{"id":"W2101622159","doi":"10.1109/iembs.2007.4353588","title":"Manifold Learning Applied on EEG Signal of the Epileptic Patients for Detection of Normal and Pre-Seizure States","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Isomap; Nonlinear dimensionality reduction; Pattern recognition (psychology); Dimensionality reduction; Electroencephalography; Feature extraction; Artificial intelligence; Principal component analysis; Computer science; Epileptic seizure; Feature vector; Manifold (fluid mechanics); Feature (linguistics); Dimension (graph theory); Embedding; Speech recognition; Mathematics; Psychology; Neuroscience; Engineering","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":[0.0001846513,0.0001016095,0.0001248365,0.00005784005,0.000102228,0.0000342331,0.0001809802,0.00004993242,0.00000614528],"category_scores_gemma":[0.0001244997,0.00007304857,0.00003688517,0.0001073733,0.00009458339,0.0000996423,0.00007046392,0.0001418614,7.175108e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001048741,"about_ca_system_score_gemma":0.00001204186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003810636,"about_ca_topic_score_gemma":0.000002199239,"domain_scores_codex":[0.9991999,0.000005725422,0.00021231,0.000222705,0.0001819679,0.0001773492],"domain_scores_gemma":[0.9993971,0.0001623886,0.0002227437,0.00004226509,0.0001428348,0.00003267338],"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":[0.0002662559,0.00006022625,0.009579227,0.0001708168,0.000004751452,5.271372e-8,0.002815735,0.00002036685,0.9696915,0.002640761,0.00002453821,0.01472575],"study_design_scores_gemma":[0.0003844855,0.0006339055,0.02218735,0.0000835134,0.000009866258,0.000001317388,0.0003565493,0.003178379,0.9722139,0.0007272562,0.0001404137,0.00008312241],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950696,0.000003453774,0.003338187,0.00001723781,0.00006772258,0.0003537076,0.000004601687,0.00002153368,0.001123925],"genre_scores_gemma":[0.9997001,0.000002873442,0.0001012196,0.00006248859,0.00001770451,0.00001141424,5.102362e-7,0.000008203358,0.0000954776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01464263,"threshold_uncertainty_score":0.2978834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01667579624028053,"score_gpt":0.2355983397923654,"score_spread":0.2189225435520849,"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."}}