{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008056733,0.00007657746,0.0000932176,0.00005753025,0.00007428002,0.00004332693,0.0001267657,0.00005133454,0.00001347182],"category_scores_gemma":[0.00002975859,0.00006282565,0.00002538014,0.00008218939,0.00004222882,0.0001252799,0.0000385649,0.00004339076,0.000005179204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009684946,"about_ca_system_score_gemma":0.00001151246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000198117,"about_ca_topic_score_gemma":0.00002083446,"domain_scores_codex":[0.9992917,0.00002728822,0.0001793221,0.0002963992,0.0001025778,0.0001026831],"domain_scores_gemma":[0.9995633,0.0001370426,0.00004948841,0.0001395205,0.00004111325,0.00006950479],"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":[0.00002658453,0.00007291145,0.0001583528,0.000005001235,0.000001459451,4.646737e-8,0.0001481728,0.00002324878,0.9821459,0.005615062,0.001566547,0.01023671],"study_design_scores_gemma":[0.0001557595,0.0001024052,0.0008917716,0.000002991587,0.000003023129,0.000003943643,0.00001699338,0.1195745,0.8729084,0.0003761105,0.005891563,0.00007261465],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7216098,0.000001987516,0.2763466,0.001356908,0.00008199635,0.0002357785,0.00002448934,0.00002910535,0.0003133045],"genre_scores_gemma":[0.9914013,0.000001626309,0.00671909,0.001580144,0.00004434121,0.00009479661,0.00001037527,0.000007909881,0.0001403762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2697915,"threshold_uncertainty_score":0.2561956,"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."}}