{"id":"W1494834104","doi":"10.1007/s10548-015-0437-3","title":"MEG–EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy","year":2015,"lang":"en","type":"article","venue":"Brain Topography","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; Concordia University; Université de Montréal; École de Technologie Supérieure; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Epilepsy Society; Savoy Foundation; American Epilepsy Society","keywords":"Electroencephalography; Ictal; Computer science; Statistical parametric mapping; Magnetoencephalography; Parametric statistics; Artificial intelligence; Pattern recognition (psychology); Sensor fusion; Magnetic resonance imaging; Psychology; Neuroscience; Mathematics; Medicine; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00295211,0.0006482818,0.0006589218,0.001495186,0.0001503638,0.001197555,0.0005416222,0.001245894,0.0009313599],"category_scores_gemma":[0.008467329,0.0002913465,0.0004047569,0.001067537,0.001639958,0.001848056,0.001072976,0.0007191433,0.0002139591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004552362,"about_ca_system_score_gemma":0.0003658979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000267182,"about_ca_topic_score_gemma":0.0001783958,"domain_scores_codex":[0.9990402,0.0005138139,0.00004821178,0.00009548975,0.0002722871,0.00002995163],"domain_scores_gemma":[0.9984028,0.00115882,0.0001512423,0.0001116374,0.0001408477,0.00003476175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000685613,0.000164507,0.008710804,0.001344128,0.0003185596,0.0007814434,0.0003359389,0.1187898,0.05040254,0.06451526,0.002105079,0.7518463],"study_design_scores_gemma":[0.00009677982,0.001335843,0.01918855,0.0003982272,0.0001872774,0.003270616,0.0003151381,0.7730479,0.03455047,0.1537231,0.01373953,0.0001467199],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05384093,0.04261811,0.8942917,0.004480009,0.000171831,0.00009118889,0.0001197752,0.0002901661,0.004096183],"genre_scores_gemma":[0.7883954,0.01690398,0.1927378,0.0005211444,0.0005663414,0.0001197678,0.0000879656,0.00005976406,0.0006079078],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00295211,"threshold_uncertainty_score":0.01561248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01645519689208595,"score_gpt":0.283072527555801,"score_spread":0.2666173306637151,"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."}}