{"id":"W4386304006","doi":"10.1101/2023.08.25.554741","title":"PyHFO: Lightweight Deep Learning-powered End-to-End High-Frequency Oscillations Analysis Application","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"UCB Pharma; Greenwich Biosciences; SENSHIN Medical Research Foundation; Children's Discovery and Innovation Institute, University of California, Los Angeles; GW Pharmaceuticals; Upsher-Smith; H. Lundbeck A/S; Zogenix; National Institute of Neurological Disorders and Stroke; Eisai; Sunovion","keywords":"Computer science; Deep learning; Electroencephalography; Ictal; Artificial intelligence; Artifact (error); Context (archaeology); Software; Machine learning; Neuroscience","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007219311,0.001263364,0.0005738389,0.000719292,0.0002455364,0.0006933348,0.001972496,0.0006164864,0.01625259],"category_scores_gemma":[0.002035609,0.0005449964,0.0005410982,0.0003655993,0.0003892697,0.001135891,0.001781047,0.001052046,0.004491803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005561832,"about_ca_system_score_gemma":0.0009826917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003038085,"about_ca_topic_score_gemma":0.003418651,"domain_scores_codex":[0.9995993,0.0000363941,0.0000208875,0.000123821,0.0001511914,0.00006843562],"domain_scores_gemma":[0.9994634,0.0001762288,0.00006164488,0.0001017738,0.0001208066,0.00007618176],"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":[0.004648086,0.0006800017,0.009145168,0.0009070875,0.0005176255,0.001355482,0.0004955721,0.06051778,0.09377734,0.006144889,0.2448795,0.5769315],"study_design_scores_gemma":[0.0004832145,0.0003820187,0.007364893,0.00009747587,0.00006828381,0.000476623,0.00006036309,0.8427698,0.09517168,0.009209049,0.04371742,0.000199173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.03213742,0.0002786031,0.6783978,0.000338683,0.0002004861,0.0004489517,0.004821326,0.2781878,0.005188891],"genre_scores_gemma":[0.5741857,0.0004797681,0.3656529,0.001451883,0.0001410062,0.001459935,0.0168073,0.01607175,0.02374983],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01625259,"threshold_uncertainty_score":0.05437028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01940115352935951,"score_gpt":0.245132621689612,"score_spread":0.2257314681602525,"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."}}