{"id":"W3040538438","doi":"10.3390/s20133703","title":"Trends in Compressive Sensing for EEG Signal Processing Applications","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Compressed sensing; Brain–computer interface; Electroencephalography; Computer science; Field (mathematics); Neural engineering; Signal processing; Energy (signal processing); Interface (matter); SIGNAL (programming language); Artificial intelligence; Machine learning; Neuroscience; Digital signal processing; Psychology; Computer hardware","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00005717577,0.000466945,0.001253319,0.0004578758,0.00007499428,0.00007225425,0.0002097665,0.0002962386,0.000007391038],"category_scores_gemma":[0.000006842121,0.0004621338,0.0003385128,0.0006754729,0.00004364409,0.00004607421,0.00004277721,0.0004566802,0.00001482137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001319779,"about_ca_system_score_gemma":0.00004677619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005605408,"about_ca_topic_score_gemma":0.000006220901,"domain_scores_codex":[0.9985099,0.00004936086,0.0005305774,0.0004205671,0.0001309043,0.0003586308],"domain_scores_gemma":[0.9993008,0.000139499,0.0001529184,0.0002652245,0.00005466281,0.00008690199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001551953,0.000006529982,6.981815e-8,0.002106078,0.00004213341,0.00002006335,0.00006110493,0.001102544,0.00001104458,0.00002625943,0.0008735385,0.9957491],"study_design_scores_gemma":[0.00008751864,0.00001222482,3.949995e-7,0.005051469,0.000197539,0.00003600433,0.00001863703,0.05996003,0.00005376662,0.0001113698,0.9340224,0.0004486035],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000002857948,0.9780725,0.01772041,0.00001500287,0.00006288095,0.0008691483,0.00006173826,0.0009719952,0.002223504],"genre_scores_gemma":[0.0006231082,0.987449,0.01081528,0.00002285263,0.0003745529,0.0001742991,0.0002274608,0.0002241673,0.0000892078],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9953005,"threshold_uncertainty_score":0.999783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04805201194775321,"score_gpt":0.319051792001372,"score_spread":0.2709997800536187,"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."}}