{"id":"W4247044607","doi":"10.1101/2021.02.18.431803","title":"MEG, myself, and I: individual identification from neurophysiological brain activity","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Neurophysiology; Identifiability; Magnetoencephalography; Functional magnetic resonance imaging; Connectome; Resting state fMRI; Neuroscience; Brain activity and meditation; Psychology; Neuroimaging; Neural correlates of consciousness; Identification (biology); Computer science; Electroencephalography; Artificial intelligence; Cognitive psychology; Functional connectivity; Machine learning; Biology; Cognition","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":[0.001144376,0.0003192626,0.0003057932,0.0008187597,0.0001942096,0.0008380149,0.0002486835,0.0004216422,0.002500381],"category_scores_gemma":[0.008272361,0.0001128078,0.0001593516,0.000590485,0.0005078904,0.0008161733,0.0005658814,0.000419925,0.0005068695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001360236,"about_ca_system_score_gemma":0.0001575901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006632244,"about_ca_topic_score_gemma":0.0007572107,"domain_scores_codex":[0.9996873,0.0001105996,0.00001780218,0.0001161049,0.00004170449,0.00002656996],"domain_scores_gemma":[0.9986544,0.0007191435,0.0002021664,0.0002683184,0.00009664051,0.00005938923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001854357,0.0002057204,0.3395142,0.0005949909,0.0005400655,0.0008963374,0.005206459,0.006660288,0.1043169,0.02476755,0.01498761,0.5004555],"study_design_scores_gemma":[0.00005743119,0.0002769021,0.8030692,0.0001449678,0.0002044526,0.00172721,0.002636342,0.0421712,0.03130268,0.1036116,0.0146757,0.0001223474],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9120562,0.0007526946,0.07844676,0.00101249,0.0001048759,0.0000672054,0.001798027,0.000301953,0.005459609],"genre_scores_gemma":[0.9830105,0.0001972664,0.0151168,0.00009072962,0.00005109639,0.00005587686,0.0005686762,0.00004821584,0.0008608417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002500381,"threshold_uncertainty_score":0.008364618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04418949784252992,"score_gpt":0.2437831322609644,"score_spread":0.1995936344184345,"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."}}