{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005497681,0.0005147056,0.0002872704,0.0008606063,0.000080089,0.0006782493,0.000161137,0.0004130473,0.001561127],"category_scores_gemma":[0.002750357,0.0001015876,0.0002428565,0.0006941966,0.0001420006,0.0004472361,0.0002828605,0.0003096086,0.0005088237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001209083,"about_ca_system_score_gemma":0.00009502711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003998196,"about_ca_topic_score_gemma":0.0007358519,"domain_scores_codex":[0.9997543,0.0000867823,0.00001988848,0.00005160043,0.00006311639,0.00002429657],"domain_scores_gemma":[0.9992892,0.0004652253,0.0001088133,0.00002852077,0.00008225031,0.00002609928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001894337,0.000736464,0.1818983,0.0006750753,0.0004407654,0.0003809951,0.0005301092,0.01085875,0.1177679,0.00113071,0.002127823,0.6815588],"study_design_scores_gemma":[0.0001054588,0.001297937,0.7975253,0.0001098245,0.0002221271,0.001344699,0.0005917966,0.1626926,0.02968891,0.004042844,0.002302278,0.00007625919],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8854107,0.0008682317,0.1062027,0.0003143151,0.0000682938,0.0001918047,0.001069669,0.0003645921,0.005509706],"genre_scores_gemma":[0.9747105,0.0005727887,0.02287366,0.00007560764,0.00007077723,0.0001011524,0.0004753764,0.00002135277,0.001098783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001561127,"threshold_uncertainty_score":0.005222499,"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."}}