{"id":"W3031257706","doi":"10.35693/2500-1388-2016-0-3-77-82","title":"DEVELOPMENT OF THE HARDWARE AND SOFTWARE COMPLEX CONTROLLING ROBOTIC DEVICES BY MEANS OF BIOELECTRIC SIGNALS OF THE BRAIN AND MUSCLES","year":2016,"lang":"en","type":"article","venue":"Science and Innovations in Medicine","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interface (matter); Computer science; Software; Electroencephalography; Preprocessor; Artificial intelligence; Exoskeleton; Brain–computer interface; Robot; Electromyography; Control unit; Computer hardware; Simulation; Pattern recognition (psychology); Neuroscience","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009342906,0.00006662254,0.0001658729,0.0001790844,0.0001784534,0.000009216804,0.00030253,0.00001726414,0.000004874857],"category_scores_gemma":[0.00174662,0.00002868159,0.000006471611,0.001590486,0.001924945,0.0001319836,0.0001153702,0.00005008437,3.806681e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000112525,"about_ca_system_score_gemma":0.0001072846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001354643,"about_ca_topic_score_gemma":0.00002461248,"domain_scores_codex":[0.9989626,0.00004397051,0.0003502743,0.0001849708,0.0003395469,0.0001186132],"domain_scores_gemma":[0.9989837,0.0004961112,0.0002019517,0.0001245383,0.0001759352,0.00001777908],"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":[0.000002587339,0.00001264461,0.008401071,0.00002762279,0.000001525088,4.311368e-8,0.001083422,0.00000640674,0.9659823,0.000961941,0.0001404291,0.02337999],"study_design_scores_gemma":[0.0008730742,0.0001296583,0.1238191,0.0008965576,0.000007696233,0.000006538005,0.0007205885,0.001465698,0.8702488,0.0009487495,0.0007910143,0.00009260215],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896626,0.000179537,0.003636667,0.006244705,0.00005596194,0.0001651521,0.000005893288,0.000003878996,0.00004554472],"genre_scores_gemma":[0.9981817,0.00002299944,0.0009743124,0.000786872,0.000006292589,0.00000266394,1.407516e-7,0.000002019987,0.00002301024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.115418,"threshold_uncertainty_score":0.7092541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04921042950834458,"score_gpt":0.2989771633409345,"score_spread":0.24976673383259,"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."}}