{"id":"W2766661546","doi":"10.1002/hbm.23849","title":"Multichannel wearable f<scp>NIRS‐EEG</scp>system for long‐term clinical monitoring","year":2017,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Heart Institute; Université de Montréal; Hôpital Notre-Dame; Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal; Centre Hospitalier de l’Université de Montréal","funders":"Canadian Institutes of Health Research; Heart and Stroke Foundation of Canada","keywords":"Electroencephalography; Artifact (error); Computer science; Neuroimaging; Wearable computer; Epilepsy; Artificial intelligence; Neuroscience; Psychology; Embedded system","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001292373,0.0002587347,0.0006223259,0.0001299831,0.001185402,0.000333677,0.0003861414,0.0002084549,0.00000730322],"category_scores_gemma":[0.001654753,0.0002454282,0.0002930967,0.0000397948,0.0002283249,0.00021131,0.0001512969,0.0004756622,0.00005091487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001248931,"about_ca_system_score_gemma":0.00004650395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003211645,"about_ca_topic_score_gemma":0.000002748759,"domain_scores_codex":[0.9978803,0.00005980347,0.0006011009,0.0005547408,0.0002585596,0.0006454391],"domain_scores_gemma":[0.997766,0.0004934246,0.0002800514,0.00100934,0.0001640345,0.0002871578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000554224,0.0004617011,0.7413827,0.00213533,0.0003245879,0.0002635753,0.001136279,0.000001517398,0.2327237,0.003694711,0.01090757,0.006913006],"study_design_scores_gemma":[0.003655312,0.0005782765,0.9666192,0.004178475,0.0001543884,0.00006545272,0.0007287156,0.001970483,0.01445469,0.0007693354,0.006550679,0.0002749698],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9416673,0.0002840169,0.0321691,0.001482094,0.001031642,0.001210383,0.000006335679,0.0009840806,0.0211651],"genre_scores_gemma":[0.974966,0.00002171195,0.01556054,0.0001778343,0.001690446,0.0001065336,0.000008462989,0.00006860687,0.00739981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2252366,"threshold_uncertainty_score":0.9999998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1068355572462914,"score_gpt":0.4218924859280999,"score_spread":0.3150569286818085,"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."}}