{"id":"W4313467508","doi":"10.3389/fnrgo.2022.1045653","title":"Merging Brain-Computer Interface P300 speller datasets: Perspectives and pitfalls","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neuroergonomics","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale; Alberta Innovates","keywords":"Computer science; Alphanumeric; Brain–computer interface; Interface (matter); Process (computing); Information retrieval; Field (mathematics); Artificial intelligence; Machine learning; Data mining; Electroencephalography","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":[0.01162945,0.001343208,0.001195169,0.006248012,0.001178995,0.003814203,0.003413058,0.001548299,0.001900187],"category_scores_gemma":[0.04207437,0.0004693374,0.001408591,0.008657104,0.001151116,0.005446391,0.004034545,0.00164773,0.002146313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000776054,"about_ca_system_score_gemma":0.00138545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002129672,"about_ca_topic_score_gemma":0.001780398,"domain_scores_codex":[0.9863039,0.003132484,0.002690845,0.002222405,0.005139304,0.0005111777],"domain_scores_gemma":[0.973303,0.007539259,0.001719616,0.009919894,0.007028297,0.0004899577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002166573,0.0007923581,0.0534956,0.003857884,0.0009451518,0.002073092,0.001513313,0.01182854,0.04108864,0.01144025,0.07865833,0.7921404],"study_design_scores_gemma":[0.0006729267,0.001737445,0.2597605,0.002176858,0.0009821954,0.008706049,0.005042806,0.09480333,0.1692754,0.06092575,0.3951721,0.0007447064],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4538094,0.01659162,0.385142,0.01253052,0.003209249,0.002120159,0.07146662,0.04083135,0.01429903],"genre_scores_gemma":[0.4736322,0.003880466,0.3509014,0.001254977,0.0008620843,0.001576753,0.1623231,0.003056625,0.002512348],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01162945,"threshold_uncertainty_score":0.06150311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0125408296434966,"score_gpt":0.2429104163256487,"score_spread":0.2303695866821521,"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."}}