e-7,0.0000705478,0.001059142,0.0003821125,0.8617734,0.0005283163,0.0002997204,0.04079602],"study_design_scores_gemma":[0.003834098,0.00273003,0.7499726,0.0009546936,0.00001896963,0.00009689719,0.0001023277,0.1611952,0.07831379,0.001686745,0.0006348846,0.0004596825],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9542297,0.04267828,0.00004991421,0.001279548,0.0000982057,0.0005398651,0.00004287585,0.00005935865,0.001022222],"genre_scores_gemma":[0.990051,0.00415893,0.004675171,0.00028168,0.0001933713,0.00006566025,0.000004227271,0.00005440449,0.0005155692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7834596,"threshold_uncertainty_score":0.9997652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01279706107860017,"score_gpt":0.2527713482347381,"score_spread":0.2399742871561379,"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."}}