{"id":"W4385080006","doi":"10.1109/iscas46773.2023.10181356","title":"Design of a Sleep Modulation System with FPGA-Accelerated Deep Learning for Closed-loop Stage-Specific In-Phase Auditory Stimulation","year":2023,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of Neurological Disorders and Stroke","keywords":"Computer science; Field-programmable gate array; Convolutional neural network; Sleep (system call); Artificial intelligence; Deep learning; Speech recognition; Computer hardware","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":[0.0001845536,0.0005562983,0.0003777951,0.0003034452,0.0002004677,0.0004355894,0.00120565,0.0003778243,0.004897489],"category_scores_gemma":[0.0002869824,0.0002284261,0.0002974427,0.0001496398,0.0001511849,0.0003523508,0.000439984,0.0003618513,0.001218826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004095714,"about_ca_system_score_gemma":0.0006435577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001481941,"about_ca_topic_score_gemma":0.002543721,"domain_scores_codex":[0.9998636,0.00001446144,0.00001177345,0.00004264058,0.00003880644,0.00002860949],"domain_scores_gemma":[0.9998943,0.00002125907,0.00001420674,0.00001095525,0.00004073953,0.00001857477],"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":[0.0009853219,0.0007694298,0.006077468,0.0008105508,0.0002368689,0.0008406224,0.0002815677,0.09760351,0.2633897,0.003981757,0.01255348,0.6124697],"study_design_scores_gemma":[0.0002012053,0.001091915,0.0041402,0.00007486238,0.000129454,0.0005982172,0.00004164248,0.9051183,0.0729864,0.001691506,0.01386034,0.00006582747],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07156297,0.0005467088,0.9082213,0.0004658661,0.0003080074,0.0006511351,0.0002864509,0.007736842,0.0102207],"genre_scores_gemma":[0.8816649,0.0001985752,0.1112508,0.0005233946,0.00005989265,0.0005484369,0.0002153923,0.0001330184,0.00540551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004897489,"threshold_uncertainty_score":0.01638371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08304653544200329,"score_gpt":0.3070722782017378,"score_spread":0.2240257427597345,"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."}}