{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001911911,0.0009481284,0.0009068604,0.003212797,0.000293614,0.001626139,0.001013445,0.001267183,0.003636016],"category_scores_gemma":[0.003350755,0.0004153338,0.0009095953,0.003740499,0.0006176538,0.002434007,0.0007275011,0.001743206,0.002638471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008340148,"about_ca_system_score_gemma":0.00151469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001595911,"about_ca_topic_score_gemma":0.001413352,"domain_scores_codex":[0.9993312,0.00009700657,0.0001096573,0.0001829864,0.0002421461,0.00003704605],"domain_scores_gemma":[0.9975618,0.001182,0.0001754509,0.0000674648,0.0009575121,0.00005572881],"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":[0.00007546078,0.00006362118,0.0006207703,0.01293117,0.00008931124,0.0001727595,0.0001189612,0.0005534076,0.002640403,0.008111182,0.01145355,0.9631695],"study_design_scores_gemma":[0.0000240779,0.0003842564,0.005232206,0.006901172,0.0002097641,0.003145471,0.0002312595,0.00111639,0.003560377,0.009148586,0.9699686,0.0000778166],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001107189,0.9874856,0.003615796,0.00162119,0.0004498795,0.00003208398,0.0001123984,0.00005513829,0.005520712],"genre_scores_gemma":[0.004726333,0.987775,0.004062188,0.0007934348,0.0005596093,0.00005517239,0.0001955733,0.00001869204,0.001813996],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003636016,"threshold_uncertainty_score":0.01216364,"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."}}