{"id":"W4402535853","doi":"10.1016/j.neucom.2024.128577","title":"Deep learning in motor imagery EEG signal decoding: A Systematic Review","year":2024,"lang":"en","type":"review","venue":"Neurocomputing","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Motor imagery; Electroencephalography; Computer science; Decoding methods; Artificial intelligence; SIGNAL (programming language); Deep learning; Motor learning; Pattern recognition (psychology); Speech recognition; Brain–computer interface; Computer vision; Psychology; Neuroscience; Algorithm","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":[0.002228117,0.001251491,0.004210797,0.002705753,0.0002257297,0.001605852,0.001392586,0.001322783,0.004889918],"category_scores_gemma":[0.008450852,0.0004829381,0.00350046,0.002928004,0.0005703203,0.001527164,0.001133658,0.001302126,0.0006628374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006340091,"about_ca_system_score_gemma":0.003147472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003452569,"about_ca_topic_score_gemma":0.008918482,"domain_scores_codex":[0.9992593,0.0001912325,0.0002479789,0.0001327761,0.0001365811,0.00003201899],"domain_scores_gemma":[0.9960978,0.00313879,0.0003879137,0.00005599155,0.0002722033,0.0000472618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0002994059,0.00006802304,0.0004578562,0.3418715,0.004607325,0.0001117587,0.0000763521,0.0007013691,0.0005462377,0.0008603102,0.007245731,0.6431541],"study_design_scores_gemma":[0.001185712,0.00150855,0.01012828,0.5554478,0.08644383,0.002580974,0.0004004555,0.002100413,0.002252821,0.007761315,0.3298727,0.0003172671],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001573074,0.9992949,0.0002435189,0.00009226026,0.00003693771,0.00001432159,0.00005657754,0.000004566088,0.00009956608],"genre_scores_gemma":[0.001800773,0.9971826,0.000569054,0.0002098066,0.00005899051,0.00002759232,0.0000646253,0.000003499877,0.00008308506],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004889918,"threshold_uncertainty_score":0.01635838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05628134548859225,"score_gpt":0.3372937615336569,"score_spread":0.2810124160450646,"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."}}