{"id":"W4387587709","doi":"10.1109/tnsre.2023.3324148","title":"Improving Generalized Zero-Shot Learning SSVEP Classification Performance From Data-Efficient Perspective","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Classifier (UML); Machine learning; Pattern recognition (psychology); Data mining","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":[0.001289514,0.0005848792,0.0009432719,0.0006591229,0.0002563836,0.0007015831,0.001079283,0.0006704924,0.001300669],"category_scores_gemma":[0.005004304,0.0002480236,0.0004888197,0.0006704314,0.0005231769,0.001473645,0.001148735,0.000930746,0.0004796285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003928967,"about_ca_system_score_gemma":0.001010091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002844119,"about_ca_topic_score_gemma":0.003776159,"domain_scores_codex":[0.9991782,0.0001726349,0.00006401975,0.0001587301,0.0003464339,0.00008006928],"domain_scores_gemma":[0.9984784,0.0007568381,0.00007386889,0.0001868155,0.0004593153,0.00004471748],"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":[0.0003050502,0.0002949029,0.001802668,0.0001956806,0.00006180955,0.0001177227,0.0001491563,0.07652099,0.05017049,0.005008523,0.00214964,0.8632233],"study_design_scores_gemma":[0.00001586849,0.0001035488,0.0009609675,0.000008083025,0.00001937049,0.00006778576,0.00002213479,0.9829634,0.01323595,0.00194121,0.0006486685,0.00001303059],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08005974,0.0005358893,0.916505,0.0001598402,0.00003601153,0.00007698675,0.00006942979,0.001144764,0.001412408],"genre_scores_gemma":[0.7076965,0.0004857218,0.288628,0.0002274804,0.00005763811,0.0001559233,0.000523032,0.0001096599,0.002116093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002844119,"threshold_uncertainty_score":0.006819725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04191208266012766,"score_gpt":0.2703037975916301,"score_spread":0.2283917149315024,"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."}}