{"id":"W3215846288","doi":"10.3389/fnbot.2021.692183","title":"Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion","year":2021,"lang":"en","type":"article","venue":"Frontiers in Neurorobotics","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ministero dello Sviluppo Economico; Ontario Ministry of Research, Innovation and Science; Ontario Ministry of Economic Development and Innovation; Canada Foundation for Innovation; Ontario Research Foundation","keywords":"Computer science; Electroencephalography; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Brain–computer interface; Electromyography; Exoskeleton; Speech recognition; Simulation; Physical medicine and rehabilitation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003782525,0.0001901492,0.0003431288,0.0001266318,0.0001077713,0.00004912533,0.0003622095,0.0001012336,0.00004335767],"category_scores_gemma":[0.001069717,0.0001941307,0.0001133439,0.0005546166,0.0001311861,0.0001455695,0.0002825346,0.0004020762,0.000003360854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004365783,"about_ca_system_score_gemma":0.0000972261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008707992,"about_ca_topic_score_gemma":0.000003419083,"domain_scores_codex":[0.9973597,0.0007474049,0.0004803974,0.0005795931,0.0004530009,0.0003798984],"domain_scores_gemma":[0.9986896,0.0005962944,0.000185262,0.0003424453,0.0001028102,0.00008362397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008592459,0.0001938653,0.009040984,0.00004845869,0.00001024253,0.0002212987,0.0003163262,0.8605453,0.09595043,0.001152326,0.004387047,0.02804783],"study_design_scores_gemma":[0.0005336211,0.0001655774,0.003632865,0.00004926161,0.00001672645,0.0001440875,0.00007007908,0.95222,0.04148698,0.001128122,0.0003778063,0.0001749196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4025098,0.0004761324,0.5879546,0.001360904,0.006480627,0.0002651717,0.00001105607,0.00007788985,0.0008638296],"genre_scores_gemma":[0.7969174,0.0001052619,0.1999161,0.002270864,0.0001365311,0.000006416233,0.000006899532,0.00003443492,0.0006061697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3944075,"threshold_uncertainty_score":0.7916421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05681451978171615,"score_gpt":0.3481919851270388,"score_spread":0.2913774653453227,"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."}}