{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009084598,0.0005501146,0.001108839,0.001198031,0.0004181357,0.0009081803,0.0008918262,0.0007833556,0.0008124112],"category_scores_gemma":[0.003431805,0.0002038114,0.0008276453,0.0009927952,0.0006137886,0.00132267,0.0009221846,0.000629269,0.0005153396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004726044,"about_ca_system_score_gemma":0.0004352829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00149353,"about_ca_topic_score_gemma":0.0009504039,"domain_scores_codex":[0.9988926,0.000237976,0.0001239885,0.0002620091,0.0004344891,0.00004892256],"domain_scores_gemma":[0.9991843,0.0001897924,0.00009132394,0.0001016743,0.000379941,0.00005284688],"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":[0.000328184,0.0001256425,0.003868068,0.0003651512,0.0002784843,0.00037134,0.0003269874,0.1467777,0.07098523,0.04864182,0.002626986,0.7253044],"study_design_scores_gemma":[0.00001750189,0.0002345932,0.002074518,0.00001350525,0.00003388097,0.0004221803,0.00002986035,0.9708844,0.01036434,0.01259694,0.003264297,0.00006398245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0185821,0.0002891699,0.9801185,0.00009885556,0.00004948832,0.00003348203,0.00003126641,0.0001853093,0.0006117414],"genre_scores_gemma":[0.365103,0.0003665961,0.6323401,0.00009805761,0.000136323,0.00009055865,0.000214194,0.0000687772,0.001582462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00149353,"threshold_uncertainty_score":0.004804492,"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."}}