{"id":"W3165814049","doi":"10.1016/j.neuroimage.2022.118994","title":"Robust learning from corrupted EEG with dynamic spatial filtering","year":2022,"lang":"en","type":"article","venue":"NeuroImage","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"InteraXon (Canada)","funders":"Agence Nationale de la Recherche; Mitacs; Department of Atomic Energy, Government of India","keywords":"Computer science; Robustness (evolution); Electroencephalography; Artificial intelligence; Deep learning; Channel (broadcasting); Machine learning; Wearable computer; Pattern recognition (psychology); Noise (video); Speech recognition; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007045465,0.000171123,0.0001586367,0.00008022827,0.0004573643,0.000138431,0.0004521406,0.0000182157,0.0007941864],"category_scores_gemma":[0.00009038071,0.0001607179,0.0000452933,0.000219528,0.00007056917,0.0001567917,0.00044993,0.0006300993,0.00005215549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003820627,"about_ca_system_score_gemma":0.0000240763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001531773,"about_ca_topic_score_gemma":0.00002610229,"domain_scores_codex":[0.9982869,0.0002712611,0.000163159,0.0006094849,0.0003654505,0.000303727],"domain_scores_gemma":[0.9992856,0.0002606276,0.0001029266,0.0002744831,0.0000126657,0.00006364192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009923327,0.00006095625,0.0009182881,0.000006021136,0.000003384559,0.0005390017,0.0004201729,0.05255378,0.9405586,0.000008975398,0.0002452334,0.004586411],"study_design_scores_gemma":[0.001917944,0.001845582,0.03421832,0.00004573864,0.00003023871,0.0004385204,0.0003308359,0.7233366,0.2109418,0.00006479371,0.02596571,0.0008638995],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846602,0.00001128266,0.0121698,0.0002736369,0.0005765921,0.0001422547,0.00003984199,0.0002970975,0.001829279],"genre_scores_gemma":[0.9973577,0.000002793741,0.0004314288,0.0009159023,0.00004621487,0.00001835634,0.00001400258,0.00004210958,0.001171507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7296168,"threshold_uncertainty_score":0.8695784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03585080360628563,"score_gpt":0.2414048625760727,"score_spread":0.2055540589697871,"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."}}