{"id":"W2742829803","doi":"10.1109/icac.2017.41","title":"Deep Learning with Edge Computing for Localization of Epileptogenicity Using Multimodal rs-fMRI and EEG Big Data","year":2017,"lang":"en","type":"article","venue":"","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Electroencephalography; Epilepsy; Context (archaeology); Ictal; Deep learning; Artificial intelligence; Electrocorticography; Functional magnetic resonance imaging; Edge computing; Neurostimulation; Neuroscience; Psychology; Enhanced Data Rates for GSM Evolution","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":[0.0003091879,0.0006159411,0.0004063881,0.0003818546,0.0002004998,0.0004596935,0.0006301692,0.0004854116,0.0007268573],"category_scores_gemma":[0.001185337,0.0002039805,0.0004396147,0.000436944,0.0003299916,0.0005857158,0.0006738983,0.0006173171,0.0001916951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003456209,"about_ca_system_score_gemma":0.0003724237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003751609,"about_ca_topic_score_gemma":0.005076994,"domain_scores_codex":[0.9998716,0.00003356222,0.000007196005,0.00003281034,0.00003191821,0.00002287789],"domain_scores_gemma":[0.9997992,0.0001080849,0.00002427182,0.00001925494,0.0000328069,0.00001639529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000273106,0.0001466,0.002197412,0.00007631883,0.000074986,0.0003047104,0.00005950442,0.7418694,0.01065039,0.006497366,0.002251602,0.2355986],"study_design_scores_gemma":[0.000001609935,0.00001257394,0.0001386688,0.00000156399,0.000002966527,0.0000107073,0.000002927161,0.9973241,0.0007669867,0.001597917,0.000137596,0.000002390351],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06366945,0.0006859131,0.9323986,0.0003819702,0.00005003808,0.00004137588,0.0001584316,0.0008872677,0.00172707],"genre_scores_gemma":[0.8527573,0.0004748743,0.1445121,0.0001858544,0.0000523464,0.00007357152,0.0003017153,0.00006173379,0.001580415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003751609,"threshold_uncertainty_score":0.007459521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1462194536879587,"score_gpt":0.3262109196008898,"score_spread":0.1799914659129311,"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."}}