{"id":"W4399884112","doi":"10.3390/computers13070158","title":"Personalized Classifier Selection for EEG-Based BCIs","year":2024,"lang":"en","type":"article","venue":"Computers","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Mitacs; Holland Bloorview Kids Rehabilitation Hospital Foundation","keywords":"Classifier (UML); Brain–computer interface; Electroencephalography; Computer science; Artificial intelligence; Pattern recognition (psychology); Quadratic classifier; Machine learning; Speech recognition; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001077378,0.0001386246,0.0001266672,0.0001300436,0.0001246316,0.0002735695,0.0002244874,0.00005396942,0.00005043087],"category_scores_gemma":[0.00004114353,0.0001212204,0.0001472801,0.0002616966,0.00007691994,0.00013913,0.00003284866,0.0001201147,0.00007400911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006022884,"about_ca_system_score_gemma":0.0000619604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003565846,"about_ca_topic_score_gemma":0.000001584316,"domain_scores_codex":[0.9989263,0.00005641734,0.0001418779,0.0004696847,0.0001582859,0.0002474276],"domain_scores_gemma":[0.9991481,0.000629217,0.00002609883,0.0001064534,0.00002595336,0.00006417045],"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":[0.0002073455,0.0001757701,0.0001824925,0.0005081714,0.00005860966,0.00004570874,0.001561246,0.004735212,0.4457926,0.03882068,0.3835946,0.1243176],"study_design_scores_gemma":[0.0003303587,0.000133226,0.00003765894,0.00007324444,0.000009028206,0.00001570236,0.000007915185,0.6774495,0.09466314,0.0003819287,0.2267555,0.0001428305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09377398,0.0001232149,0.8976591,0.002777579,0.003817891,0.0003494781,0.00002005735,0.0006696549,0.0008090131],"genre_scores_gemma":[0.9885654,0.000002900105,0.006480633,0.002880563,0.0002773168,0.00003257951,0.00000486165,0.00002643135,0.001729274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8947915,"threshold_uncertainty_score":0.4943225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04869461512978038,"score_gpt":0.3035730751520018,"score_spread":0.2548784600222214,"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."}}