{"id":"W2536427815","doi":"10.1016/j.neuroimage.2016.10.031","title":"Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open-source TESA software","year":2016,"lang":"en","type":"review","venue":"NeuroImage","topic":"Transcranial Magnetic Stimulation Studies","field":"Neuroscience","cited_by":432,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"Medical Research Council; National Health and Medical Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Electroencephalography; Transcranial magnetic stimulation; Computer science; Artifact (error); Open source; SIGNAL (programming language); Speech recognition; Artificial intelligence; Software; Neuroscience; Psychology; Stimulation","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":[0.0006896668,0.0005988919,0.001509668,0.0002682718,0.0005809081,0.0004231072,0.00122349,0.00009704682,0.0001059074],"category_scores_gemma":[0.002213669,0.0003815633,0.0001407237,0.001324679,0.0003775133,0.0004604773,0.0006272001,0.0005325224,0.0000328238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002678953,"about_ca_system_score_gemma":0.0000748077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001373688,"about_ca_topic_score_gemma":0.00001670788,"domain_scores_codex":[0.9952093,0.00119253,0.0008310956,0.001882479,0.0004342683,0.0004502862],"domain_scores_gemma":[0.9968646,0.001354427,0.0003195534,0.001219574,0.00008308476,0.0001588252],"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":[0.00001159716,0.00003013287,0.000006004825,0.004208438,0.00001648084,0.000005950834,0.00004176788,0.000002024603,0.00029252,0.00003909991,0.001569459,0.9937765],"study_design_scores_gemma":[0.0004784082,0.0003079007,0.0003607125,0.003774781,0.002160656,0.0001695624,0.000001738737,0.00005673541,0.00000102037,0.00002311506,0.9922628,0.000402586],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002259653,0.9871464,0.004791109,0.003347034,0.0003427068,0.003895847,0.0003003102,0.0001195035,0.00003446036],"genre_scores_gemma":[0.0004216339,0.9973693,0.0002227287,0.001081835,0.0004941802,0.0001502893,0.00003991956,0.00007685927,0.0001431991],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9933739,"threshold_uncertainty_score":0.9998636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09918977948365859,"score_gpt":0.3690177610624745,"score_spread":0.2698279815788159,"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."}}