{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003392125,0.001476925,0.0006504311,0.0009364528,0.0002844556,0.0009712767,0.0008936159,0.001537227,0.001522517],"category_scores_gemma":[0.009445868,0.0003758613,0.0007371271,0.0005349722,0.000388612,0.00136713,0.0007695774,0.00106242,0.0002863712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001558037,"about_ca_system_score_gemma":0.0008634931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01520857,"about_ca_topic_score_gemma":0.008876971,"domain_scores_codex":[0.9990507,0.0002614924,0.00007779321,0.0001888639,0.0002931512,0.0001281114],"domain_scores_gemma":[0.9975595,0.00114739,0.0002069449,0.0001746882,0.0008240404,0.00008746392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001502013,0.0005072391,0.009228553,0.0003128769,0.0005050368,0.0001591588,0.00005293091,0.7542139,0.01398737,0.001607289,0.00173739,0.2161863],"study_design_scores_gemma":[0.0000106152,0.0001659419,0.0009883735,0.00001301498,0.00003920416,0.00001880202,0.000007648572,0.9933096,0.005064462,0.0002013981,0.0001733103,0.000007624517],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7331329,0.005034934,0.2494693,0.001112215,0.0008059336,0.0004150437,0.0006217723,0.002188552,0.007219401],"genre_scores_gemma":[0.9619023,0.0004525271,0.03568541,0.0001068453,0.00003927877,0.00007534332,0.0003385739,0.00005590706,0.001343769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01520857,"threshold_uncertainty_score":0.03024012,"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."}}