{"id":"W3134400247","doi":"","title":"A deep learning approach for detecting epileptic spike in magnetoencephalography signals","year":2020,"lang":"en","type":"dissertation","venue":"Summit (Simon Fraser University)","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Magnetoencephalography; Spike (software development); Artificial intelligence; Epilepsy; Computer science; Neuroscience; Psychology; Electroencephalography; Speech recognition; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004002484,0.0007345977,0.0003260983,0.0006216887,0.000204252,0.0003867333,0.0006500553,0.0006531635,0.00107977],"category_scores_gemma":[0.0006447745,0.0001994066,0.0003743327,0.0004767762,0.0002031831,0.0004933251,0.000511381,0.0006113522,0.0003658105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000534017,"about_ca_system_score_gemma":0.0005904661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006819897,"about_ca_topic_score_gemma":0.008624508,"domain_scores_codex":[0.9997881,0.00003193281,0.00001370457,0.00005609917,0.00007800812,0.00003207801],"domain_scores_gemma":[0.9998729,0.00003732059,0.00001665185,0.00001432065,0.00004953691,0.000009177093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001565166,0.0002355976,0.002485761,0.00008373328,0.0001029968,0.0001589044,0.00004279762,0.1807797,0.04216389,0.002167503,0.004891142,0.7667315],"study_design_scores_gemma":[0.00000435948,0.0000480712,0.0007310784,0.000005819798,0.0000125593,0.00003341441,0.000006445794,0.990027,0.00748072,0.0008626764,0.0007818727,0.00000602989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08136325,0.001325112,0.911407,0.0004221503,0.000102951,0.0000859891,0.0004001377,0.002461555,0.002431895],"genre_scores_gemma":[0.717907,0.0008502705,0.2718915,0.0003308605,0.0000894179,0.0001148422,0.001035424,0.0000734626,0.007707207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006819897,"threshold_uncertainty_score":0.01356041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02033742736226726,"score_gpt":0.2323539219346122,"score_spread":0.212016494572345,"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."}}