{"id":"W2598849256","doi":"10.1142/s0129065717500253","title":"A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings","year":2017,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Isfahan; Ministerio de Economía y Competitividad; Generalitat de Catalunya; European Commission; Agència per a la Competitivitat de l’Empresa; Chongqing Science and Technology Commission; McGill University","keywords":"Neural decoding; Decoding methods; Computer science; Channel (broadcasting); Speech recognition; Pattern recognition (psychology); Artificial intelligence; Algorithm; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005075744,0.0006495152,0.0003871521,0.0007397925,0.0001917962,0.0005948464,0.0008246381,0.0009152836,0.002507539],"category_scores_gemma":[0.001352641,0.0002653077,0.0002747791,0.0005040286,0.0002947292,0.0007676533,0.000342671,0.0006146166,0.001438772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002691556,"about_ca_system_score_gemma":0.0004187063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006801928,"about_ca_topic_score_gemma":0.001052538,"domain_scores_codex":[0.9995613,0.00006064686,0.00002859219,0.0001257944,0.0002050729,0.00001861586],"domain_scores_gemma":[0.9994611,0.0001523474,0.00007547615,0.00008055755,0.0002040653,0.00002648068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000281,0.00008436927,0.00130228,0.0002173851,0.00005409724,0.0001379681,0.00009860596,0.005217516,0.3271526,0.001921283,0.002291619,0.6612414],"study_design_scores_gemma":[0.0001399016,0.0006505785,0.01298427,0.0000909149,0.0001422972,0.002352548,0.00009446327,0.6636651,0.2961815,0.00224673,0.02127773,0.0001740024],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007679303,0.0001356989,0.9902796,0.00004021335,0.00004205787,0.00005073427,0.00007214178,0.001300517,0.0003997202],"genre_scores_gemma":[0.1111227,0.0001730175,0.8862112,0.00006442792,0.00004162827,0.0001498526,0.0001890939,0.0001194271,0.001928557],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002507539,"threshold_uncertainty_score":0.008388519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04322783891698063,"score_gpt":0.3077254046586479,"score_spread":0.2644975657416673,"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."}}