{"id":"W2541652489","doi":"10.1109/tic-sth.2009.5444491","title":"EEG signal classification based on a Riemannian distance measure","year":2009,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Measure (data warehouse); Riemannian manifold; Pattern recognition (psychology); Metric (unit); Manifold (fluid mechanics); Mathematics; Riemannian geometry; Artificial intelligence; SIGNAL (programming language); k-nearest neighbors algorithm; Electroencephalography; Statistical manifold; Separable space; Nonlinear dimensionality reduction; Information geometry; Computer science; Mathematical analysis; Dimensionality reduction; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001083648,0.0001123275,0.00008893711,0.00005211457,0.0001048215,0.00008913936,0.000252382,0.00004083234,0.0001233504],"category_scores_gemma":[0.0000660294,0.00008611977,0.00004650826,0.0001817518,0.00004212351,0.0001121144,0.000006769831,0.0001164161,0.0001299257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002823974,"about_ca_system_score_gemma":0.00002283648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000181821,"about_ca_topic_score_gemma":0.000003529191,"domain_scores_codex":[0.9989384,0.00007692954,0.0001375094,0.0003620388,0.0003006083,0.0001845438],"domain_scores_gemma":[0.9994863,0.000120605,0.00004935754,0.0002510638,0.00002572807,0.00006690891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001408153,0.0004029947,0.0002025131,0.0000089556,0.000001420863,0.00001977069,0.0002111823,0.0007913532,0.9084047,0.03375239,0.01126313,0.0448008],"study_design_scores_gemma":[0.0006874018,0.0007411375,0.01698449,0.00009320591,0.000005636452,0.000006532843,0.00004460824,0.2937787,0.6645156,0.001942253,0.02081083,0.000389701],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2201619,0.00002917986,0.364132,0.02898858,0.0004913234,0.0006091308,0.00002157126,0.0008609901,0.3847053],"genre_scores_gemma":[0.9898518,5.427548e-7,0.0005310164,0.007833632,0.00004631049,0.000004205114,0.000001201166,0.000005869937,0.001725405],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7696899,"threshold_uncertainty_score":0.3511862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04369449720053528,"score_gpt":0.2748964069514122,"score_spread":0.2312019097508769,"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."}}