{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002598534,0.0002971522,0.0003503047,0.0003900505,0.0002831921,0.000181986,0.0008402606,0.00003915861,0.0000799865],"category_scores_gemma":[0.000102337,0.0003295154,0.00007360691,0.0003319439,0.0002151002,0.0003795019,0.001268553,0.0006340171,0.00001231771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001938771,"about_ca_system_score_gemma":0.00004435428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001665048,"about_ca_topic_score_gemma":0.000003411723,"domain_scores_codex":[0.9974924,0.0003622551,0.0003826923,0.001061552,0.0002064103,0.0004946967],"domain_scores_gemma":[0.9989492,0.000303536,0.0001453892,0.000479242,0.000009316393,0.000113375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008812589,0.0008128209,0.008120382,0.00009694839,0.00007836583,0.0005543775,0.02047515,0.06770674,0.04232918,0.004693393,0.8199597,0.03429169],"study_design_scores_gemma":[0.003290926,0.001032282,0.004060314,0.0000625758,0.00003109962,0.000554707,0.007584075,0.3256007,0.03856862,0.002158127,0.6152785,0.00177799],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751467,0.0006893338,0.01488773,0.003754666,0.004109107,0.0004009437,0.0002588936,0.0001247604,0.0006278991],"genre_scores_gemma":[0.9874991,0.0002213508,0.007999268,0.00342517,0.0002058918,0.00002333465,0.00001369299,0.00006522307,0.0005470213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.257894,"threshold_uncertainty_score":0.9999157,"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."}}