{"id":"W4389077922","doi":"10.1109/sensors56945.2023.10325200","title":"Signal Decomposition Method with Sensor-Fusion for Reducing Motion Artifacts in Intra-Oral EEG","year":2023,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Electroencephalography; SIGNAL (programming language); Computer vision; Independent component analysis; Pattern recognition (psychology); Discrete wavelet transform; Wavelet; Sensor fusion; Motion (physics); MATLAB; Wavelet transform; Medicine","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":[0.0002937165,0.0005103462,0.0003243969,0.0004531978,0.0001389024,0.0003354533,0.0002799701,0.0003975646,0.0009933496],"category_scores_gemma":[0.0007670139,0.0001456288,0.0005006653,0.0006087283,0.0002108272,0.0005587973,0.0003530885,0.00044676,0.0003158531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001299225,"about_ca_system_score_gemma":0.0002871912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000571405,"about_ca_topic_score_gemma":0.0008668417,"domain_scores_codex":[0.9998102,0.00003307766,0.00001603598,0.00004092215,0.00008716332,0.00001266341],"domain_scores_gemma":[0.9998913,0.00002911747,0.00001689126,0.00001519608,0.00004202356,0.000005418175],"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":[0.0003244332,0.00008388799,0.001200872,0.0003081874,0.00008500337,0.0001924806,0.0001808264,0.02764286,0.2785303,0.003001914,0.001040415,0.6874088],"study_design_scores_gemma":[0.00003976929,0.0005170681,0.01111892,0.00006181803,0.0001403316,0.0008576288,0.0001220195,0.8192919,0.1550545,0.003350894,0.009390351,0.0000547015],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03643366,0.000510191,0.9618276,0.00008402083,0.00005993747,0.0000483085,0.00004960799,0.0002524834,0.0007342071],"genre_scores_gemma":[0.3119611,0.0009400629,0.6848415,0.00008032272,0.00004970562,0.000110833,0.0002244581,0.00006411732,0.001727969],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009933496,"threshold_uncertainty_score":0.003323078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04742976690129388,"score_gpt":0.3412883169756225,"score_spread":0.2938585500743287,"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."}}