{"id":"W1989245491","doi":"10.1088/1741-2560/10/4/046018","title":"Dynamic topographical pattern classification of multichannel prefrontal NIRS signals","year":2013,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Holland Bloorview Kids Rehabilitation Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pattern recognition (psychology); Computer science; Artificial intelligence; Linear discriminant analysis; Brain–computer interface; Classifier (UML); Brain activity and meditation; Majority rule; Motor imagery; Linear classifier; Feature extraction; Electroencephalography; Neuroscience; Psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.00009532884,0.00009420509,0.0002588409,0.0001772231,0.00001007645,0.00001387672,0.00007401103,0.00005281273,0.00003849836],"category_scores_gemma":[0.00008895406,0.00007204364,0.000136176,0.00009776758,0.00002809774,0.0001497184,0.00001278622,0.00030539,0.00000187925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003156546,"about_ca_system_score_gemma":0.00001169943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009584614,"about_ca_topic_score_gemma":2.208621e-7,"domain_scores_codex":[0.9991799,0.000009736194,0.0003859527,0.00007262477,0.0002091638,0.0001426463],"domain_scores_gemma":[0.9994621,0.00004809544,0.0001263998,0.00009620304,0.0001498221,0.0001173287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001705729,0.00008269965,0.00252479,0.00007098259,0.00003736978,0.00001762947,0.000058696,0.0004534452,0.9931439,0.00001298485,0.00008906823,0.003491422],"study_design_scores_gemma":[0.0005584595,0.0007403811,0.1897858,0.0002850924,0.00007539193,0.000339843,0.00008592551,0.7655344,0.04241097,0.00004613791,0.00002454232,0.0001130331],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9655086,0.0001416552,0.0331334,0.0009083782,0.0001115849,0.0001120314,8.88417e-7,0.00003767127,0.0000458338],"genre_scores_gemma":[0.9882447,0.00002825225,0.01159583,0.00004318361,0.00004746415,0.000003274912,0.000001023167,0.00001533883,0.00002092285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9507329,"threshold_uncertainty_score":0.2937855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01105413542357711,"score_gpt":0.272948814596435,"score_spread":0.2618946791728579,"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."}}