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Liang F, Yu S, Pang S, Wang X, Jie J, Gao F, Song Z, Li B, Liao WH, Yin M. Non-human primate models and systems for gait and neurophysiological analysis. Front Neurosci 2023; 17:1141567. [PMID: 37188006 PMCID: PMC10175625 DOI: 10.3389/fnins.2023.1141567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
Brain-computer interfaces (BCIs) have garnered extensive interest and become a groundbreaking technology to restore movement, tactile sense, and communication in patients. Prior to their use in human subjects, clinical BCIs require rigorous validation and verification (V&V). Non-human primates (NHPs) are often considered the ultimate and widely used animal model for neuroscience studies, including BCIs V&V, due to their proximity to humans. This literature review summarizes 94 NHP gait analysis studies until 1 June, 2022, including seven BCI-oriented studies. Due to technological limitations, most of these studies used wired neural recordings to access electrophysiological data. However, wireless neural recording systems for NHPs enabled neuroscience research in humans, and many on NHP locomotion, while posing numerous technical challenges, such as signal quality, data throughout, working distance, size, and power constraint, that have yet to be overcome. Besides neurological data, motion capture (MoCap) systems are usually required in BCI and gait studies to capture locomotion kinematics. However, current studies have exclusively relied on image processing-based MoCap systems, which have insufficient accuracy (error: ≥4° and 9 mm). While the role of the motor cortex during locomotion is still unclear and worth further exploration, future BCI and gait studies require simultaneous, high-speed, accurate neurophysiological, and movement measures. Therefore, the infrared MoCap system which has high accuracy and speed, together with a high spatiotemporal resolution neural recording system, may expand the scope and improve the quality of the motor and neurophysiological analysis in NHPs.
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Affiliation(s)
- Fengyan Liang
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
- Department of Rehabilitation Medicine, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
| | - Shanshan Yu
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Siqi Pang
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Xiao Wang
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Jing Jie
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Fei Gao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhenhua Song
- Department of Rehabilitation Medicine, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
| | - Binbin Li
- Department of Rehabilitation Medicine, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
| | - Wei-Hsin Liao
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, China
| | - Ming Yin
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
- *Correspondence: Ming Yin,
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Marshall JD, Li T, Wu JH, Dunn TW. Leaving flatland: Advances in 3D behavioral measurement. Curr Opin Neurobiol 2022; 73:102522. [PMID: 35453000 DOI: 10.1016/j.conb.2022.02.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/25/2022] [Accepted: 02/02/2022] [Indexed: 01/10/2023]
Abstract
Animals move in three dimensions (3D). Thus, 3D measurement is necessary to report the true kinematics of animal movement. Existing 3D measurement techniques draw on specialized hardware, such as motion capture or depth cameras, as well as deep multi-view and monocular computer vision. Continued advances at the intersection of deep learning and computer vision will facilitate 3D tracking across more anatomical features, with less training data, in additional species, and within more natural, occlusive environments. 3D behavioral measurement enables unique applications in phenotyping, investigating the neural basis of behavior, and designing artificial agents capable of imitating animal behavior.
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Affiliation(s)
- Jesse D Marshall
- Harvard University, Department of Organismic and Evolutionary Biology, Cambridge, MA 02138, USA.
| | - Tianqing Li
- Duke University, Pratt School of Engineering, Department of Biomedical Engineering, Durham, NC 27708, USA. https://twitter.com/tianqingxli
| | - Joshua H Wu
- Duke University, Pratt School of Engineering, Department of Biomedical Engineering, Durham, NC 27708, USA
| | - Timothy W Dunn
- Duke University, Pratt School of Engineering, Department of Biomedical Engineering, Durham, NC 27708, USA.
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