{"id":"W4412431194","doi":"10.1016/j.engappai.2025.111688","title":"Detection of error in static and dynamic visual stimulation via electroencephalogram and eye-tracking systems","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Eye tracking; Artificial intelligence; Computer vision; Electroencephalography; Eye movement; Neuroscience","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001404969,0.00008417125,0.0001337303,0.0002765034,0.00003733033,0.00002881935,0.00009499679,0.00003941572,5.43394e-7],"category_scores_gemma":[0.00009914552,0.00009191596,0.00001414678,0.0005206068,0.00007293624,0.00009637202,0.00002983307,0.00009780464,4.912321e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002632493,"about_ca_system_score_gemma":0.00001040805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006233289,"about_ca_topic_score_gemma":0.00002081241,"domain_scores_codex":[0.9992043,0.00002320011,0.0003557791,0.0002180827,0.00008117355,0.0001174493],"domain_scores_gemma":[0.9994857,0.0002633357,0.00008284691,0.0001064364,0.000041232,0.00002047102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007756762,0.00004126058,0.0001641597,0.0001186884,0.000002761896,1.602439e-7,0.0001911641,0.1459729,0.7856728,0.002608527,7.411678e-8,0.06521971],"study_design_scores_gemma":[0.00001122518,0.00003863081,0.001224097,0.00005126548,0.000003852139,0.000001182996,0.00006886644,0.6347535,0.3631916,0.0006065441,0.000006780097,0.00004243061],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4922268,0.0001089904,0.5074031,0.00001538628,0.00003481554,0.0001859198,0.000001175318,0.00002075463,0.000003113899],"genre_scores_gemma":[0.9985433,0.00002728867,0.00135088,0.000004315073,0.000007079617,0.0000553975,8.26409e-7,0.000005859926,0.000005002505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5063166,"threshold_uncertainty_score":0.3748224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01762271898808566,"score_gpt":0.3244039565407086,"score_spread":0.306781237552623,"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."}}