{"id":"W4383301701","doi":"10.1007/s10548-023-00986-5","title":"Generative Adversarial Network (GAN) for Simulating Electroencephalography","year":2023,"lang":"en","type":"article","venue":"Brain Topography","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"University of British Columbia","keywords":"Electroencephalography; Computer science; Artificial intelligence; Generative grammar; Neuroimaging; Pattern recognition (psychology); Generative model; Brain activity and meditation; Replicate; Machine learning; Psychology; Neuroscience; Mathematics","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":[0.0004130849,0.0002565652,0.0002591138,0.0003818391,0.0005187551,0.0001506038,0.0004454952,0.0001000777,0.00002489165],"category_scores_gemma":[0.0003809554,0.0002381589,0.0004039932,0.002470244,0.0001749917,0.0002104251,0.00007797383,0.0001874592,0.00002612781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001040949,"about_ca_system_score_gemma":0.0000277222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005912348,"about_ca_topic_score_gemma":0.000010817,"domain_scores_codex":[0.997667,0.0001862757,0.0003165609,0.0007071837,0.0002753548,0.000847627],"domain_scores_gemma":[0.9975224,0.001899749,0.0001279782,0.0002823349,0.00005160895,0.0001159025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005008826,0.0002286265,0.009121264,0.0001128622,0.0002159642,0.00008693249,0.003759522,0.08144761,0.4786135,0.06533118,0.3193677,0.04121391],"study_design_scores_gemma":[0.005344974,0.00257951,0.006329986,0.0001759407,0.0000891626,0.00003559893,0.0005108474,0.2132263,0.2888196,0.1663755,0.3142658,0.002246672],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9242568,0.0002359482,0.05509881,0.006272174,0.004229372,0.002118495,0.0001149817,0.002136614,0.005536779],"genre_scores_gemma":[0.9901101,0.00002331529,0.003282186,0.004625149,0.001461079,0.0001138368,0.00002432295,0.00004209765,0.0003179022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1897939,"threshold_uncertainty_score":0.971184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02951179102154989,"score_gpt":0.2914932588406995,"score_spread":0.2619814678191496,"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."}}