{"id":"W4402306980","doi":"10.18280/ts.410442","title":"Performance Analysis of Hybrid – BCI Signals Using CNN for Motor Movement Classification","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Brain–computer interface; Movement (music); Computer science; Speech recognition; Motor imagery; Artificial intelligence; Pattern recognition (psychology); Electroencephalography; Psychology; Neuroscience; Acoustics; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004323386,0.000629972,0.0003807587,0.0004595376,0.0001875308,0.000455842,0.0004033279,0.0004824003,0.002591614],"category_scores_gemma":[0.001268478,0.0001248951,0.0002803646,0.000381329,0.0001237514,0.0002800913,0.0002688958,0.0002894055,0.0005255265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005130256,"about_ca_system_score_gemma":0.0003997344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01978464,"about_ca_topic_score_gemma":0.02042471,"domain_scores_codex":[0.9997676,0.00002811482,0.00001556167,0.00005006215,0.00008133503,0.00005735047],"domain_scores_gemma":[0.999558,0.0001548033,0.00003276916,0.00003449139,0.0001997567,0.00002020309],"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":[0.002856842,0.0003533397,0.02033162,0.0004409766,0.0005044499,0.0002883846,0.00007617079,0.1792427,0.09486122,0.0008365258,0.005586541,0.6946214],"study_design_scores_gemma":[0.00001382769,0.0002440497,0.0190287,0.00001771302,0.00008165983,0.0001194578,0.00002053238,0.9571444,0.02241752,0.0001358079,0.0007619082,0.00001444649],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9200166,0.002778452,0.06420114,0.000288138,0.0002474714,0.00005670523,0.00100932,0.00130188,0.01010021],"genre_scores_gemma":[0.9874254,0.0002608146,0.007951747,0.00004485338,0.00002577224,0.00002142523,0.0007284412,0.00003082327,0.003510756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01978464,"threshold_uncertainty_score":0.03933895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04938988182063841,"score_gpt":0.2873874282148783,"score_spread":0.2379975463942399,"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."}}