{"id":"W3159367133","doi":"10.1101/2021.04.28.440961","title":"Holo-Hilbert Spectral-based Noise Removal Method for EEG High-Frequency Bands","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; Hotchkiss Brain Institute; University of Calgary","funders":"","keywords":"Hilbert–Huang transform; Electroencephalography; Computer science; Noise (video); Radio spectrum; Spectral density; Frequency band; Artificial intelligence; Speech recognition; SIGNAL (programming language); Pattern recognition (psychology); Psychology; White noise; Telecommunications; Neuroscience","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002243525,0.0008807728,0.001023753,0.0005955059,0.0002722476,0.001282023,0.002582761,0.0009583416,0.00004003177],"category_scores_gemma":[0.0005015057,0.0009870874,0.0005044995,0.001146447,0.0001012608,0.0005161329,0.0008500888,0.001134426,0.00002409284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004520321,"about_ca_system_score_gemma":0.002253581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001548059,"about_ca_topic_score_gemma":0.000008959312,"domain_scores_codex":[0.9945955,0.0005672781,0.0009489497,0.002266906,0.0007340016,0.000887314],"domain_scores_gemma":[0.9937577,0.0003483984,0.0007163762,0.003690773,0.001092022,0.0003947499],"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":[0.00004109554,0.0004822554,0.0004986219,0.0005795272,0.0002541999,0.00031042,0.00006745481,0.0007802213,0.904336,0.09113558,0.001481651,0.00003292046],"study_design_scores_gemma":[0.001156227,0.000170365,0.01083374,0.00046017,0.0001333627,1.962628e-7,0.000001700023,0.02666756,0.9519978,0.0002555847,0.006448559,0.001874801],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05485317,0.0005359069,0.9369791,0.002487808,0.0015048,0.001402302,0.0001174875,0.0020668,0.00005263201],"genre_scores_gemma":[0.4153089,0.00003763681,0.5826235,0.001243784,0.0002353872,0.0004323825,0.000001472067,0.0001064296,0.0000105374],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3604557,"threshold_uncertainty_score":0.9997547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01633733098358602,"score_gpt":0.2588780836116166,"score_spread":0.2425407526280306,"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."}}