{"id":"W2137650880","doi":"10.1109/iembs.2004.1403143","title":"Time-frequency analysis of visual evoked potentials by means of matching pursuit with chirplet atoms","year":2005,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Time–frequency analysis; Matching pursuit; Short-time Fourier transform; Computer science; Speech recognition; SIGNAL (programming language); Evoked potential; Harmonics; Artificial intelligence; Fourier transform; Visual evoked potentials; Pattern recognition (psychology); Fundamental frequency; Harmonic; Computer vision; Fourier analysis; Acoustics; Mathematics; Physics; Psychology; Neuroscience","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003077172,0.0004012094,0.0003116546,0.0007761329,0.0001964745,0.0003009831,0.000324951,0.000376966,0.0008885885],"category_scores_gemma":[0.001286386,0.0001428775,0.0002954246,0.000695309,0.0002578756,0.0005837395,0.0004684987,0.0003709354,0.0002592986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001329495,"about_ca_system_score_gemma":0.0002384983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004306991,"about_ca_topic_score_gemma":0.0004517802,"domain_scores_codex":[0.9998266,0.00004753033,0.000008994491,0.00003432262,0.00007222629,0.00001037766],"domain_scores_gemma":[0.9997484,0.0001230471,0.00003341798,0.00003358312,0.0000493168,0.00001225979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004295912,0.00008534325,0.001337714,0.0002064706,0.00008080729,0.0001855643,0.0001281219,0.03509953,0.3916199,0.01218471,0.00058803,0.5580543],"study_design_scores_gemma":[0.00002439223,0.0001824677,0.003311018,0.0000120131,0.00002454939,0.0003559708,0.0000247487,0.9253294,0.06324735,0.005496928,0.001967547,0.0000235978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0463522,0.0001639622,0.9523404,0.00005462304,0.00001669447,0.00004426986,0.00003607275,0.0002322264,0.0007594268],"genre_scores_gemma":[0.2747137,0.0002798787,0.723578,0.00003114463,0.00003043471,0.00009629151,0.0001247542,0.0000545237,0.001091303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008885885,"threshold_uncertainty_score":0.002972603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00637468010022912,"score_gpt":0.2570111403888191,"score_spread":0.25063646028859,"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."}}