{"id":"W4307988660","doi":"10.32920/21428727","title":"Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification","year":2022,"lang":"en","type":"preprint","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Mitacs","keywords":"Electroencephalography; Ictal; Principal component analysis; Pattern recognition (psychology); Computer science; Epilepsy; Artificial intelligence; Feature extraction; SIGNAL (programming language); Feature (linguistics); Window (computing); Epileptic seizure; Speech recognition; Psychology; Neuroscience","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006310145,0.0005395866,0.000484595,0.001307621,0.0002162323,0.0004871956,0.0003062407,0.0004018636,0.001283739],"category_scores_gemma":[0.002148897,0.0001791395,0.000560029,0.002225517,0.0003554989,0.0004538739,0.0003203957,0.0005080851,0.0005000992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001370143,"about_ca_system_score_gemma":0.0003204788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001734738,"about_ca_topic_score_gemma":0.001287369,"domain_scores_codex":[0.9995703,0.0001163558,0.00003430038,0.00009156611,0.0001592578,0.00002807075],"domain_scores_gemma":[0.9993961,0.0003205425,0.00005611425,0.00007761096,0.0001329519,0.0000167105],"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":[0.0001318611,0.00008093648,0.0010768,0.0001928022,0.0001010854,0.000205348,0.00009190689,0.04275467,0.05289064,0.005647966,0.002108719,0.8947173],"study_design_scores_gemma":[0.00001260642,0.0001456738,0.008794555,0.00003179052,0.00006635756,0.0004601355,0.00004661541,0.9510887,0.02618041,0.005243666,0.007882583,0.00004698281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02704229,0.002833438,0.9682544,0.0002005288,0.0001107139,0.00004739243,0.0001007296,0.0004645263,0.000945956],"genre_scores_gemma":[0.2784914,0.00493655,0.7127189,0.00005612389,0.0002177384,0.0001266508,0.0004902756,0.0001701316,0.002792327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001734738,"threshold_uncertainty_score":0.004294515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03988671358502826,"score_gpt":0.3002640979221644,"score_spread":0.2603773843371361,"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."}}