{"id":"W3008800301","doi":"10.1109/isspit47144.2019.9001818","title":"EEG signal Extraction Utilizing Null Space Approach","year":2019,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Electroencephalography; Independent component analysis; Computer science; Artifact (error); Pattern recognition (psychology); Artificial intelligence; FastICA; SIGNAL (programming language); Noise (video); Null (SQL); Blind signal separation; Speech recognition; Data mining; Channel (broadcasting); Image (mathematics)","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":[0.0003342972,0.00008446106,0.00008663042,0.00008915046,0.00004354377,0.0001540274,0.000370385,0.00006512622,0.0001377202],"category_scores_gemma":[0.000005458937,0.00007582141,0.00004233692,0.0002243224,0.00001383877,0.0008065454,0.00009513997,0.0001403501,0.00035559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002614829,"about_ca_system_score_gemma":0.00003487497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003188289,"about_ca_topic_score_gemma":0.000001533033,"domain_scores_codex":[0.9991668,0.00005725659,0.0001220681,0.0002872766,0.0002160767,0.0001504487],"domain_scores_gemma":[0.9994313,0.00004387463,0.00005641739,0.0003769224,0.00004631049,0.00004513395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004177696,0.0001006988,0.0006342232,0.00001166831,0.000007427644,0.000001234824,0.001229977,0.0002624944,0.02061576,0.9615501,0.004001197,0.01158105],"study_design_scores_gemma":[0.0004656389,0.000212249,0.003864011,0.00001887238,0.000005133736,0.0000659214,0.0007812126,0.8237212,0.1062321,0.01067627,0.05336106,0.0005963241],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008719834,0.00001131191,0.7030748,0.0003928876,0.00006456899,0.0001378842,7.67474e-8,0.000540002,0.2870587],"genre_scores_gemma":[0.730636,0.000002002622,0.2640338,0.0003702893,0.00001470099,0.000007103343,0.000001177504,0.00000514868,0.004929852],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9508738,"threshold_uncertainty_score":0.4570509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02144624491466448,"score_gpt":0.2729789206883118,"score_spread":0.2515326757736473,"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."}}