{"id":"W4402630720","doi":"10.1101/2024.09.15.613144","title":"Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Electroencephalography; Filter bank; Brain–computer interface; Spatial filter; Envelope (radar); Computer science; Filter (signal processing); Pattern recognition (psychology); Artificial intelligence; Speech recognition; Computer vision; Psychology; Neuroscience; Telecommunications","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":[0.0005673078,0.0003546953,0.0002745638,0.0007920408,0.0001125386,0.0003921204,0.000180848,0.000333425,0.001091347],"category_scores_gemma":[0.002037243,0.00008380738,0.0002554111,0.0008471645,0.0001788371,0.0004333327,0.0002865578,0.0002282674,0.0002823801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002017838,"about_ca_system_score_gemma":0.0002181254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001555429,"about_ca_topic_score_gemma":0.001411259,"domain_scores_codex":[0.9996547,0.0000837662,0.00002461205,0.00006574126,0.0001333552,0.00003784525],"domain_scores_gemma":[0.9996421,0.0001715077,0.00004270825,0.00002364005,0.0001032384,0.00001681808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00133466,0.000282463,0.009799709,0.0002591753,0.0001116906,0.0003904139,0.00009542878,0.03818191,0.1684632,0.001062359,0.001876424,0.7781426],"study_design_scores_gemma":[0.00004417512,0.0005091316,0.07349212,0.00003887651,0.0001183686,0.000688972,0.0000760957,0.841337,0.08072457,0.001239549,0.001692796,0.00003833165],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5731937,0.001255906,0.4216151,0.0002006985,0.00007558086,0.0001140407,0.0003729061,0.0007204679,0.002451572],"genre_scores_gemma":[0.9513118,0.0002451457,0.04743326,0.00002685975,0.00001669847,0.00003978111,0.0002235453,0.00001509968,0.0006879065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001555429,"threshold_uncertainty_score":0.003650963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01444755604574091,"score_gpt":0.2392460133193669,"score_spread":0.224798457273626,"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."}}