{"id":"W2138178674","doi":"10.1109/tbme.2003.816076","title":"Temporally constrained ica: an application to artifact rejection in electromagnetic brain signal analysis","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":257,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Montreal Neurological Institute and Hospital; Aston University","keywords":"Independent component analysis; A priori and a posteriori; Artifact (error); SIGNAL (programming language); Computer science; Signal processing; Waveform; Pattern recognition (psychology); Blind signal separation; Artificial intelligence; Magnetoencephalography; Electroencephalography; Set (abstract data type); Speech recognition; Channel (broadcasting); Digital signal processing; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056334,0.0001588126,0.0001982364,0.001198964,0.00005755599,0.00007427017,0.0002598503,0.0001286298,0.00003338814],"category_scores_gemma":[0.00001934991,0.0001711062,0.00008601939,0.003171507,0.00002710604,0.0002733339,0.00000123667,0.0002886801,0.00001685149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001170479,"about_ca_system_score_gemma":0.00007632311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004742657,"about_ca_topic_score_gemma":0.00005157434,"domain_scores_codex":[0.9985402,0.0001006763,0.0003436054,0.0004256791,0.0003174638,0.0002723889],"domain_scores_gemma":[0.9992296,0.0001058294,0.00003913671,0.00035355,0.00003790774,0.0002339441],"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":[0.00002089879,0.0007661568,0.00001481487,0.00001590927,0.00013114,0.00001383969,0.001131628,0.3890631,0.5304307,0.008865098,0.0000502349,0.06949644],"study_design_scores_gemma":[0.0003755992,0.0007010367,0.0006533371,0.00001545093,0.00003110508,0.00001743222,0.00002495735,0.9217834,0.07428326,0.0002635205,0.001503292,0.0003475897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02839153,0.000003092079,0.9697667,0.0009374984,0.00006876018,0.0002636751,0.000002623357,0.0004859786,0.00008016248],"genre_scores_gemma":[0.9258238,0.00000203398,0.0736857,0.0003142347,0.00001111578,0.0001211402,0.000004899637,0.00001163799,0.00002544508],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8974323,"threshold_uncertainty_score":0.6977507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007024912417257371,"score_gpt":0.2401210362216599,"score_spread":0.2330961238044025,"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."}}