{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00133181,0.001004459,0.0007422341,0.002394477,0.0002939535,0.001321045,0.0009373887,0.001724315,0.005416191],"category_scores_gemma":[0.002607207,0.0004630121,0.0008178268,0.003253107,0.0009625656,0.002159644,0.0008576375,0.002676204,0.002205225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006508038,"about_ca_system_score_gemma":0.001286468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001113919,"about_ca_topic_score_gemma":0.0009075531,"domain_scores_codex":[0.9993445,0.0001345552,0.00008219221,0.0001397692,0.0002658346,0.00003316039],"domain_scores_gemma":[0.997777,0.001458269,0.0001415684,0.00007474457,0.0004919982,0.00005641392],"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":[0.00007052322,0.0000912633,0.0003677238,0.01566014,0.00009746331,0.0001850381,0.0001401384,0.002218527,0.004639144,0.03715097,0.02005116,0.919328],"study_design_scores_gemma":[0.00002265979,0.000311015,0.001787866,0.006193258,0.0001387093,0.002005734,0.0001583266,0.004406881,0.003606401,0.02734814,0.9539337,0.00008725065],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0006608501,0.9805712,0.009072333,0.002238643,0.0007294769,0.00002921302,0.00006827833,0.00004967368,0.006580315],"genre_scores_gemma":[0.004502684,0.9872055,0.005145403,0.0006584222,0.0009158905,0.00003388435,0.00008877297,0.00001431101,0.001435173],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005416191,"threshold_uncertainty_score":0.01811892,"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."}}