{"id":"W3136994693","doi":"10.1109/tcds.2021.3065200","title":"A Survey on Neuromarketing Using EEG Signals","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Army Research Office","keywords":"Neuromarketing; Computer science; Electroencephalography; Functional magnetic resonance imaging; Process (computing); Product (mathematics); Artificial intelligence; Human–computer interaction; Data science; Neuroscience; Psychology","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":[0.0002736799,0.0002024162,0.0002171057,0.0001120322,0.0004156356,0.0001970185,0.00007365405,0.00006021586,0.00005501523],"category_scores_gemma":[0.00008190014,0.0001914625,0.00004989078,0.0003146978,0.00006687322,0.0001381752,0.000004103289,0.0001978311,0.00006486118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005148433,"about_ca_system_score_gemma":0.00009144522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009350593,"about_ca_topic_score_gemma":0.00003816193,"domain_scores_codex":[0.9980523,0.0005997199,0.0002794045,0.0005395952,0.000274792,0.0002541886],"domain_scores_gemma":[0.9980049,0.001671252,0.00006630676,0.0000666896,0.00008739479,0.0001034657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004768922,0.0007002735,0.00131091,0.0001601268,0.0001238093,0.0005448046,0.001536599,0.001397342,0.9615335,0.00001307348,0.0002708194,0.03193181],"study_design_scores_gemma":[0.0007616879,0.0001601085,0.005212598,0.0008024842,0.00001776953,0.0003724728,0.0009552061,0.004614567,0.9865673,0.000004737883,0.0001487397,0.0003823459],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9479371,0.0000525954,0.04848021,0.00001783634,0.0009482751,0.0002399827,0.0001795308,0.00006308449,0.002081395],"genre_scores_gemma":[0.998549,0.00003082439,0.00007122099,0.0006494851,0.00002413747,0.000017607,0.00000379372,0.00002139394,0.000632464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05061199,"threshold_uncertainty_score":0.7807615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08177045781237519,"score_gpt":0.2942505910659309,"score_spread":0.2124801332535557,"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."}}