{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhang C"],"funding":["Innovation Team Project of Guangdong Provincial Department of Education","STI 2030-Major Projects of the Ministry of Science and Technology of China","National Key Research and Development Program of China"],"pagination":["e0286742"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10793883"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["19(1)"],"pubmed_abstract":["Brain machine interfaces (BMI) connect brains directly to the outside world, bypassing natural neural systems and actuators. Neuronal-activity-to-motion transformation algorithms allow applications such as control of prosthetics or computer cursors. These algorithms lie within a spectrum between bio-mimetic control and bio-feedback control. The bio-mimetic approach relies on increasingly complex algorithms to decode neural activity by mimicking the natural neural system and actuator relationship while focusing on machine learning: the supervised fitting of decoder parameters. On the other hand, the bio-feedback approach uses simple algorithms and relies primarily on user learning, which may take some time, but can facilitate control of novel, non-biological appendages. An increasing amount"],"journal":["PloS one"],"pubmed_title":["Rhesus monkeys learn to control a directional-key inspired brain machine interface via bio-feedback."],"pmcid":["PMC10793883"],"funding_grant_id":["2020YFC0832402","2021ZD0200407","2021KCXTD014"],"pubmed_authors":["Li Z","Tang S","Wang H","Zhang C"],"additional_accession":[]},"is_claimable":false,"name":"Rhesus monkeys learn to control a directional-key inspired brain machine interface via bio-feedback.","description":"Brain machine interfaces (BMI) connect brains directly to the outside world, bypassing natural neural systems and actuators. Neuronal-activity-to-motion transformation algorithms allow applications such as control of prosthetics or computer cursors. These algorithms lie within a spectrum between bio-mimetic control and bio-feedback control. The bio-mimetic approach relies on increasingly complex algorithms to decode neural activity by mimicking the natural neural system and actuator relationship while focusing on machine learning: the supervised fitting of decoder parameters. On the other hand, the bio-feedback approach uses simple algorithms and relies primarily on user learning, which may take some time, but can facilitate control of novel, non-biological appendages. An increasing amount","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2026-06-03T15:09:10.081Z","creation":"2025-04-21T16:57:37.068Z"},"accession":"S-EPMC10793883","cross_references":{"pubmed":["38232123"],"doi":["10.1371/journal.pone.0286742"]}}