{"id":"W4398187707","doi":"10.1109/jbhi.2024.3403878","title":"PSEENet: A Pseudo-Siamese Neural Network Incorporating Electroencephalography and Electrooculography Characteristics for Heterogeneous Sleep Staging","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Electrooculography; Electroencephalography; Computer science; Modalities; Artificial intelligence; Classifier (UML); Artificial neural network; Pattern recognition (psychology); Sleep Stages; Modality (human–computer interaction); Eye movement; Polysomnography; Neuroscience; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001052039,0.0002036164,0.0004300079,0.0003959049,0.0002731642,0.000290607,0.0002007807,0.00008730868,0.000001888429],"category_scores_gemma":[0.00005749945,0.0001472826,0.0001235267,0.0004883137,0.0002653977,0.000362216,0.00004183115,0.0004991093,4.558113e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002797545,"about_ca_system_score_gemma":0.0001383904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002546731,"about_ca_topic_score_gemma":0.000001204168,"domain_scores_codex":[0.9975457,0.00007083538,0.001316084,0.0001467195,0.0003572064,0.0005634151],"domain_scores_gemma":[0.9984447,0.000345287,0.0006405608,0.00008369423,0.00006715296,0.0004185445],"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":[0.000385426,0.0002482213,0.005168159,0.006104713,0.0002686658,0.0002248199,0.008976668,0.0004027045,0.01227276,0.00282423,0.01066159,0.9524621],"study_design_scores_gemma":[0.001480854,0.009018435,0.0009827341,0.001460604,0.00008916738,0.006170814,0.0002752443,0.9484302,0.002256317,0.007559821,0.0216998,0.0005759932],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8801301,0.002315098,0.111322,0.004387379,0.001406372,0.0003235993,0.00004028053,0.00006300215,0.00001209003],"genre_scores_gemma":[0.9832506,0.00141593,0.009748094,0.004710213,0.0008467381,0.000005730585,0.000003536554,0.00001638279,0.000002828771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9518861,"threshold_uncertainty_score":0.6006009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02461376845536419,"score_gpt":0.3015318926372931,"score_spread":0.276918124181929,"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."}}