{"id":"W2968168094","doi":"10.5539/ijps.v11n4p1","title":"Modeling Nonlinear Transfer Functions from Speech Envelopes to Encephalography with Neural Networks","year":2019,"lang":"en","type":"article","venue":"International Journal of Psychological Studies","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft; Nvidia","keywords":"Electroencephalography; Artificial neural network; Stimulus (psychology); Speech recognition; Nonlinear system; Neurophysiology; Transfer function; Computer science; Linear model; Contrast (vision); Pattern recognition (psychology); Artificial intelligence; Psychology; Neuroscience; Machine learning; Physics; Cognitive psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002967878,0.0006042047,0.0001529727,0.0002271131,0.00009552315,0.0003240251,0.0003666111,0.0005032643,0.0006628111],"category_scores_gemma":[0.001111523,0.0002750338,0.0004273288,0.0002079846,0.0002591815,0.0004340632,0.0003217821,0.0005558748,0.0001617126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003942616,"about_ca_system_score_gemma":0.0002781672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003950203,"about_ca_topic_score_gemma":0.003860343,"domain_scores_codex":[0.9999354,0.00002136547,0.000003714884,0.00001729658,0.00001213679,0.00001013908],"domain_scores_gemma":[0.9998191,0.000119403,0.00002119924,0.00001284395,0.00002268615,0.000004763782],"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":[0.00002908856,0.00002058426,0.0004201846,0.00002625833,0.00002403307,0.00003828928,0.00002517604,0.9783137,0.007790147,0.000846326,0.0000778062,0.01238835],"study_design_scores_gemma":[6.988576e-7,0.000005727995,0.0001766541,0.000001121228,0.000001881096,0.000004131527,0.000001366496,0.998675,0.0006770264,0.0004145393,0.0000403118,0.000001453245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.187111,0.000332088,0.8099502,0.0001444818,0.00003931958,0.00004499289,0.0001089078,0.0004721872,0.001796894],"genre_scores_gemma":[0.944577,0.0003237887,0.05244787,0.00003644201,0.00001759915,0.00009073222,0.0001175031,0.00004246285,0.002346649],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003950203,"threshold_uncertainty_score":0.007854402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07229133102781335,"score_gpt":0.3501292083159895,"score_spread":0.2778378772881762,"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."}}