{"id":"W2164427480","doi":"10.1155/2011/406391","title":"PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction","year":2011,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":184,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Python (programming language); Computer science; Open source; Electroencephalography; Feature extraction; Artificial intelligence; Pattern recognition (psychology); Speech recognition; Software; Programming language; Neuroscience; Psychology","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":[0.0003446059,0.0001486969,0.0001515036,0.0001057055,0.0005490459,0.0005965637,0.001309708,0.00004707909,0.00001062126],"category_scores_gemma":[0.0000865289,0.0001348905,0.00004584646,0.000504815,0.0001518835,0.002226593,0.0004123265,0.0001155121,0.000006484571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001392262,"about_ca_system_score_gemma":0.00004807998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004676304,"about_ca_topic_score_gemma":0.000009750615,"domain_scores_codex":[0.9985202,0.0000441845,0.0002221997,0.0007033767,0.0002398291,0.0002702275],"domain_scores_gemma":[0.9991332,0.0001076693,0.000149712,0.000283488,0.0001734606,0.0001524979],"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":[0.00008345849,0.0004500362,0.001193604,0.0000321357,0.00001147814,0.00001745127,0.003237079,0.230576,0.005251775,0.2376936,0.0006399288,0.5208135],"study_design_scores_gemma":[0.00005374668,0.0003965585,0.008260951,0.00001164833,0.000005939562,0.00006124697,0.00008486455,0.9605107,0.003170928,0.02451728,0.002730973,0.0001951101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0118704,0.00005222491,0.9867673,0.0002762227,0.0002791549,0.000233125,0.000004212912,0.00008163159,0.0004357564],"genre_scores_gemma":[0.8802192,0.00001708118,0.1185073,0.0006777827,0.00003687389,0.00002157019,0.000005022199,0.00001022426,0.0005049687],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8683487,"threshold_uncertainty_score":0.5752678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.121298983739666,"score_gpt":0.3281203659125222,"score_spread":0.2068213821728563,"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."}}