{"id":"W4307216773","doi":"10.3390/app122110813","title":"Intelligent Control of Robotic Arm Using Brain Computer Interface and Artificial Intelligence","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Brain–computer interface; Computer science; Artificial intelligence; Random forest; Support vector machine; Decision tree; Electroencephalography; Robotics; Software deployment; Machine learning; Interface (matter); Boosting (machine learning); Gradient boosting; Classifier (UML); Robotic arm; Robot; Pattern recognition (psychology)","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":[0.0002830723,0.0003187949,0.0002648586,0.0003096274,0.0002058338,0.0004338608,0.000361445,0.0003087932,0.001295556],"category_scores_gemma":[0.0006406222,0.0001048319,0.0002181,0.0001944383,0.0002080358,0.0003037977,0.0002653456,0.0001970315,0.0003492587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001705926,"about_ca_system_score_gemma":0.0002690166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001019707,"about_ca_topic_score_gemma":0.001020419,"domain_scores_codex":[0.9997776,0.00003527292,0.00001471782,0.00004776374,0.00009130308,0.00003338041],"domain_scores_gemma":[0.999861,0.00003727267,0.00002651468,0.00002181627,0.0000460736,0.00000731625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004060062,0.0002701005,0.003755934,0.0003345365,0.00009194639,0.0004396263,0.0002245448,0.1261755,0.2925477,0.004507026,0.002928954,0.5683182],"study_design_scores_gemma":[0.00004070683,0.0007956921,0.01077087,0.00005025817,0.00005680129,0.0005585493,0.00006081094,0.8973556,0.07817437,0.003608682,0.008474765,0.00005281523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1889507,0.0008146763,0.7950509,0.0002241377,0.0001345728,0.000181581,0.00006865698,0.002848592,0.01172615],"genre_scores_gemma":[0.9264992,0.0001748009,0.07085773,0.00007623819,0.00001764372,0.00007076802,0.00004391343,0.00002989451,0.002229795],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001295556,"threshold_uncertainty_score":0.004334092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07289084466571548,"score_gpt":0.3093943986949229,"score_spread":0.2365035540292074,"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."}}