{"id":"W4317930160","doi":"10.3389/frai.2022.1072801","title":"Trends in EEG signal feature extraction applications","year":2023,"lang":"en","type":"review","venue":"Frontiers in Artificial Intelligence","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":149,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Feature extraction; Pipeline (software); Electroencephalography; SIGNAL (programming language); Domain (mathematical analysis); Signal processing; Artificial intelligence; Focus (optics); Feature (linguistics); Frequency domain; Pattern recognition (psychology); Time domain; Speech recognition; Machine learning; Computer vision; Digital signal processing","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004108104,0.000452275,0.001035908,0.00176229,0.0001115703,0.0001729495,0.000980694,0.0004630697,0.00007493632],"category_scores_gemma":[0.0001205455,0.0004244754,0.0003178694,0.003561708,0.000185874,0.0002126632,0.0001347558,0.001243454,0.0004021336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000290048,"about_ca_system_score_gemma":0.0001049535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002577197,"about_ca_topic_score_gemma":0.0001260577,"domain_scores_codex":[0.9967151,0.0003310232,0.0009743256,0.001095792,0.0003362113,0.0005475054],"domain_scores_gemma":[0.998652,0.0004335047,0.0003398642,0.0004585857,0.00002325702,0.00009276475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007815888,0.0001015461,0.000003481335,0.000611912,0.000006851732,0.000048711,0.0001311724,0.0001708863,0.00002826366,0.001021447,0.004077889,0.99379],"study_design_scores_gemma":[0.00001506753,0.00004154409,0.000002387335,0.002887688,0.00004186364,0.00002377118,0.0001464832,0.004260299,0.001237515,0.00695997,0.9838465,0.0005368948],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000007089235,0.8970858,0.09618889,0.0001884615,0.003429696,0.001169445,0.0001023643,0.0002658459,0.001562374],"genre_scores_gemma":[0.0001956638,0.9940221,0.001965478,0.00006495797,0.0003625661,0.0005535345,0.00004904519,0.00007671043,0.002709966],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9932531,"threshold_uncertainty_score":0.9998207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.130808365683402,"score_gpt":0.393486768384004,"score_spread":0.2626784027006021,"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."}}