{"id":"W2119065917","doi":"10.1155/2013/167069","title":"Wavelet-Based Artifact Identification and Separation Technique for EEG Signals during Galvanic Vestibular Stimulation","year":2013,"lang":"en","type":"article","venue":"Computational and Mathematical Methods in Medicine","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Galvanic vestibular stimulation; Artifact (error); Vestibular system; Electroencephalography; Identification (biology); Wavelet; Artificial intelligence; Pattern recognition (psychology); Computer science; Galvanic cell; Stimulation; Speech recognition; Audiology; Neuroscience; Medicine; Psychology; Biology; Materials science","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":[0.0003289817,0.0005199277,0.0003216401,0.0005566085,0.0001719181,0.0002785707,0.0003675325,0.0003614197,0.0008193417],"category_scores_gemma":[0.00106713,0.0001845683,0.0005760676,0.0006204255,0.0002102428,0.0004712939,0.0002574106,0.0004870816,0.000286885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001319186,"about_ca_system_score_gemma":0.0002846484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000603462,"about_ca_topic_score_gemma":0.0007584406,"domain_scores_codex":[0.9998298,0.00002899421,0.00001281489,0.00003297443,0.00008573481,0.000009701504],"domain_scores_gemma":[0.9997383,0.0000995026,0.00004105523,0.00002553664,0.00008560793,0.00001009794],"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":[0.0002948404,0.00007737465,0.001088311,0.0003340578,0.00009746609,0.0001829664,0.0001103987,0.01861026,0.3798519,0.001849337,0.0007362886,0.5967668],"study_design_scores_gemma":[0.00007664087,0.0005267804,0.008922987,0.00004166622,0.0002002022,0.001026801,0.00005272833,0.7127785,0.2659754,0.001845386,0.008487847,0.00006508739],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02117791,0.0002769791,0.9780096,0.00003850588,0.00003270727,0.00002358097,0.00002621744,0.0001897485,0.0002247548],"genre_scores_gemma":[0.2421644,0.0009717917,0.7549552,0.00005158977,0.00008690341,0.0001193743,0.0001815401,0.00009635802,0.001372952],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0008193417,"threshold_uncertainty_score":0.002741039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07320012194238586,"score_gpt":0.4285328719443654,"score_spread":0.3553327500019796,"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."}}