{"id":"W4388102349","doi":"10.18280/ts.400517","title":"High-Dimension EEG Biometric Authentication Leveraging Sub-Band Cube-Code Representation","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eskişehir Osmangazi Üniversitesi","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Data cube; Biometrics; Code (set theory); Singular value decomposition; Wavelet; Dimension (graph theory); Feature extraction; Principal component analysis; Dimensionality reduction; Computer vision; Data mining; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0005531104,0.0006977877,0.000575937,0.0007565972,0.0002564929,0.0007687338,0.0004965753,0.0004185044,0.002253013],"category_scores_gemma":[0.001987818,0.000170227,0.0005472748,0.00089828,0.000430521,0.001107785,0.001185634,0.000688323,0.001113405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003085445,"about_ca_system_score_gemma":0.0008090945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001735857,"about_ca_topic_score_gemma":0.001714048,"domain_scores_codex":[0.99958,0.00007950275,0.000019436,0.00007494736,0.0001963835,0.00004982035],"domain_scores_gemma":[0.999341,0.0001470926,0.00007562357,0.0001566562,0.0002373947,0.00004216675],"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":[0.0006876577,0.0001619658,0.001883427,0.0001419838,0.00009602485,0.0003477103,0.0002359864,0.1278255,0.1163834,0.01686821,0.006089246,0.729279],"study_design_scores_gemma":[0.00001516728,0.0001703348,0.001962659,0.00001975769,0.00002181122,0.0003158277,0.00004612079,0.9535749,0.0311526,0.008470183,0.004208185,0.00004243141],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05932729,0.0003139054,0.9366274,0.0001746421,0.00007438487,0.00008417575,0.0002338345,0.00144166,0.001722799],"genre_scores_gemma":[0.552187,0.0005758372,0.4421791,0.0001437159,0.00005651587,0.0002040789,0.0009528964,0.0001134867,0.003587369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002253013,"threshold_uncertainty_score":0.007537127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05968641429937818,"score_gpt":0.2978798690140809,"score_spread":0.2381934547147027,"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."}}