{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001797368,0.0002372705,0.0001467567,0.0002092936,0.0001931555,0.0003789671,0.0007424994,0.0005045959,0.001559251],"category_scores_gemma":[0.00057813,0.000123381,0.0001347434,0.0001682788,0.0002895849,0.0005418259,0.0001958085,0.0003278748,0.0004382602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00036703,"about_ca_system_score_gemma":0.0003544434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006886133,"about_ca_topic_score_gemma":0.001287032,"domain_scores_codex":[0.9999019,0.00001448612,0.000008595832,0.00002262969,0.00004475531,0.000007649959],"domain_scores_gemma":[0.999868,0.00003993832,0.00001717737,0.00001980253,0.00004717729,0.000007798672],"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":[0.000198797,0.0001356832,0.001318115,0.000183833,0.00005178797,0.000229562,0.0001931219,0.1784267,0.4659391,0.04770104,0.002433313,0.3031888],"study_design_scores_gemma":[0.00003163498,0.0002307644,0.0007491013,0.00001832758,0.00002860583,0.0002834628,0.0000232924,0.8562536,0.1208603,0.007412434,0.01408228,0.00002632748],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02829234,0.000246729,0.9669387,0.0002453751,0.00005065494,0.00004268996,0.00003224006,0.0006367053,0.003514608],"genre_scores_gemma":[0.4466331,0.000300491,0.5445303,0.0002247532,0.00004046659,0.000121102,0.00007545116,0.00007648439,0.007997892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001559251,"threshold_uncertainty_score":0.005216241,"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."}}