{"id":"W3084261808","doi":"10.1088/1741-2552/abb5be","title":"EEG data augmentation: towards class imbalance problem in sleep staging tasks","year":2020,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; China Postdoctoral Science Foundation","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Sleep (system call); Segmentation; Electroencephalography; Sleep Stages; Class (philosophy); Pattern recognition (psychology); Machine learning; Polysomnography; 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":[0.002020485,0.001026734,0.0005560338,0.0005406087,0.0003450472,0.0006555766,0.0007483545,0.0005953599,0.0009004342],"category_scores_gemma":[0.005534716,0.0002354167,0.0006725637,0.0005000024,0.0005274412,0.001033347,0.001033258,0.001164781,0.0003369803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000363871,"about_ca_system_score_gemma":0.0004493191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001098501,"about_ca_topic_score_gemma":0.001709658,"domain_scores_codex":[0.9993446,0.0002330756,0.00004110445,0.0002026165,0.0001220437,0.0000565102],"domain_scores_gemma":[0.9982554,0.0007956444,0.0003025976,0.0003478167,0.0002211661,0.00007730754],"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":[0.0009836777,0.0005169276,0.03247394,0.0003777017,0.0002903187,0.0002245501,0.0006294369,0.1081259,0.05480035,0.002905432,0.009771807,0.7889],"study_design_scores_gemma":[0.00005230424,0.0005015858,0.03403394,0.0000965861,0.0001201392,0.0003273894,0.000220069,0.9161035,0.03500657,0.007245319,0.006241886,0.00005059975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4339453,0.002360567,0.5545459,0.00133709,0.0004298457,0.0003186383,0.0009865265,0.002584536,0.003491648],"genre_scores_gemma":[0.9196569,0.0003959374,0.07601979,0.0002417927,0.0002299132,0.0001867627,0.001132144,0.0001405969,0.001996159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002020485,"threshold_uncertainty_score":0.01068544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0568486807920523,"score_gpt":0.2884237282564034,"score_spread":0.2315750474643511,"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."}}