{"id":"W4311162632","doi":"10.18280/ts.390508","title":"Deep and Statistical Features Classification Model for Electroencephalography Signals","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Inönü Üniversitesi","keywords":"Artificial intelligence; Pattern recognition (psychology); Preprocessor; Computer science; Electroencephalography; Feature extraction; Feature (linguistics); SIGNAL (programming language); Deep learning; Statistical model; Hilbert–Huang transform; Data pre-processing; Psychology; Computer vision","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":[0.0007242427,0.0006139819,0.000453499,0.0006377234,0.000178072,0.0005495842,0.0005780347,0.0006113697,0.0009355614],"category_scores_gemma":[0.001618455,0.0001770238,0.0007018787,0.00055316,0.0002405688,0.0005983265,0.000392701,0.0008842196,0.0002429642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005785708,"about_ca_system_score_gemma":0.0005415641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007192242,"about_ca_topic_score_gemma":0.005653721,"domain_scores_codex":[0.9997559,0.00005014485,0.00002021457,0.00006463507,0.0000641251,0.00004493618],"domain_scores_gemma":[0.9995784,0.0001833423,0.00005143091,0.00003604846,0.0001338654,0.00001694159],"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":[0.0001667222,0.0001446299,0.003798556,0.00005293171,0.00007644667,0.00009654406,0.00007869393,0.7580593,0.009726078,0.005534049,0.001808387,0.2204576],"study_design_scores_gemma":[0.000001384945,0.00001725947,0.0003387424,0.000001660733,0.00000372874,0.000006524027,0.000001894986,0.9984725,0.0003559563,0.0006975468,0.0001004733,0.00000233888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08801343,0.0004868266,0.9088097,0.0003789765,0.00007874007,0.00005494112,0.0002663915,0.0006557844,0.001255238],"genre_scores_gemma":[0.8986987,0.0002909887,0.09647503,0.000115521,0.00007038872,0.0001395709,0.0004985307,0.00003405458,0.003677164],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007192242,"threshold_uncertainty_score":0.01430076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03897486821102948,"score_gpt":0.2841727351003705,"score_spread":0.245197866889341,"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."}}