{"id":"W2151930786","doi":"10.5539/mas.v8n1p164","title":"Extraction of Inherent Frequency Components of Multiway EEG Data Using Two-Stage Neural Canonical Correlation Analysis","year":2014,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Canonical correlation; Computer science; Electroencephalography; Artificial neural network; Pattern recognition (psychology); Nonlinear system; Correlation; Set (abstract data type); Data set; Process (computing); Artificial intelligence; Function (biology); Data mining; Mathematics","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":[0.0007028171,0.0009560848,0.0006057529,0.001330472,0.0004482681,0.0006957995,0.0005241193,0.0004064479,0.001624829],"category_scores_gemma":[0.002154294,0.0002918421,0.0009934603,0.001586811,0.0002998338,0.0008683308,0.0006464376,0.0006362655,0.0005360518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002652761,"about_ca_system_score_gemma":0.0009748399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004284305,"about_ca_topic_score_gemma":0.007033672,"domain_scores_codex":[0.9993768,0.0001087549,0.00004506735,0.0001909612,0.0002184199,0.00006001314],"domain_scores_gemma":[0.9994442,0.0001505285,0.0000460526,0.00007049142,0.0002637313,0.00002485344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001592941,0.0001355036,0.005197995,0.0002507173,0.0001618745,0.0003121142,0.0002756931,0.054648,0.06322786,0.009643751,0.003071093,0.8629161],"study_design_scores_gemma":[0.00001243863,0.00011332,0.008232472,0.00002490611,0.00006507563,0.0003091257,0.00009188545,0.9554031,0.0257451,0.00439395,0.005551174,0.00005748203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01287406,0.0001364094,0.9858339,0.00004006322,0.00003901471,0.00006280866,0.00006203869,0.0003788633,0.0005727498],"genre_scores_gemma":[0.1701202,0.0003964297,0.826894,0.00003450216,0.00004406712,0.0001882072,0.0004756263,0.0001287278,0.001718201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004284305,"threshold_uncertainty_score":0.008518696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0802915553029301,"score_gpt":0.3334591325793002,"score_spread":0.2531675772763701,"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."}}