{"id":"W2046579124","doi":"10.1109/icassp.2007.366699","title":"A Simple and Fast Algorithm for Automatic Suppression of High-Amplitude Artifacts in EEG Data","year":2007,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Algorithm; Electroencephalography; Blocking (statistics); Distortion (music); Amplitude; Simple (philosophy); SIMPLE algorithm; Matrix (chemical analysis); Transformation matrix; Artifact (error); Artificial intelligence; Computer vision; Bandwidth (computing)","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":[0.001156483,0.001297749,0.0008803437,0.001307527,0.0005959334,0.0007940449,0.001123318,0.001305069,0.00452936],"category_scores_gemma":[0.003067033,0.0005191007,0.0006020078,0.001147065,0.0005622732,0.001048158,0.0009241253,0.001781675,0.003886014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002325926,"about_ca_system_score_gemma":0.0008809243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008091203,"about_ca_topic_score_gemma":0.001625967,"domain_scores_codex":[0.999201,0.0001177425,0.00006951333,0.0001461747,0.0004186865,0.0000469083],"domain_scores_gemma":[0.9988497,0.0004175346,0.0001224156,0.0002203901,0.0003400358,0.00004988814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002228047,0.00008534947,0.0002761725,0.0003084003,0.00009417729,0.0001074987,0.0001235411,0.005694451,0.2164197,0.004343569,0.00414264,0.7681816],"study_design_scores_gemma":[0.0004331771,0.0008250939,0.006668853,0.0001456499,0.0002288499,0.00301872,0.0001279872,0.4645942,0.4165199,0.01345717,0.09369505,0.0002854521],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00114423,0.0001368891,0.9976386,0.00004169824,0.00006230042,0.00005878504,0.00002879332,0.0006943261,0.0001944503],"genre_scores_gemma":[0.01001778,0.0001716525,0.9881424,0.00005280556,0.00005204393,0.0001579459,0.0001265244,0.00009898267,0.001179778],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00452936,"threshold_uncertainty_score":0.01515222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03375466559849929,"score_gpt":0.326698108123645,"score_spread":0.2929434425251457,"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."}}