{"id":"W4372341360","doi":"10.3390/bioengineering10050553","title":"Fast Optical Signals for Real-Time Retinotopy and Brain Computer Interface","year":2023,"lang":"en","type":"article","venue":"Bioengineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Brain–computer interface; Computer science; Retinotopy; Artificial intelligence; Visual cortex; Support vector machine; Pattern recognition (psychology); Visual field; Computer vision; Electroencephalography; Physics; Neuroscience; Optics","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":[0.0006008805,0.00062538,0.0003026437,0.0006566003,0.0002096701,0.0007463788,0.0005439882,0.0007130998,0.005354555],"category_scores_gemma":[0.001767921,0.0002265803,0.0002370087,0.0008121596,0.0004060668,0.0007442601,0.0004383016,0.0008121267,0.001709966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004910188,"about_ca_system_score_gemma":0.0004137842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006826626,"about_ca_topic_score_gemma":0.0009831308,"domain_scores_codex":[0.9993555,0.0001311573,0.000033988,0.0001181235,0.00031723,0.0000440688],"domain_scores_gemma":[0.9994161,0.0002146094,0.00008389073,0.00009040792,0.0001727691,0.00002228183],"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":[0.0003128694,0.0001110216,0.001035552,0.0003406417,0.00005425747,0.0001247225,0.0000929856,0.003380069,0.4430304,0.01085332,0.005012601,0.5356515],"study_design_scores_gemma":[0.00009922738,0.001118626,0.01325816,0.0002492007,0.0001127585,0.001683122,0.0001180724,0.240218,0.6424627,0.02115731,0.07935867,0.0001641744],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05106179,0.00420039,0.9301106,0.0007361432,0.0004541014,0.0001624034,0.000458068,0.00280386,0.01001265],"genre_scores_gemma":[0.5089886,0.002274619,0.4790199,0.0005398743,0.0002653213,0.0002586711,0.000503694,0.0002311055,0.007918164],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005354555,"threshold_uncertainty_score":0.01791275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02493956331278507,"score_gpt":0.2747273329111374,"score_spread":0.2497877695983524,"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."}}