{"id":"W1973469542","doi":"10.1109/iembs.2010.5626501","title":"Application of Empirical Mode Decomposition and Teager energy operator to EEG signals for mental task classification","year":2010,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hilbert–Huang transform; Electroencephalography; Linear discriminant analysis; Energy operator; Pattern recognition (psychology); Artificial intelligence; Computer science; Feature extraction; Energy (signal processing); Detrended fluctuation analysis; SIGNAL (programming language); Feature (linguistics); Mode (computer interface); Speech recognition; Mathematics; Statistics; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001539105,0.0007638198,0.0005318717,0.001153419,0.0002465768,0.0005890392,0.00047342,0.0005197756,0.00170648],"category_scores_gemma":[0.004837272,0.0001830068,0.0006555442,0.0008412884,0.0004076332,0.00114813,0.0004621047,0.0007741008,0.0004596736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001688642,"about_ca_system_score_gemma":0.0003492827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005330144,"about_ca_topic_score_gemma":0.0007061535,"domain_scores_codex":[0.9994349,0.0001846244,0.0000380542,0.0001159653,0.0001875404,0.00003898402],"domain_scores_gemma":[0.9986185,0.0008578963,0.0001161688,0.0001356663,0.0002352542,0.00003655256],"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":[0.0003602528,0.0001543718,0.002450206,0.0001918483,0.00008495408,0.0001483561,0.0003280998,0.0176508,0.09468341,0.004976986,0.0009850444,0.8779858],"study_design_scores_gemma":[0.00004580146,0.0004534241,0.01406767,0.00004268054,0.00006242432,0.0006555236,0.000149931,0.9213004,0.049835,0.008095347,0.005197853,0.00009378206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02783087,0.0001374398,0.9711756,0.00004468815,0.00002859106,0.00004810785,0.00003969869,0.000302104,0.0003928571],"genre_scores_gemma":[0.1845547,0.0002681524,0.8136104,0.00003144006,0.00004710261,0.0001528002,0.0001401426,0.00009267357,0.00110254],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00170648,"threshold_uncertainty_score":0.00813967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03020377647085811,"score_gpt":0.375612905068913,"score_spread":0.3454091285980549,"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."}}