{"id":"W2996752967","doi":"10.1016/j.clinph.2019.11.032","title":"A novel method for extracting interictal epileptiform discharges in multi-channel MEG: Use of fractional type of blind source separation","year":2019,"lang":"en","type":"article","venue":"Clinical Neurophysiology","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Fakultet Medicinskih Nauka, Univerziteta U Kragujevcu; Kyushu University; Ministry of Education, Culture, Sports, Science and Technology; McGill University","keywords":"Ictal; Pattern recognition (psychology); Waveform; Blind signal separation; Computer science; SIGNAL (programming language); Independent component analysis; Electroencephalography; Artificial intelligence; Channel (broadcasting); Neuroscience; Psychology; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002686676,0.0001107752,0.000545654,0.0001012541,0.00001662808,0.000003128855,0.00006340682,0.0001327889,0.00005168553],"category_scores_gemma":[0.0021422,0.00008549451,0.0001940788,0.0001274096,0.00009415776,0.0000909922,0.00006169706,0.0003064652,0.00002152729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001584497,"about_ca_system_score_gemma":0.00008416922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004603693,"about_ca_topic_score_gemma":0.000007522344,"domain_scores_codex":[0.9985639,0.0001442206,0.0006665159,0.0003163459,0.0001114124,0.0001975595],"domain_scores_gemma":[0.9962462,0.002982131,0.0002676979,0.0002144012,0.0002047745,0.00008482228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01420742,0.004118881,0.0962522,0.0001990196,0.0002034666,0.000008241759,0.0001157883,0.001120262,0.8785843,0.0000876038,0.00006182207,0.005041045],"study_design_scores_gemma":[0.009267202,0.009452335,0.8651175,0.0001072816,0.00007216434,0.00002559979,0.00009284549,0.1101928,0.004809397,0.00009252601,0.0006438523,0.0001265481],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9716973,0.00001230257,0.02712036,0.0002069678,0.0002879654,0.0006235313,0.00002745358,0.000008792874,0.00001530353],"genre_scores_gemma":[0.9792064,0.00005320074,0.02009104,0.0001470953,0.00009282144,0.00002334786,0.00007325837,0.00001828723,0.0002945707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8737748,"threshold_uncertainty_score":0.3486365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2712443894355021,"score_gpt":0.5063138682990671,"score_spread":0.2350694788635651,"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."}}