{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008484211,0.0001767041,0.0002755654,0.0002002604,0.0005771957,0.0001364324,0.0007882917,0.00002738909,0.000134941],"category_scores_gemma":[0.00005837088,0.0001560533,0.00005058055,0.0006476407,0.0009944731,0.0001261394,0.0005507275,0.0002262276,0.000008736311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003783335,"about_ca_system_score_gemma":0.0000654231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002287326,"about_ca_topic_score_gemma":0.000003791452,"domain_scores_codex":[0.9978601,0.0001625944,0.0004394138,0.0006583244,0.0005182321,0.0003613541],"domain_scores_gemma":[0.998692,0.0008116392,0.0002072283,0.0001890139,0.00002020139,0.00007992705],"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":[0.00005744989,0.0002102776,0.00008874117,0.00002148425,0.000008120343,0.000006282629,0.002248374,0.3133039,0.5743491,0.07377347,0.00008810734,0.03584466],"study_design_scores_gemma":[0.00007606776,0.0004549552,0.00001812587,0.00001467464,0.000008290255,0.00005176286,0.001118256,0.4598238,0.5281757,0.009784343,0.0002663138,0.0002076992],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5886505,0.00008206968,0.4091941,0.0006879781,0.0005513262,0.0002929179,0.0000076076,0.0000451443,0.0004883151],"genre_scores_gemma":[0.9952249,0.00000336778,0.003500572,0.001163949,0.00006477397,0.00001372948,2.446288e-7,0.0000092747,0.00001919348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4065743,"threshold_uncertainty_score":0.6363671,"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."}}