{"id":"W4384829332","doi":"10.3390/e25071086","title":"Probing Intrinsic Neural Timescales in EEG with an Information-Theory Inspired Approach: Permutation Entropy Time Delay Estimation (PE-TD)","year":2023,"lang":"en","type":"article","venue":"Entropy","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Ottawa Mental Health Centre; University of Ottawa","funders":"Università degli Studi di Padova; National Natural Science Foundation of China; Shanghai Center for Brain Science and Brain-Inspired Technology","keywords":"Permutation (music); Entropy estimation; Entropy (arrow of time); Electroencephalography; Statistical physics; Information theory; Artificial intelligence; Computer science; Artificial neural network; Transfer entropy; Mathematics; Algorithm; Pattern recognition (psychology); Principle of maximum entropy; Physics; Statistics; Psychology; Neuroscience; Quantum mechanics; Estimator","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":[0.0006033243,0.0004364532,0.0002128537,0.0008086925,0.0001598936,0.0004902493,0.0002900007,0.0003500838,0.0006633925],"category_scores_gemma":[0.003603536,0.0001301524,0.0003055656,0.0006186418,0.000420368,0.00105437,0.0006138708,0.0006130502,0.0001353757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001435154,"about_ca_system_score_gemma":0.0002418364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000401036,"about_ca_topic_score_gemma":0.0004171259,"domain_scores_codex":[0.9998515,0.00004380817,0.00001272947,0.00003962342,0.0000402936,0.00001202554],"domain_scores_gemma":[0.9991751,0.0004835361,0.00014493,0.00009899443,0.00005951133,0.00003776466],"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":[0.0006278748,0.0001896129,0.02497458,0.0006795932,0.000341332,0.0006742331,0.0004954704,0.2281749,0.2133929,0.07045408,0.002191006,0.4578044],"study_design_scores_gemma":[0.00001238634,0.0001732409,0.01637667,0.00003759783,0.00005567267,0.0004951632,0.00006302851,0.9180738,0.02530785,0.03686483,0.002462727,0.00007705833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1000738,0.0007768624,0.8967273,0.0001795346,0.00007808618,0.00003281998,0.0002729565,0.0002277192,0.001631047],"genre_scores_gemma":[0.8297128,0.0008603625,0.167915,0.00007849832,0.000159457,0.00005523679,0.0004060662,0.00007017933,0.0007424704],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008086925,"threshold_uncertainty_score":0.003190696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.013848795048466,"score_gpt":0.2278916615803322,"score_spread":0.2140428665318662,"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."}}