{"id":"W2409078106","doi":"10.1088/1741-2560/13/2/026024","title":"Pushing the P300-based brain–computer interface beyond 100 bpm: extending performance guided constraints into the temporal domain","year":2016,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Algoma University","funders":"Algoma University","keywords":"Brain–computer interface; Computer science; Artificial intelligence; Flashing; Domain (mathematical analysis); Interface (matter); Sentence; Pattern recognition (psychology); Speech recognition; Electroencephalography; Mathematics","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":[0.0006109076,0.0005752123,0.0002497825,0.0002537552,0.0001346486,0.0005375804,0.0008495727,0.0004080828,0.002608214],"category_scores_gemma":[0.003585708,0.000161442,0.0002669002,0.0002297771,0.0004016198,0.001194969,0.0008193163,0.0006714688,0.0006607705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002246727,"about_ca_system_score_gemma":0.0003349948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003956123,"about_ca_topic_score_gemma":0.0004421736,"domain_scores_codex":[0.9995233,0.00009481868,0.00003235589,0.00009531451,0.0002301297,0.0000240775],"domain_scores_gemma":[0.9990849,0.0004331476,0.0001301124,0.0001262222,0.000178164,0.00004749521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001167268,0.0002135039,0.001269777,0.0003969261,0.0000312181,0.0001627159,0.0001243503,0.002677014,0.7439929,0.002725378,0.0008208273,0.2464181],"study_design_scores_gemma":[0.0006386557,0.006567688,0.04556558,0.0001658651,0.0001699494,0.003254561,0.0001820129,0.2222262,0.6831915,0.01823421,0.01965108,0.000152726],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.36746,0.001107506,0.6216643,0.0005085092,0.0001593169,0.0003278412,0.0002511448,0.0007068389,0.007814549],"genre_scores_gemma":[0.8443608,0.0005467678,0.1529064,0.000221965,0.0001182277,0.0002341144,0.0001927033,0.00008914246,0.001329788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002608214,"threshold_uncertainty_score":0.008725345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02241871060157112,"score_gpt":0.265199760152856,"score_spread":0.2427810495512848,"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."}}