{"id":"W2901051931","doi":"10.1002/spe.2668","title":"Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN","year":2018,"lang":"en","type":"article","venue":"Software Practice and Experience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Cloud computing; Electroencephalography; Artificial intelligence; Classifier (UML); Deep learning; Brain–computer interface; Machine learning; Pattern recognition (psychology); Medicine; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006531543,0.0001658803,0.0002058216,0.00008195367,0.000431251,0.00008974035,0.0001866136,0.00006649042,0.000004169799],"category_scores_gemma":[0.002231075,0.0001228172,0.0000220353,0.0003043642,0.0001529392,0.0008306071,0.00007248909,0.0001185768,0.000002286657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005517726,"about_ca_system_score_gemma":0.0001056641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003532946,"about_ca_topic_score_gemma":0.0002190129,"domain_scores_codex":[0.9977956,0.000496039,0.000369849,0.0006476652,0.0004685429,0.0002222691],"domain_scores_gemma":[0.9972337,0.001285924,0.000442768,0.0004026708,0.0005350405,0.00009989843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.006663848,0.006365234,0.01193362,0.001200189,0.0001003953,0.0006772712,0.5353516,0.00005737902,0.2568382,0.001303423,0.001384714,0.1781241],"study_design_scores_gemma":[0.007831083,0.0175452,0.002267409,0.0010269,0.0003051476,0.01021047,0.5756461,0.03108512,0.346561,0.0001240171,0.006366915,0.001030646],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869239,0.0001169801,0.01012594,0.000975891,0.0003391669,0.001371221,0.00001311097,0.0001045259,0.00002921259],"genre_scores_gemma":[0.994445,0.000007636388,0.004568196,0.000500678,0.00014895,0.0002868484,0.000003072615,0.00001678705,0.00002288233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1770934,"threshold_uncertainty_score":0.5008339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08535133327897979,"score_gpt":0.3937019394376899,"score_spread":0.3083506061587101,"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."}}