{"id":"W2122014760","doi":"10.1109/iembs.2002.1134407","title":"A wavelet based de-noising technique for ocular artifact correction of the electroencephalogram","year":2003,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artifact (error); Electroencephalography; Thresholding; Wavelet; Artificial intelligence; Computer science; Wavelet transform; SIGNAL (programming language); Pattern recognition (psychology); Computer vision; Stationary wavelet transform; Speech recognition; Noise reduction; Discrete wavelet transform; Image (mathematics); 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":[0.0002781514,0.0003257127,0.0002785324,0.0003361492,0.0001342978,0.0002705764,0.0003191014,0.0004282188,0.0007822042],"category_scores_gemma":[0.0009333952,0.0001347113,0.000352295,0.000412416,0.0002011054,0.0003973504,0.0002129927,0.0004863073,0.0003903703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008666725,"about_ca_system_score_gemma":0.0001704133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002284842,"about_ca_topic_score_gemma":0.0003857636,"domain_scores_codex":[0.999851,0.00002638851,0.000009781466,0.00002486951,0.00007806848,0.000009907995],"domain_scores_gemma":[0.9997699,0.00006916206,0.00003124233,0.00003805214,0.000081916,0.000009788063],"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":[0.0001759567,0.00004905621,0.0004508373,0.0002157305,0.00004841224,0.0002542335,0.0000702911,0.005765431,0.5368948,0.003955338,0.0008835935,0.4512363],"study_design_scores_gemma":[0.000067483,0.0007944471,0.007457438,0.00007144012,0.000183172,0.003901788,0.00007551816,0.4113221,0.534864,0.00305886,0.03812598,0.00007776934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03658697,0.0009468187,0.9607844,0.0001158044,0.0001312454,0.00003176041,0.00003761201,0.0002153205,0.001150045],"genre_scores_gemma":[0.1823788,0.002242044,0.8100311,0.00007097212,0.0001493196,0.00005522456,0.0001669061,0.00008577396,0.004819798],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0007822042,"threshold_uncertainty_score":0.002616704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01945763057347787,"score_gpt":0.2675816808095704,"score_spread":0.2481240502360925,"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."}}