{"id":"W2778092082","doi":"10.1109/tii.2017.2785415","title":"Mind Control of a Robotic Arm With Visual Fusion Technology","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":149,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Robotics and System; Natural Science Foundation of Guangdong Province","keywords":"Computer vision; Artificial intelligence; Computer science; Robotic arm; Object (grammar); Visual servoing; Task (project management); Motion control; Obstacle avoidance; Robot; Control system; Mobile robot; Engineering","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":[0.000211075,0.0002872988,0.0002183016,0.0001874688,0.0002680332,0.0003858349,0.000497024,0.0003236845,0.001007378],"category_scores_gemma":[0.0003830615,0.0001250462,0.0002999988,0.0001082207,0.0003159525,0.0004028818,0.0008214653,0.0003006906,0.0001425702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002472331,"about_ca_system_score_gemma":0.0002696727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001051779,"about_ca_topic_score_gemma":0.001044847,"domain_scores_codex":[0.9998358,0.0000135544,0.000007753481,0.0000434718,0.00007760247,0.00002181191],"domain_scores_gemma":[0.999873,0.00003134216,0.00002905445,0.00001837567,0.00003418247,0.0000138934],"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":[0.0004585953,0.0001469337,0.00141058,0.0002158323,0.00009019253,0.0004398545,0.0005907833,0.06944688,0.583953,0.01025446,0.001126502,0.3318664],"study_design_scores_gemma":[0.0001283516,0.001258468,0.005992298,0.00004584538,0.0001230501,0.0006346094,0.0001133159,0.8183244,0.1507655,0.009238268,0.01330347,0.00007239257],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1574366,0.0003708681,0.8338962,0.0001627907,0.00006835828,0.00005595199,0.00002492988,0.0008956211,0.007088671],"genre_scores_gemma":[0.9522984,0.00008454582,0.04500465,0.00004928491,0.00001819513,0.00004514187,0.00002084092,0.00001675886,0.002462228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001051779,"threshold_uncertainty_score":0.003369987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05104758141838629,"score_gpt":0.2921254179642555,"score_spread":0.2410778365458692,"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."}}