{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001449438,0.000120365,0.0002215399,0.0001654436,0.0001089909,0.00005707359,0.00008580332,0.0000633599,0.00007916207],"category_scores_gemma":[0.00007857654,0.0001173005,0.000210002,0.0005125556,0.00004843776,0.0001454946,0.00003554097,0.0001189214,0.000009288655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005870348,"about_ca_system_score_gemma":0.00004701149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002646012,"about_ca_topic_score_gemma":0.000007999982,"domain_scores_codex":[0.9987956,0.0001134452,0.0002619295,0.0003822769,0.000274473,0.0001722397],"domain_scores_gemma":[0.9991018,0.0003961488,0.0001772575,0.0001318855,0.0001437304,0.00004918496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001748277,0.0004237345,0.001690767,0.00005987793,0.0001853995,0.00002646294,0.0007234956,0.0917578,0.8948781,0.005760089,0.001736305,0.002583163],"study_design_scores_gemma":[0.001248467,0.0002831547,0.2651744,0.00003033525,0.0001940138,0.00009368186,0.0001852538,0.2080217,0.5172136,0.005129167,0.002101998,0.0003241165],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6242312,0.00001981769,0.3744883,0.0004069622,0.0004273709,0.0001200869,0.00003043698,0.00003215015,0.0002436336],"genre_scores_gemma":[0.9848256,0.000001829033,0.01447454,0.0001972136,0.0001071418,0.000008640379,0.0001464455,0.000009752393,0.0002288364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3776644,"threshold_uncertainty_score":0.4783376,"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."}}