{"id":"W4403837099","doi":"10.1016/j.dib.2024.111065","title":"Auditory evoked potential electroencephalography-biometric dataset","year":2024,"lang":"en","type":"article","venue":"Data in Brief","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Electroencephalography; Biometrics; Speech recognition; Computer science; Pattern recognition (psychology); Artificial intelligence; Neuroscience; Psychology","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":[0.0005285053,0.001081094,0.001040552,0.001046051,0.000267413,0.0005123704,0.0007908839,0.0008643929,0.007978814],"category_scores_gemma":[0.001877008,0.0001170985,0.0006124077,0.001021781,0.0002042596,0.000256149,0.0008124707,0.0005265829,0.007312425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002707059,"about_ca_system_score_gemma":0.0004876016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001818472,"about_ca_topic_score_gemma":0.003316376,"domain_scores_codex":[0.9994292,0.00009829504,0.000119992,0.0001295826,0.0001556554,0.00006740126],"domain_scores_gemma":[0.9991721,0.0001372859,0.00009444348,0.0002194292,0.0003101745,0.00006665549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.007535127,0.003225071,0.1508561,0.00625863,0.001595247,0.006102191,0.0004760672,0.009528993,0.0737734,0.001551714,0.3741493,0.3649482],"study_design_scores_gemma":[0.001074703,0.002673727,0.7107136,0.0003303762,0.0005685469,0.01188112,0.0005129073,0.01058004,0.02525556,0.002328796,0.2338026,0.0002779795],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2539848,0.001807612,0.01326031,0.0003964504,0.0003858024,0.002092474,0.7161862,0.001795459,0.01009094],"genre_scores_gemma":[0.1848771,0.0006561944,0.007588798,0.0002286782,0.0001513488,0.003809897,0.7970628,0.0001034911,0.005521677],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.007978814,"threshold_uncertainty_score":0.02669185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03539467463478876,"score_gpt":0.3073078220755569,"score_spread":0.2719131474407681,"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."}}