{"id":"W2163092406","doi":"10.3389/fnagi.2014.00055","title":"The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis","year":2014,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Electroencephalography; Artifact (error); Computer science; Independent component analysis; Wavelet; Blind signal separation; Artificial intelligence; Pattern recognition (psychology); Algorithm; Channel (broadcasting); Psychology; Neuroscience","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004415476,0.001271697,0.0009246609,0.001414348,0.0003934968,0.0008482008,0.0005297263,0.0008446886,0.000487534],"category_scores_gemma":[0.0198238,0.0002425326,0.0006865698,0.0007509277,0.0004091645,0.0009231076,0.0007585894,0.0004268192,0.0003055056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002499257,"about_ca_system_score_gemma":0.000385306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001572271,"about_ca_topic_score_gemma":0.001574977,"domain_scores_codex":[0.9960485,0.001971144,0.0004305693,0.0005483634,0.0008596834,0.0001418162],"domain_scores_gemma":[0.9872113,0.008917638,0.001102455,0.0008479163,0.001773826,0.0001467661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004663593,0.001084801,0.01828309,0.0006008703,0.0006370101,0.0003528213,0.0003553196,0.03452175,0.06663051,0.0004035653,0.001131894,0.8713347],"study_design_scores_gemma":[0.0005689212,0.00893925,0.1709173,0.0001542936,0.001467757,0.003216783,0.0005631896,0.6586826,0.1494014,0.001421903,0.00438937,0.0002772564],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8741557,0.004362778,0.1173029,0.0002272093,0.0001910737,0.0002746847,0.0001777731,0.00158639,0.001721367],"genre_scores_gemma":[0.8549049,0.001182886,0.1423236,0.0001623092,0.0001025378,0.0001166205,0.0004331323,0.0001171793,0.0006568552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004415476,"threshold_uncertainty_score":0.02335161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01292777227494897,"score_gpt":0.2586238909300071,"score_spread":0.2456961186550581,"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."}}