{"id":"W3180057944","doi":"10.3390/computation9070078","title":"Wavelet Power Spectral Domain Functional Principal Component Analysis for Feature Extraction of Epileptic EEGs","year":2021,"lang":"en","type":"article","venue":"Computation","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pattern recognition (psychology); Feature extraction; Principal component analysis; Computer science; Artificial intelligence; Wavelet; Wavelet transform; Ictal; Feature (linguistics); Electroencephalography","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":[0.0006059533,0.0009910379,0.0005292529,0.001867687,0.0002573718,0.0004931561,0.0003294811,0.0003369931,0.002047129],"category_scores_gemma":[0.002211882,0.0001761039,0.000915808,0.002543664,0.0002442817,0.0006900046,0.000353804,0.0006345602,0.00103799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001763457,"about_ca_system_score_gemma":0.0004988674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001507409,"about_ca_topic_score_gemma":0.001358326,"domain_scores_codex":[0.9996884,0.00007862576,0.00002493495,0.00005411713,0.0001264422,0.00002751159],"domain_scores_gemma":[0.9996207,0.0001298732,0.00003601737,0.00005690057,0.0001443126,0.00001220818],"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":[0.0002848653,0.000173329,0.002243745,0.0004808877,0.0001832756,0.000353646,0.0001428092,0.06680714,0.1215366,0.009433057,0.005073614,0.7932871],"study_design_scores_gemma":[0.00002156533,0.0001311695,0.009815243,0.00004537293,0.0001005265,0.0003523674,0.00008155413,0.9373478,0.03550563,0.007630357,0.008912589,0.00005580029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02427239,0.0005620411,0.9724895,0.000114452,0.00006106787,0.00007836649,0.0004008981,0.0008674384,0.001153823],"genre_scores_gemma":[0.3673148,0.001652314,0.6263469,0.00005577494,0.00009552325,0.0002862265,0.001870236,0.0002905147,0.002087617],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002047129,"threshold_uncertainty_score":0.006848276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03187127253675393,"score_gpt":0.2934080696122023,"score_spread":0.2615367970754484,"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."}}