{"id":"W4292317441","doi":"10.1038/s41598-022-18257-x","title":"CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Spectrogram; Pattern recognition (psychology); Computer science; Artificial intelligence; Electroencephalography; Image (mathematics); Speech recognition; Fusion; Psychology; Neuroscience","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002296592,0.0001829531,0.0002625046,0.00109259,0.001098767,0.0002290981,0.0001067176,0.00005171096,0.01879772],"category_scores_gemma":[0.00007995191,0.0001941986,0.0003666269,0.002809059,0.0001994738,0.0001449554,0.00008226925,0.0002214955,0.000197141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002293041,"about_ca_system_score_gemma":0.0001360417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003010894,"about_ca_topic_score_gemma":0.0001397296,"domain_scores_codex":[0.9968069,0.0004987751,0.0005288221,0.001078847,0.00064753,0.0004390833],"domain_scores_gemma":[0.9982425,0.00006744791,0.0005531818,0.0007172346,0.0002741148,0.0001455321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0009246507,0.009296224,0.04715716,0.0001456788,0.004313518,0.01516419,0.01572466,0.01027216,0.4070635,0.0001059398,0.08364753,0.4061848],"study_design_scores_gemma":[0.01363665,0.004241544,0.2615155,0.0003452332,0.01690273,0.007961974,0.03816633,0.08831076,0.195632,0.1749873,0.1877396,0.0105605],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9685863,0.00005143256,0.00733143,0.00009980401,0.01005091,0.0006143553,0.00006956454,0.0002093149,0.0129869],"genre_scores_gemma":[0.9934741,7.21702e-7,0.00143911,0.0001212892,0.00005632815,0.0001157255,0.001418296,0.0000275849,0.003346812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3956243,"threshold_uncertainty_score":0.9820992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0300700528527454,"score_gpt":0.3152413275068377,"score_spread":0.2851712746540923,"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."}}