{"id":"W3172690264","doi":"10.1002/hbm.25535","title":"Data‐driven beamforming technique to attenuate ballistocardiogram artefacts in electroencephalography–functional magnetic resonance imaging without detecting cardiac pulses in electrocardiography recordings","year":2021,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Institut Universitaire de Gériatrie de Montréal; École de Technologie Supérieure; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Concordia University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Concordia University; Savoy Foundation","keywords":"Electroencephalography; Beamforming; Functional magnetic resonance imaging; Artificial intelligence; Neuroimaging; EEG-fMRI; Computer science; Magnetic resonance imaging; Brain activity and meditation; Pattern recognition (psychology); Neuroscience; Psychology; Medicine; Radiology","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":[0.0004878806,0.0007209119,0.0002559256,0.0004242805,0.0001585355,0.0002350937,0.0003174691,0.000501778,0.00129049],"category_scores_gemma":[0.001675426,0.0002054656,0.0004201182,0.0004561034,0.0002689099,0.0003647321,0.0002877509,0.0003715974,0.0004786726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001155235,"about_ca_system_score_gemma":0.0002783089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005586921,"about_ca_topic_score_gemma":0.001178721,"domain_scores_codex":[0.9997759,0.00005336258,0.00002556231,0.00005372543,0.00007035965,0.0000211876],"domain_scores_gemma":[0.9995885,0.0001800515,0.00004736866,0.00004540064,0.0001247316,0.00001382475],"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":[0.0002370634,0.00005516846,0.0005670766,0.0002334493,0.00004413228,0.0001235843,0.0001188795,0.003504291,0.8796379,0.0007248933,0.0003234464,0.1144301],"study_design_scores_gemma":[0.00008889937,0.00113253,0.01856528,0.00007325806,0.0002053543,0.001628448,0.00008822529,0.08506633,0.8801078,0.001952268,0.01099928,0.00009246276],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1031278,0.000849492,0.8941209,0.0001354897,0.0001001429,0.0001473196,0.000135818,0.0004447403,0.0009382712],"genre_scores_gemma":[0.2969809,0.001204185,0.6988652,0.0002187333,0.00006371867,0.0003645326,0.0003503398,0.0001457652,0.001806637],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00129049,"threshold_uncertainty_score":0.004317164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04156637766156131,"score_gpt":0.2742660141226926,"score_spread":0.2326996364611313,"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."}}