{"id":"W2035986876","doi":"10.1109/embc.2013.6609663","title":"Application of a variation of empirical mode decomposition and teager energy operator to EEG signals for mental task classification","year":2013,"lang":"en","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hilbert–Huang transform; Energy operator; Pattern recognition (psychology); Artificial intelligence; Electroencephalography; Computer science; Classifier (UML); Feature extraction; Energy (signal processing); Speech recognition; Principal component analysis; Time–frequency analysis; Mathematics; Statistics; Computer vision; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000749605,0.00007898586,0.0001271895,0.00009311808,0.00001650982,0.0000125803,0.00005743558,0.00006242021,0.00002213654],"category_scores_gemma":[0.00001381622,0.00007537953,0.00002404985,0.00008995774,0.000008989232,0.0001397538,0.00001354495,0.00002076641,0.000001912344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003993393,"about_ca_system_score_gemma":0.000004826952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002367791,"about_ca_topic_score_gemma":0.00001454716,"domain_scores_codex":[0.9994445,0.00001514822,0.0002609296,0.000125327,0.00008128589,0.00007280974],"domain_scores_gemma":[0.9996419,0.00006187514,0.00004319384,0.0001227078,0.00008858922,0.00004172131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005703014,0.00005685772,0.0005620247,0.0000378449,0.00001309464,4.555387e-9,0.0001559717,0.0008910052,0.9809227,0.00239276,0.003191544,0.01177052],"study_design_scores_gemma":[0.0001220424,0.00006231124,0.01836571,0.000009414473,0.000008995081,2.925657e-7,0.000009607297,0.6165901,0.3637418,0.0006276935,0.0003901557,0.00007192226],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2982792,0.00001196503,0.7006237,0.0002128574,0.00001133197,0.000532892,0.00002197966,0.00008879137,0.000217299],"genre_scores_gemma":[0.9562443,0.00001229539,0.04260166,0.0000880272,0.0000143021,0.0009466078,0.00007016247,0.00001464053,0.000007943843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.658022,"threshold_uncertainty_score":0.3073888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01087966963927483,"score_gpt":0.3492382732885271,"score_spread":0.3383586036492523,"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."}}