{"id":"W3198462785","doi":"10.1111/aor.14059","title":"Characterizing the stimulation interference in electroencephalographic signals during brain–computer interface–controlled functional electrical stimulation therapy","year":2021,"lang":"en","type":"article","venue":"Artificial Organs","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"","keywords":"Brain–computer interface; Electroencephalography; Functional electrical stimulation; Interference (communication); Noise (video); Stimulation; Computer science; Frequency band; SIGNAL (programming language); Speech recognition; Artificial intelligence; Psychology; Neuroscience; Telecommunications","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":[0.0003383624,0.000403637,0.0002377209,0.0004822892,0.0001019567,0.0002488614,0.0001645731,0.0003088915,0.001170788],"category_scores_gemma":[0.002323094,0.00007136904,0.0001631441,0.0003518159,0.0002275336,0.0001824998,0.0001589351,0.0001357029,0.0001467519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001280288,"about_ca_system_score_gemma":0.0001345312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005914397,"about_ca_topic_score_gemma":0.0007637758,"domain_scores_codex":[0.9997161,0.00007801887,0.00002763795,0.00004903981,0.0001028807,0.0000263601],"domain_scores_gemma":[0.9993919,0.0003348362,0.00007543294,0.0000296026,0.0001476971,0.00002051032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001701611,0.000315442,0.02102986,0.0008347225,0.0001806114,0.0003995869,0.0005496397,0.0034728,0.8005481,0.0002503186,0.0004557238,0.1702615],"study_design_scores_gemma":[0.0001221714,0.002751579,0.7041497,0.00009983721,0.0002261942,0.001750839,0.0003148078,0.0291321,0.257871,0.0007287753,0.0028038,0.00004925023],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9650365,0.0004459966,0.03263365,0.0000453454,0.00002479827,0.0001375498,0.000259665,0.00008670509,0.001329812],"genre_scores_gemma":[0.9887558,0.0001877417,0.01020118,0.00003277636,0.00001662197,0.0001412784,0.0002098072,0.00002007982,0.0004346498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001170788,"threshold_uncertainty_score":0.003916681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03464029536367805,"score_gpt":0.2728992763304096,"score_spread":0.2382589809667315,"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."}}