{"id":"W4414070121","doi":"10.70594/brain/16.3/12","title":"Electroencephalography (EEG) - Based Neuromarketing: Predicting Favourable and Unfavourable Consumer Reactions Using ML Techniques","year":2025,"lang":"en","type":"article","venue":"BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE","topic":"Color perception and design","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bishop's University","funders":"","keywords":"Neuromarketing; Electroencephalography; Headset; Bayes' theorem; Support vector machine; Unconscious mind; Product (mathematics); Naive Bayes classifier","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003766925,0.0001820292,0.000211899,0.00141751,0.0009227007,0.0003214157,0.0003370219,0.0001321835,0.00009813888],"category_scores_gemma":[0.001630526,0.0001833994,0.00004536921,0.003210625,0.001505592,0.0002665668,0.0001335454,0.0008259271,0.000008078773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005037999,"about_ca_system_score_gemma":0.0001947217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001158239,"about_ca_topic_score_gemma":0.000164779,"domain_scores_codex":[0.9962187,0.001127271,0.0004907494,0.0009116474,0.000393259,0.0008583923],"domain_scores_gemma":[0.9974961,0.001770709,0.0000738808,0.0003353175,0.0001494538,0.0001745979],"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":[0.000483915,0.0005209012,0.03747786,0.00008586644,0.000006858358,0.0001158273,0.0006960116,0.0002177547,0.806528,0.03246941,0.001612691,0.1197849],"study_design_scores_gemma":[0.0006820537,0.002933508,0.2013521,0.001622228,0.00005006499,0.0003824469,0.01244627,0.482351,0.1520787,0.09176787,0.05247892,0.001854834],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9391264,0.0005491761,0.03811911,0.006244891,0.0007043419,0.001397227,0.00001026489,0.0002601554,0.01358845],"genre_scores_gemma":[0.9973718,0.0002037478,0.0005843554,0.001068566,0.00002716684,0.00008993916,8.230272e-7,0.00001393157,0.0006396528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6544493,"threshold_uncertainty_score":0.7478809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2258943588961025,"score_gpt":0.4618756866830366,"score_spread":0.2359813277869342,"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."}}