{"id":"W3169534597","doi":"10.3389/fnins.2021.611653","title":"Cross-Subject EEG Emotion Recognition With Self-Organized Graph Neural Network","year":2021,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":110,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Guangzhou Municipal Science and Technology Project; South China University of Technology; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; York University","keywords":"Electroencephalography; Computer science; Emotion recognition; Graph; Speech recognition; Pattern recognition (psychology); Cognition; Subject (documents); Artificial intelligence; Artificial neural network; Psychology; Neuroscience; Theoretical computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.000510754,0.0007414434,0.0005340763,0.0006554301,0.0001996356,0.0004672846,0.0007153674,0.0005101249,0.0007914199],"category_scores_gemma":[0.001561618,0.0002294912,0.000762259,0.0005215754,0.0003282026,0.0007942743,0.0005295103,0.0005811809,0.0002568232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004554739,"about_ca_system_score_gemma":0.0002292666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003615331,"about_ca_topic_score_gemma":0.00437056,"domain_scores_codex":[0.9996965,0.00007212171,0.00001233932,0.0001393469,0.00004358633,0.00003599796],"domain_scores_gemma":[0.9995582,0.0001698644,0.00005748086,0.00006978452,0.0001228597,0.00002192824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007154227,0.0003687924,0.009165274,0.0001936854,0.0004547802,0.0002478595,0.000284729,0.5346942,0.03299261,0.003011739,0.003921947,0.413949],"study_design_scores_gemma":[0.000004983351,0.00005022573,0.001674874,0.000002716242,0.00002201607,0.00002536408,0.00001213487,0.9949544,0.001601635,0.001481817,0.0001633536,0.000006395443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2775046,0.0007198493,0.7170044,0.0002683984,0.0001611196,0.0001179024,0.000344902,0.00195334,0.001925535],"genre_scores_gemma":[0.9321881,0.0002172679,0.06526533,0.0001075343,0.00003475527,0.00007779353,0.0006304435,0.00008025385,0.001398469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003615331,"threshold_uncertainty_score":0.007188559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01968314890840981,"score_gpt":0.2488515623002956,"score_spread":0.2291684133918858,"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."}}