{"id":"W2681204121","doi":"10.71781/18537","title":"Décoder la localisation de l'attention visuelle spatiale grâce au signal EEG","year":2016,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Université de Montréal; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Electroencephalography; Psychology; Physics; Computer science; Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.000789875,0.0009021616,0.0004451155,0.0006975287,0.0002230215,0.00118255,0.0004813984,0.0005614822,0.007777904],"category_scores_gemma":[0.005056554,0.0002708843,0.0004473679,0.0005324165,0.0002904252,0.0009263765,0.0006074182,0.0006325039,0.003046375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003373009,"about_ca_system_score_gemma":0.0008378054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005422092,"about_ca_topic_score_gemma":0.004893767,"domain_scores_codex":[0.9995657,0.00007447249,0.00002794706,0.0001406333,0.0001367836,0.00005448061],"domain_scores_gemma":[0.9987343,0.0005115926,0.00008757702,0.00013081,0.0004938162,0.00004185901],"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":[0.001079892,0.00006669425,0.007530268,0.0004874362,0.0001345064,0.0002448786,0.0004848157,0.01563103,0.1702629,0.003171409,0.004211427,0.7966949],"study_design_scores_gemma":[0.0001409696,0.00112974,0.1017759,0.0004084685,0.0003466902,0.001157831,0.0007155246,0.5975512,0.2522891,0.013386,0.03095769,0.0001409788],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3080741,0.001897165,0.6661444,0.0005708138,0.0006964777,0.0002824715,0.00265419,0.004813884,0.01486643],"genre_scores_gemma":[0.807676,0.0009929672,0.1726871,0.0002044444,0.0001702615,0.0002292527,0.00197648,0.0003628993,0.01570069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007777904,"threshold_uncertainty_score":0.02601969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007805401061686053,"score_gpt":0.1960341144279539,"score_spread":0.1882287133662679,"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."}}