{"id":"W2012424930","doi":"10.1016/j.jneumeth.2014.06.004","title":"A wavelet based algorithm for the identification of oscillatory event-related potential components","year":2014,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University","funders":"","keywords":"Wavelet; Computer science; Electroencephalography; Pattern recognition (psychology); Algorithm; Asymmetry; Property (philosophy); Component (thermodynamics); Artificial intelligence; Identification (biology); Matching (statistics); Extension (predicate logic); Event-related potential; Cascade algorithm; Wavelet transform; Wavelet packet decomposition; Mathematics; Psychology; Statistics","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":[0.005103155,0.0001454531,0.0002913608,0.0002196991,0.0002703137,0.0001110295,0.001080398,0.00005491524,0.000005106126],"category_scores_gemma":[0.00227268,0.00009417158,0.0002659289,0.0004751002,0.0003957243,0.0002953579,0.00007882941,0.0002593276,0.000001190625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002440383,"about_ca_system_score_gemma":0.00007907636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001563889,"about_ca_topic_score_gemma":6.678158e-8,"domain_scores_codex":[0.9966268,0.001160584,0.001009597,0.0003112567,0.0006317669,0.0002599837],"domain_scores_gemma":[0.9962147,0.001827916,0.001329569,0.0003134329,0.0002163756,0.00009795815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000018915,0.00007745281,0.000006822781,0.000008835753,0.000002202822,0.000002264114,0.00005775211,0.001153915,0.8536074,0.0000712846,0.00008052665,0.1449127],"study_design_scores_gemma":[0.0003709599,0.0002394456,0.00216772,0.00001791752,0.00001985337,0.00008554505,0.000009067683,0.5415034,0.4533865,0.0004037844,0.001733586,0.00006225572],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1189628,0.00004131193,0.8765605,0.0007450363,0.003450409,0.0002013807,0.000009731226,0.00001289344,0.0000159395],"genre_scores_gemma":[0.8881072,0.00001662852,0.110802,0.000822216,0.0001219045,0.000003927084,1.864415e-7,0.00001433213,0.0001115877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7691444,"threshold_uncertainty_score":0.3840206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05447556795556949,"score_gpt":0.3689259280488388,"score_spread":0.3144503600932693,"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."}}