{"id":"W3208187939","doi":"10.3390/s21217258","title":"Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Fraser Health; National Research Council Canada; Surrey Memorial Hospital; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation","keywords":"Pooling; Channel (broadcasting); Event (particle physics); Computer science; SIGNAL (programming language); Real-time computing; Algorithm; Telecommunications; Artificial intelligence; Physics","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":[0.001409564,0.0008364214,0.0005132087,0.0008585476,0.0002128068,0.0006758702,0.0007515767,0.0004177011,0.002391707],"category_scores_gemma":[0.005157294,0.0003152502,0.0008822969,0.0008732832,0.0003757376,0.001707804,0.001220835,0.0004574299,0.0005347912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003048899,"about_ca_system_score_gemma":0.000320705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005012044,"about_ca_topic_score_gemma":0.0008127852,"domain_scores_codex":[0.9995356,0.0001223672,0.00003423311,0.0001211698,0.0001389732,0.00004785151],"domain_scores_gemma":[0.9987782,0.0007069869,0.000118549,0.0001754142,0.0001805007,0.00004029736],"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":[0.0009177707,0.000263209,0.003668532,0.0004878971,0.0005205037,0.0004532197,0.0002409955,0.05698636,0.3362586,0.003614194,0.002545383,0.5940433],"study_design_scores_gemma":[0.0001489657,0.001236904,0.03652921,0.0000930392,0.0009424798,0.001614098,0.000140004,0.6997797,0.2290896,0.01781802,0.0124,0.0002079291],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1042063,0.000703026,0.8910383,0.0001654304,0.00008062474,0.00009458693,0.0001828588,0.00105618,0.002472599],"genre_scores_gemma":[0.6358644,0.0006277707,0.3610383,0.0002053601,0.0001506714,0.0002548883,0.0004691138,0.0002613795,0.001128169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002391707,"threshold_uncertainty_score":0.008001029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01409640307506447,"score_gpt":0.2549578668104209,"score_spread":0.2408614637353564,"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."}}