{"id":"W2004230937","doi":"10.1109/tbme.2013.2283514","title":"Minimum Variance Brain Source Localization for Short Data Sequences","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Adaptive beamformer; Context (archaeology); Beamforming; Computer science; Signal processing; Pattern recognition (psychology); SIGNAL (programming language); Minimum-variance unbiased estimator; Artificial intelligence; Electroencephalography; Variance (accounting); Higher-order statistics; Algorithm; Radar; Statistics; Mathematics; Mean squared error","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":[0.0007427276,0.0005346009,0.0003650382,0.0004621203,0.000169494,0.0003743216,0.0003754754,0.0005794662,0.001732364],"category_scores_gemma":[0.006495934,0.000211729,0.0003334768,0.0006605472,0.0004402249,0.0010272,0.0005223966,0.0004695012,0.0006436086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001716018,"about_ca_system_score_gemma":0.0004677311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007194267,"about_ca_topic_score_gemma":0.001072695,"domain_scores_codex":[0.9995064,0.0001788639,0.00002924818,0.0000620133,0.000203359,0.00002020205],"domain_scores_gemma":[0.9983934,0.001152546,0.0001258754,0.0001463557,0.0001614169,0.00002044088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004533742,0.0000582457,0.000658697,0.0004279393,0.00006499016,0.0003678213,0.0002582613,0.3293633,0.174917,0.0218325,0.001797537,0.4698003],"study_design_scores_gemma":[0.00003926759,0.000184719,0.0009859592,0.00003561076,0.00001674467,0.0003668457,0.00003863241,0.9396747,0.03469278,0.01964265,0.004295097,0.00002691073],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007165441,0.000167131,0.9920203,0.000057587,0.00001201732,0.0000178104,0.00001866267,0.0001661595,0.0003748141],"genre_scores_gemma":[0.1954972,0.0007983617,0.8015772,0.0001031272,0.00004993602,0.0001713264,0.0002372739,0.00008470968,0.001480923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001732364,"threshold_uncertainty_score":0.0057953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02567628017598041,"score_gpt":0.2685272053359747,"score_spread":0.2428509251599943,"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."}}