{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005651574,0.0004492769,0.0003586409,0.0002517464,0.0002108629,0.0003719603,0.0007020898,0.0004503643,0.001150965],"category_scores_gemma":[0.001147861,0.0001352296,0.0002914406,0.0002121836,0.0002324139,0.0003471548,0.0003422319,0.0003265092,0.0003797011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006700043,"about_ca_system_score_gemma":0.0004110872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00787354,"about_ca_topic_score_gemma":0.008183899,"domain_scores_codex":[0.9996901,0.00006127075,0.00002884405,0.00005868786,0.00009196339,0.00006916314],"domain_scores_gemma":[0.9996758,0.00008316619,0.0000249845,0.00004832276,0.0001215413,0.0000461498],"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":[0.004861983,0.00156897,0.0803218,0.0008026779,0.0004825794,0.02031269,0.0007629913,0.0938294,0.1760061,0.00262661,0.02747498,0.5909494],"study_design_scores_gemma":[0.0002617009,0.001389515,0.03981292,0.00007003763,0.0001898052,0.002475218,0.00040775,0.8471356,0.1000153,0.0009286088,0.007247022,0.00006650003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9580143,0.0005042733,0.03491046,0.000952652,0.0001635341,0.000209317,0.0003060545,0.001231428,0.003707958],"genre_scores_gemma":[0.9886669,0.000127441,0.009130719,0.0001843623,0.00001651812,0.0000436309,0.0001906188,0.00002578009,0.001614025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00787354,"threshold_uncertainty_score":0.0156554,"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."}}