{"id":"W4323059337","doi":"10.2139/ssrn.4356815","title":"Generalizable Electroencephalographic Classification of Parkinson's Disease Using Deep Learning","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Parkinson's disease; Psychology; Disease; Electroencephalography; Neuroscience; Deep brain stimulation; Cognitive psychology; Artificial intelligence; Medicine; Computer science; Pathology","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":[0.0003477,0.0005208617,0.0003265117,0.0005183993,0.0001116185,0.000526846,0.0002790095,0.0004936438,0.001486044],"category_scores_gemma":[0.001279004,0.0001387402,0.000464831,0.0005066473,0.0001439349,0.0004182549,0.000481278,0.0006445647,0.0004340933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002020431,"about_ca_system_score_gemma":0.0002901442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003558821,"about_ca_topic_score_gemma":0.005312183,"domain_scores_codex":[0.9998969,0.00002179232,0.000007713847,0.000033434,0.00001719536,0.00002300337],"domain_scores_gemma":[0.9997705,0.00009640239,0.0000273516,0.00004122546,0.00005055951,0.00001406186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006228545,0.0002688571,0.02137106,0.0001999391,0.0002232678,0.0003021257,0.0001037886,0.1433718,0.05204042,0.002136729,0.00605993,0.7732992],"study_design_scores_gemma":[0.00002168188,0.00007303984,0.01800566,0.00001982962,0.00004365078,0.00018574,0.00003256227,0.970148,0.005806292,0.004792963,0.0008590277,0.00001148434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4328294,0.001432889,0.5576321,0.0007475977,0.0001827835,0.00009533769,0.001863616,0.001557122,0.003659196],"genre_scores_gemma":[0.9670194,0.0003183963,0.02875071,0.00006399987,0.00006068777,0.00002430556,0.00104261,0.00004501296,0.00267504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003558821,"threshold_uncertainty_score":0.007076204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04723641418664819,"score_gpt":0.2941871350253512,"score_spread":0.246950720838703,"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."}}