{"id":"W4392697008","doi":"10.1371/journal.pone.0299271","title":"Time series classification of multi-channel nerve cuff recordings using deep learning","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Toronto Rehabilitation Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neurostimulation; Cuff; Computer science; Functional electrical stimulation; Medicine; Biomedical engineering; Stimulation; Surgery; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007128225,0.00109506,0.0004608083,0.0008388307,0.0001740026,0.0005580526,0.0005187809,0.000658517,0.0008399657],"category_scores_gemma":[0.002133035,0.0001911265,0.0006179431,0.0007369284,0.0002217411,0.0006732778,0.0004597304,0.0009803408,0.0003346638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004192628,"about_ca_system_score_gemma":0.0003710742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003453231,"about_ca_topic_score_gemma":0.004581493,"domain_scores_codex":[0.9997508,0.00003426968,0.00002309917,0.00008896978,0.00005966926,0.00004310914],"domain_scores_gemma":[0.9993852,0.0002760459,0.00007828076,0.00007488304,0.000153524,0.00003192801],"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":[0.000708894,0.0005627823,0.01415796,0.0003211528,0.0002872784,0.0004953975,0.0001765777,0.2592236,0.1123553,0.00103199,0.003712584,0.6069663],"study_design_scores_gemma":[0.000006744515,0.0001322841,0.007643897,0.00001956092,0.00002871271,0.00008244224,0.00004836178,0.9723025,0.01823825,0.0007483718,0.0007335611,0.00001530762],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5902499,0.002086158,0.4011061,0.0003971741,0.0003676924,0.0001124404,0.00122683,0.002591986,0.001861659],"genre_scores_gemma":[0.928459,0.0007048166,0.06670903,0.00007642394,0.00007594247,0.00009565108,0.001624336,0.0000560041,0.002198796],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003453231,"threshold_uncertainty_score":0.006866276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1114642794773091,"score_gpt":0.2806217831654408,"score_spread":0.1691575036881317,"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."}}