{"id":"W4392345514","doi":"10.1016/j.jneumeth.2024.110101","title":"The impact of simultaneous intracranial recordings on scalp EEG: A finite element analysis","year":2024,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"SickKids Foundation; Mental Health Research Canada; Hospital for Sick Children; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Scalp; Electroencephalography; Waveform; Artifact (error); Stereoelectroencephalography; Epilepsy; Skull; Computer science; Epilepsy surgery; Biomedical engineering; Medicine; Neuroscience; Artificial intelligence; Psychology; Surgery","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":[0.0007475598,0.0006567498,0.000446426,0.0004562939,0.0003033957,0.0008061006,0.0005347382,0.001570245,0.002094648],"category_scores_gemma":[0.006059058,0.0004569185,0.0006887396,0.0002898401,0.0005852016,0.0009516775,0.0006768571,0.0007393837,0.0002650142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002552251,"about_ca_system_score_gemma":0.0006916032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003160713,"about_ca_topic_score_gemma":0.002918758,"domain_scores_codex":[0.9995282,0.0001397684,0.00002436574,0.0000638446,0.0002001743,0.00004378307],"domain_scores_gemma":[0.9965701,0.002865644,0.0001016065,0.0001258572,0.0002830745,0.00005365253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009704069,0.0002947329,0.00358123,0.0002727879,0.000138331,0.0004132324,0.0003556027,0.8639037,0.06679571,0.002478542,0.0007141588,0.06008157],"study_design_scores_gemma":[0.000011196,0.0001215766,0.001544634,0.00001573276,0.00002899441,0.0000671478,0.00005887929,0.9909411,0.006539571,0.0003590489,0.0003005719,0.00001156394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4476064,0.0004017986,0.5448385,0.0004614479,0.0002080563,0.00009910938,0.0002688913,0.0003965162,0.005719305],"genre_scores_gemma":[0.9520195,0.0002792619,0.04460023,0.00007810094,0.00005118689,0.00009701427,0.0001585033,0.0001242352,0.002592153],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003160713,"threshold_uncertainty_score":0.007007301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05162233949710607,"score_gpt":0.4205342763923807,"score_spread":0.3689119368952746,"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."}}