{"id":"W4408429731","doi":"10.1038/s41597-025-04763-w","title":"Electromyographic typing gesture classification dataset for neurotechnological human-machine interfaces","year":2025,"lang":"en","type":"article","venue":"Scientific Data","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"","keywords":"Session (web analytics); Computer science; Task (project management); Electromyography; Typing; Interface (matter); Gesture; Artificial intelligence; Key (lock); Machine learning; Speech recognition; Human–computer interaction; Pattern recognition (psychology); Physical medicine and rehabilitation; Medicine; Engineering; World Wide Web","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":[0.0007603553,0.003534442,0.001986869,0.002708651,0.0007715795,0.001075661,0.002260107,0.002390184,0.009913941],"category_scores_gemma":[0.002530197,0.0003223956,0.001765821,0.00209804,0.000474747,0.0005961606,0.001769703,0.001573139,0.01670627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006651081,"about_ca_system_score_gemma":0.0009636126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007453525,"about_ca_topic_score_gemma":0.02184924,"domain_scores_codex":[0.9985135,0.0001411047,0.0002332216,0.0004186588,0.0004959552,0.0001974725],"domain_scores_gemma":[0.9987482,0.0001907471,0.0001284888,0.0003752171,0.0004255434,0.0001318742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002307122,0.002306384,0.02475614,0.00442247,0.0008087466,0.00193888,0.0002507675,0.006877199,0.02732596,0.0008857102,0.7124662,0.2156545],"study_design_scores_gemma":[0.001465657,0.002080956,0.2214465,0.001328688,0.0006356899,0.006929365,0.00100508,0.0564568,0.04186835,0.00321928,0.6630754,0.0004882778],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.07673438,0.003177514,0.01026343,0.0005928247,0.0007889651,0.00164591,0.8910951,0.008189646,0.007512243],"genre_scores_gemma":[0.02970438,0.000342157,0.007618577,0.0001310846,0.00008318881,0.001280562,0.9569526,0.000124745,0.00376276],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009913941,"threshold_uncertainty_score":0.03316545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05592928662354131,"score_gpt":0.3127412149516888,"score_spread":0.2568119283281475,"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."}}