{"id":"W2966612705","doi":"10.7490/f1000research.1115920.1","title":"Deep Convolutional Architecture with discriminative feature visualization for analysis of EEG data","year":2018,"lang":"en","type":"article","venue":"Faculty of 1000 Research Ltd","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Open peer review; Discriminative model; Plant biology; Convolutional neural network; Feature (linguistics); Computer science; Architecture; Electroencephalography; Neuroscience; Visualization; Artificial intelligence; Pattern recognition (psychology); Computational biology; Psychology; Biology; Geography","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":[0.00075678,0.0001173242,0.0002687876,0.0004737748,0.0001931481,0.00004305605,0.000903017,0.00006717174,0.0001065995],"category_scores_gemma":[0.001060961,0.00007993576,0.00006968754,0.00145356,0.001139247,0.0001832819,0.0003466151,0.0001858283,0.000005431815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003438814,"about_ca_system_score_gemma":0.0001229272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008337112,"about_ca_topic_score_gemma":0.0005080311,"domain_scores_codex":[0.9976776,0.0002909711,0.0001924588,0.0005398141,0.000984373,0.0003147786],"domain_scores_gemma":[0.9974144,0.0007714141,0.0001291562,0.0005228742,0.001087403,0.00007472588],"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":[0.003061592,0.001357067,0.005806296,0.0006445271,0.001679551,0.00001238078,0.01668555,0.005051835,0.8073583,0.02237719,0.1142099,0.02175584],"study_design_scores_gemma":[0.001128449,0.002131189,0.02067924,0.0001844786,0.0002538874,0.000007415876,0.001021603,0.1594418,0.7884126,0.0009750124,0.02546606,0.0002982792],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6095306,0.000110171,0.3702171,0.006256669,0.0001108917,0.001260902,0.009841229,0.00006179871,0.002610616],"genre_scores_gemma":[0.9929742,0.00000446901,0.003228192,0.00005570757,0.00006205978,0.00001391783,0.0009955515,0.00001209786,0.002653754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3834436,"threshold_uncertainty_score":0.4197604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1130972691530765,"score_gpt":0.4304779669438746,"score_spread":0.3173806977907981,"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."}}