{"id":"W2516848833","doi":"10.5220/0006002701300137","title":"Physiology-based Recognition of Facial Micro-expressions using EEG and Identification of the Relevant Sensors by Emotion","year":2016,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Electroencephalography; Computer science; Identification (biology); Emotion recognition; Facial expression; Speech recognition; Artificial intelligence; Pattern recognition (psychology); Psychology; Neuroscience; Biology","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":[0.000138862,0.0003653278,0.0002015503,0.0003882041,0.0001310214,0.000389036,0.0001685119,0.0002937336,0.002627657],"category_scores_gemma":[0.0005968036,0.00009601127,0.0001945867,0.0004838945,0.0001506167,0.000345483,0.0001822743,0.0002694118,0.0007122413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009303266,"about_ca_system_score_gemma":0.0001175996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006237033,"about_ca_topic_score_gemma":0.001324405,"domain_scores_codex":[0.9998986,0.0000195613,0.000005730279,0.00003176329,0.00002894136,0.00001546172],"domain_scores_gemma":[0.9999048,0.00003360241,0.00001536643,0.000007334861,0.00002782233,0.00001117467],"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":[0.0005699968,0.000106675,0.01112634,0.000285271,0.00007673397,0.0001375945,0.000144658,0.000585562,0.8191928,0.0005898319,0.001649889,0.1655346],"study_design_scores_gemma":[0.00006444983,0.0005977294,0.7323532,0.00008180669,0.0001930767,0.00146254,0.0003505957,0.02477225,0.2333262,0.002506188,0.004241248,0.00005072909],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6866027,0.003001112,0.2851806,0.0005051399,0.000536571,0.0006769322,0.003808762,0.001083339,0.01860488],"genre_scores_gemma":[0.943755,0.001518655,0.0496245,0.0002162626,0.0002196655,0.0002793654,0.0006468538,0.00009065457,0.003648978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002627657,"threshold_uncertainty_score":0.008790433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03852535252892533,"score_gpt":0.3025172586562539,"score_spread":0.2639919061273286,"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."}}