{"id":"W2796668973","doi":"10.1145/3180661","title":"Have We Met Before? Using Consumer-Grade Brain-Computer Interfaces to Detect Unaware Facial Recognition","year":2018,"lang":"en","type":"article","venue":"Computers in entertainment","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; DOD Counterdrug Technology Development Program Office; Office of Science","keywords":"Brain–computer interface; Computer science; Interface (matter); Facial recognition system; Work (physics); Pattern recognition (psychology); Artificial intelligence; Human–computer interaction; Speech recognition; Psychology; Electroencephalography; Neuroscience; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003336836,0.0005153125,0.000493477,0.0005605813,0.0002451381,0.0002977366,0.0009126983,0.0001393218,0.0000831875],"category_scores_gemma":[0.00008008473,0.0004870391,0.0001574623,0.0004048747,0.0003023372,0.0003084881,0.0007918729,0.0003310147,0.0001901191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003875943,"about_ca_system_score_gemma":0.00003912023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006518346,"about_ca_topic_score_gemma":0.0001319497,"domain_scores_codex":[0.9963045,0.0003972995,0.0007245353,0.001215014,0.0005072709,0.0008513917],"domain_scores_gemma":[0.998584,0.0003890421,0.0002006666,0.000498004,0.00006892109,0.0002594187],"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":[0.0004259318,0.0005846246,0.003436484,0.0001562466,0.0001159374,0.0002854,0.02353443,0.002198492,0.1471211,0.0001075375,0.009471718,0.8125621],"study_design_scores_gemma":[0.00320321,0.003951588,0.0008942506,0.00181232,0.00004196928,0.0002807681,0.0006139149,0.1271946,0.8242301,0.002757035,0.03348288,0.001537325],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8609576,0.000032804,0.1333387,0.001266589,0.003265419,0.000763875,0.00004266501,0.0001727823,0.0001595612],"genre_scores_gemma":[0.9577299,0.00001018476,0.03578487,0.005706388,0.0006240023,0.00003415811,0.00000785323,0.00005083248,0.00005175639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8110248,"threshold_uncertainty_score":0.9997581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05001658552051525,"score_gpt":0.3068066773374287,"score_spread":0.2567900918169135,"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."}}