{"id":"W2042695103","doi":"10.1016/j.actaastro.2008.09.008","title":"Prospects of brain–machine interfaces for space system control","year":2008,"lang":"en","type":"article","venue":"Acta Astronautica","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Brain–computer interface; Computer science; Human–computer interaction; Space (punctuation); Measure (data warehouse); Control system; Control (management); Control engineering; Human–machine system; Artificial intelligence; Systems engineering; Engineering; Electrical engineering; Neuroscience; Data mining; Operating system; Electroencephalography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001690612,0.0002056408,0.0003642793,0.00008177484,0.0001485011,0.00002771708,0.0004598266,0.00005810019,0.00002377256],"category_scores_gemma":[0.0003374764,0.000167283,0.0001071781,0.0001320135,0.0002367613,0.0001787127,0.00008689344,0.0001405048,0.00001857336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004166562,"about_ca_system_score_gemma":0.00005246484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001018816,"about_ca_topic_score_gemma":0.00000289944,"domain_scores_codex":[0.9984897,0.00008924134,0.0003376884,0.0004286738,0.0002577409,0.0003969766],"domain_scores_gemma":[0.9985185,0.0008044567,0.0001907476,0.0003288069,0.00005947835,0.00009806207],"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":[0.00039653,0.0002569187,0.001806682,0.0002536869,0.00004602735,0.00002093987,0.0008694206,0.0001243418,0.97147,0.01540725,0.008158912,0.001189301],"study_design_scores_gemma":[0.002291361,0.001101828,0.001909777,0.0001477801,0.00002743853,0.0001705139,0.0001147464,0.01849335,0.9606764,0.00009100051,0.01471016,0.0002656163],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9730483,0.0000982797,0.01819548,0.004830879,0.0004792307,0.0009074255,0.0001565307,0.0002056678,0.00207819],"genre_scores_gemma":[0.9969522,0.000002517458,0.001677585,0.0002690303,0.00009473353,0.0000380122,0.000002288784,0.00002576295,0.0009378637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02390389,"threshold_uncertainty_score":0.6821602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0200596709114649,"score_gpt":0.2537894739674142,"score_spread":0.2337298030559493,"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."}}