{"id":"W1765315678","doi":"10.1016/j.neucom.2015.09.085","title":"Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization","year":2015,"lang":"en","type":"article","venue":"Neurocomputing","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":104,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Electroencephalography; Artificial intelligence; Support vector machine; Computer science; Pattern recognition (psychology); Valence (chemistry); Neuromarketing; Classifier (UML); Arousal; Emotion classification; Affective computing; Graph; Discriminative model; Machine learning; Cognitive psychology; Speech recognition; Psychology; Social psychology; Neuroscience","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":[0.001064012,0.0006069948,0.0008240101,0.001599915,0.0003089101,0.0009013733,0.0005638239,0.0007778825,0.001503842],"category_scores_gemma":[0.004352062,0.0001782103,0.0008032011,0.001330438,0.0003081312,0.0009334259,0.0005364681,0.000614911,0.0006175629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003505719,"about_ca_system_score_gemma":0.0004655366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001993769,"about_ca_topic_score_gemma":0.002127204,"domain_scores_codex":[0.9992939,0.0002343925,0.00005366548,0.0001669772,0.0001682898,0.00008279066],"domain_scores_gemma":[0.9992142,0.0003788269,0.00007713077,0.00006367984,0.0002320366,0.00003410903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001278189,0.0003856697,0.004221783,0.0003046555,0.0002848606,0.0002345639,0.0001469805,0.07013497,0.06782452,0.008080232,0.006360108,0.8407435],"study_design_scores_gemma":[0.00003601153,0.0002095493,0.00720241,0.00002334015,0.0001125113,0.0001390488,0.00005149301,0.9697298,0.01213681,0.009184259,0.001131429,0.00004320543],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1161018,0.001565078,0.8781804,0.0004413273,0.0002213585,0.000163778,0.0004540874,0.0006617327,0.002210452],"genre_scores_gemma":[0.900539,0.0003540554,0.09680846,0.00008738291,0.0001496789,0.00009310613,0.0004586355,0.00006231916,0.001447454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001993769,"threshold_uncertainty_score":0.005627155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.022638832269103,"score_gpt":0.276914659345186,"score_spread":0.254275827076083,"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."}}