{"id":"W2007808090","doi":"10.1155/2014/261347","title":"Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis","year":2014,"lang":"en","type":"article","venue":"Journal of Applied Mathematics","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Electroencephalography; Computer science; Hilbert–Huang transform; Artifact (error); Canonical correlation; Multiset; Signal processing; Pattern recognition (psychology); Channel (broadcasting); Artificial intelligence; Independent component analysis; SIGNAL (programming language); Speech recognition; Mathematics; Computer vision; Neuroscience; Digital signal processing; 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":[0.0005476949,0.0006777485,0.0005194333,0.0006804165,0.0002208383,0.0004430315,0.0004018161,0.0004226898,0.0009117033],"category_scores_gemma":[0.001771964,0.0001892874,0.0007891294,0.000714997,0.0003087328,0.0007006713,0.0005497155,0.0005450064,0.0002416986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001505938,"about_ca_system_score_gemma":0.0004934422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001462731,"about_ca_topic_score_gemma":0.002336061,"domain_scores_codex":[0.9996893,0.000077356,0.0000195487,0.0000576441,0.00013599,0.00002021268],"domain_scores_gemma":[0.9995093,0.0001719382,0.00006212264,0.00006268659,0.0001677644,0.00002615953],"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":[0.000183005,0.0001270916,0.003153108,0.0003860829,0.0002322087,0.0005373022,0.0002613276,0.1910097,0.1434316,0.01680969,0.002558963,0.6413099],"study_design_scores_gemma":[0.000007076569,0.0000684783,0.001559942,0.00001585143,0.00003110215,0.0002565735,0.00002484764,0.9825019,0.01243897,0.001489345,0.001583452,0.00002261427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02313323,0.0003805022,0.9755003,0.00006249169,0.00005110487,0.00002492855,0.00002467311,0.0001486256,0.0006741384],"genre_scores_gemma":[0.2637631,0.0007500051,0.7336519,0.00006797469,0.00007576784,0.00008864725,0.0001449054,0.00009415047,0.001363584],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001462731,"threshold_uncertainty_score":0.00304997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04072240956604792,"score_gpt":0.3218380539725418,"score_spread":0.2811156444064939,"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."}}