{"id":"W2016453624","doi":"10.1016/j.neuroimage.2013.04.033","title":"ICA-based artifact correction improves spatial localization of adaptive spatial filters in MEG","year":2013,"lang":"en","type":"article","venue":"NeuroImage","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Baycrest Hospital","funders":"Canadian Institutes of Health Research; James S. McDonnell Foundation","keywords":"Computer science; Artificial intelligence; Voxel; Artifact (error); Pattern recognition (psychology); Noise (video); Magnetoencephalography; Minification; Computer vision; Electroencephalography; Neuroscience; Psychology; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005054811,0.0009399071,0.0004345085,0.0007260854,0.0002385968,0.0007801429,0.0005444379,0.0008212078,0.002644182],"category_scores_gemma":[0.004603411,0.0003028648,0.000654672,0.000834161,0.0003295524,0.0008645761,0.0004369808,0.000797499,0.001171064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002202764,"about_ca_system_score_gemma":0.0007669996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002378827,"about_ca_topic_score_gemma":0.004189457,"domain_scores_codex":[0.9997472,0.000072148,0.00002398153,0.00005842283,0.0000672524,0.0000310108],"domain_scores_gemma":[0.9991305,0.0003818313,0.00006709673,0.0001256546,0.0002688345,0.00002607819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009038497,0.0001319575,0.00176466,0.0002807697,0.00019248,0.00016826,0.0001471088,0.03014378,0.4732466,0.003063177,0.003747614,0.4862097],"study_design_scores_gemma":[0.0001261681,0.0002434106,0.02016338,0.00006050062,0.0004321668,0.0009230031,0.00005960225,0.5184323,0.4429683,0.005372169,0.01113698,0.00008202879],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06156342,0.0007335802,0.932393,0.0002780525,0.0002044371,0.00003538967,0.0001971118,0.002577105,0.002017919],"genre_scores_gemma":[0.4476218,0.001157277,0.5447156,0.0002328671,0.0002206963,0.00007154627,0.0006978431,0.001317888,0.003964496],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002644182,"threshold_uncertainty_score":0.008845687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02243887874531066,"score_gpt":0.2477330522974456,"score_spread":0.2252941735521349,"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."}}