{"id":"W2145208111","doi":"10.1109/iembs.2009.5333793","title":"A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders","year":2009,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; National Eye Institute; McGovern Institute for Brain Research, Massachusetts Institute of Technology; National Institutes of Health; Consortia for Improving Medicine with Innovation and Technology","keywords":"Computer science; Micropower; Artificial neural network; Decoding methods; Algorithm; Artificial intelligence; Power (physics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005993249,0.0001509983,0.0001552609,0.00008133823,0.0001143477,0.00009540922,0.0001698426,0.00004593937,0.0000213293],"category_scores_gemma":[0.00003417165,0.0001101583,0.00005259686,0.0001072029,0.00006911559,0.0001131878,0.00003794685,0.00009510366,0.000007231909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008796643,"about_ca_system_score_gemma":0.0000129092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009129022,"about_ca_topic_score_gemma":0.000004399721,"domain_scores_codex":[0.9990238,0.00003234695,0.0001292068,0.0003896993,0.00009984987,0.0003250935],"domain_scores_gemma":[0.9994673,0.0002621266,0.00003555838,0.000127873,0.00001615002,0.00009101914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003292392,0.0002507443,0.00002709695,0.00002106997,0.00002170858,0.00004326622,0.001791877,0.0005501334,0.5078734,0.003313274,0.01751895,0.4682593],"study_design_scores_gemma":[0.00148929,0.001932124,0.0003581628,0.00003487941,0.00001722306,0.0005087276,0.0001651384,0.1890985,0.7907584,0.01060433,0.004540646,0.0004925798],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6290665,0.0001361971,0.3614657,0.002321018,0.000467025,0.0007860912,0.00009096407,0.0002567354,0.005409816],"genre_scores_gemma":[0.9713433,0.000004686778,0.02373574,0.004242653,0.00004401565,0.000007815102,0.000001257877,0.00001003798,0.000610497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4677667,"threshold_uncertainty_score":0.4492124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01756092660288642,"score_gpt":0.2634173060628761,"score_spread":0.2458563794599896,"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."}}