{"id":"W2921583615","doi":"","title":"Steps towards sensitizing EEG feature identification in paediatric brain signals for use in BCIs","year":2018,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Electroencephalography; Brain–computer interface; Identification (biology); Feature (linguistics); Speech recognition; Computer science; Pattern recognition (psychology); Artificial intelligence; Neuroscience; Psychology; Biology","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":[0.002082274,0.001307378,0.0006941728,0.0009055755,0.0005090558,0.002226807,0.001107359,0.001644276,0.009824103],"category_scores_gemma":[0.0103359,0.000575164,0.0006988225,0.0005755353,0.0006143826,0.001622432,0.001489976,0.002132775,0.004887926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003679154,"about_ca_system_score_gemma":0.001529475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001847403,"about_ca_topic_score_gemma":0.002905809,"domain_scores_codex":[0.9989716,0.0003517909,0.0001018218,0.0001598,0.0003360947,0.0000790042],"domain_scores_gemma":[0.9963502,0.001382778,0.0002334015,0.0004275005,0.001444514,0.0001615799],"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":[0.0004080828,0.0002991788,0.005500716,0.00102853,0.00009081536,0.0005914986,0.0004810235,0.004379305,0.4314249,0.006049521,0.004093612,0.5456528],"study_design_scores_gemma":[0.00009448647,0.001866289,0.02592028,0.001277802,0.0003862027,0.004786543,0.0009109108,0.07733202,0.8060552,0.01401827,0.06715563,0.0001964853],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02077747,0.001833702,0.9682226,0.001120536,0.0001933475,0.0004268155,0.0002091921,0.001938349,0.005278069],"genre_scores_gemma":[0.2416857,0.003408407,0.7447682,0.001257457,0.0001599085,0.0005370872,0.0004544418,0.0004677299,0.007261043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009824103,"threshold_uncertainty_score":0.03286487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04958586478207638,"score_gpt":0.3061532915373887,"score_spread":0.2565674267553123,"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."}}