{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004145534,0.000531763,0.0004409888,0.0002380957,0.0001330735,0.0003089651,0.0007418816,0.0008008544,0.001771076],"category_scores_gemma":[0.001933363,0.0003075349,0.0004084366,0.0003706003,0.0003308812,0.0003410296,0.0005848453,0.001304025,0.0004733868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003681621,"about_ca_system_score_gemma":0.0003719121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005497781,"about_ca_topic_score_gemma":0.006370941,"domain_scores_codex":[0.999861,0.00005316235,0.000004979756,0.00003415938,0.00002747691,0.00001908573],"domain_scores_gemma":[0.999523,0.0003487038,0.00002622549,0.00003241805,0.00005250395,0.00001712102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004750647,0.00001617967,0.0003485471,0.00002153344,0.00002279831,0.00004563741,0.00001762574,0.9671019,0.001650502,0.004577441,0.001307683,0.02484262],"study_design_scores_gemma":[0.000001305073,0.00000294419,0.00004168655,9.948916e-7,0.000001494253,0.000007920278,8.022934e-7,0.9985886,0.0002056605,0.001035356,0.0001120142,0.00000120702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01560476,0.0001996808,0.9812294,0.0002183348,0.00006948309,0.00002476648,0.0001750831,0.0007882849,0.001690191],"genre_scores_gemma":[0.8370685,0.0003287845,0.1547607,0.0002512337,0.00009446913,0.0001327803,0.0006397527,0.0001887165,0.006535074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005497781,"threshold_uncertainty_score":0.01093155,"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."}}