{"id":"W2979480025","doi":"10.1109/embc.2019.8856308","title":"Feature Extraction of Epileptic EEG using Wavelet Power Spectra and Functional PCA","year":2019,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Principal component analysis; Pattern recognition (psychology); Feature extraction; Wavelet; Computer science; Electroencephalography; Artificial intelligence; Wavelet transform; Feature (linguistics); Epileptic seizure; Epilepsy; Functional principal component analysis; Speech recognition; 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":[0.0003952229,0.0008642729,0.0004649365,0.001984465,0.0001984624,0.0005273077,0.0003406128,0.0003024675,0.001090858],"category_scores_gemma":[0.001350984,0.0001705874,0.000647551,0.001565962,0.0002655676,0.0009538793,0.0003388571,0.0003960627,0.0005422204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009981641,"about_ca_system_score_gemma":0.0002233122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005455769,"about_ca_topic_score_gemma":0.0004902764,"domain_scores_codex":[0.9997707,0.00004963289,0.00002135397,0.00004306796,0.00009370735,0.00002155076],"domain_scores_gemma":[0.9996449,0.0001203015,0.00004573221,0.00004419976,0.0001309847,0.00001391171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002012054,0.0001368052,0.002612383,0.0003518039,0.0001518723,0.0004505706,0.0001011742,0.03013963,0.2202742,0.006882123,0.002089961,0.7366083],"study_design_scores_gemma":[0.00003384006,0.0002522173,0.0169473,0.00004492101,0.000164787,0.0009758688,0.0001303345,0.8897951,0.07106457,0.01283734,0.007672937,0.00008077789],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03647486,0.0005474581,0.9609011,0.0001023536,0.00006284613,0.00004594805,0.0002073569,0.0006119531,0.001046132],"genre_scores_gemma":[0.4356354,0.00158147,0.5595639,0.00006346025,0.0001949224,0.0001281845,0.001170817,0.0002063144,0.001455613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001984465,"threshold_uncertainty_score":0.003649294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02530801304878169,"score_gpt":0.2649268295160804,"score_spread":0.2396188164672987,"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."}}