Brain-Machine Interface-Based Rat-Robot Behavior Control

被引:3
|
作者
Zhang, Jiacheng [1 ,2 ,3 ]
Xu, Kedi [1 ,2 ,3 ]
Zhang, Shaomin [1 ,2 ,3 ]
Wang, Yueming [1 ,4 ]
Zheng, Nenggan [1 ,4 ]
Pan, Gang [4 ]
Chen, Weidong [1 ,2 ,4 ]
Wu, Zhaohui [4 ]
Zheng, Xiaoxiang [1 ,2 ,3 ]
机构
[1] Zhejiang Univ, Qiushi Acad Adv Studies QAAS, Hangzhou, Peoples R China
[2] Zhejiang Univ, Minist Educ Minist, Key Lab, Dept Biomed Engn, Hangzhou, Peoples R China
[3] Zhejiang Univ, Zhejiang Prov Key Lab CardioCerebral Vasc Detect, Hangzhou, Peoples R China
[4] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China
关键词
Brain-machine interface; Rat-robot; Behavior control; Brain stimulation; AUTOMATIC NAVIGATION; MEDIAL NUCLEUS; REMOTE-CONTROL; STIMULATION; SYSTEM; FLIGHT; THALAMUS; ANIMALS; CORTEX; CYBORG;
D O I
10.1007/978-981-13-2050-7_5
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Brain-machine interface (BMI) provides a bidirectional pathway between the brain and external facilities. The machine-to-brain pathway makes it possible to send artificial information back into the biological brain, interfering neural activities and generating sensations. The idea of the BMI-assisted bio-robotic animal system is accomplished by stimulations on specific sites of the nervous system. With the technology of BMI, animals' locomotion behavior can be precisely controlled as robots, which made the animal turning into bio-robot. In this chapter, we reviewed our lab works focused on rat-robot navigation. The principles of rat-robot system have been briefly described first, including the target brain sites chosen for locomotion control and the design of remote control system. Some methodological advances made by optogenetic technologies for better modulation control have then been introduced. Besides, we also introduced our implementation of "mind-controlled" rat navigation system. Moreover, we have presented our efforts made on combining biological intelligence with artificial intelligence, with developments of automatic control and training system assisted with images or voices inputs. We concluded this chapter by discussing further developments to acquire environmental information as well as promising applications with write-in BMIs.
引用
收藏
页码:123 / 147
页数:25
相关论文
共 50 条
  • [21] Brain-machine interface control via reinforcement learning
    DiGiovanna, Jack
    Mahmoudi, Babak
    Mitzelfelt, Jeremiah
    Sanchez, Justin C.
    Principe, Jose C.
    2007 3RD INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, VOLS 1 AND 2, 2007, : 530 - +
  • [22] The Role of the Control Framework for Continuous Teleoperation of a Brain-Machine Interface-Driven Mobile Robot
    Tonin, Luca
    Bauer, Felix Christian
    Millan, Jose del R.
    IEEE TRANSACTIONS ON ROBOTICS, 2020, 36 (01) : 78 - 91
  • [23] Neurorehabilitation with brain-machine interface
    Shindo, Keiichiro
    Ushiba, Junichi
    Liu, Meigen
    NEUROSCIENCE RESEARCH, 2010, 68 : E45 - E45
  • [24] Brain-Machine Interface Control of a Robot Arm using Actor-Critic Reinforcement Learning
    Pohlmeyer, Eric A.
    Mahmoudi, Babak
    Geng, Shijia
    Prins, Noeine
    Sanchez, Justin C.
    2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2012, : 4108 - 4111
  • [25] Brain-Machine Interface Systems
    Trajkovic, Ljiljana
    IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE, 2020, 6 (03): : 4 - 8
  • [26] Standardizing the brain-machine interface
    Peck, Morgen E.
    IEEE SPECTRUM, 2008, 45 (04) : 16 - 16
  • [27] The Brain-Machine Interface, Unplugged
    Patel, Prachi
    IEEE SPECTRUM, 2009, 46 (10) : 13 - 14
  • [28] Translating the Brain-Machine Interface
    Thakor, Nitish V.
    SCIENCE TRANSLATIONAL MEDICINE, 2013, 5 (210)
  • [29] Brain-Machine Interface Based on EEG Mapping to Control an Assistive Robotic Arm
    Ubeda, Andres
    Azorin, Jose M.
    Garcia, Nicolas
    Sabater, Jose M.
    Perez, Carlos
    2012 4TH IEEE RAS & EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL ROBOTICS AND BIOMECHATRONICS (BIOROB), 2012, : 1311 - 1315
  • [30] Reinforcement Learning-based Kalman Filter for Adaptive Brain Control in Brain-Machine Interface
    Zhang, Xiang
    Song, Zhiwei
    Wang, Yiwen
    2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), 2021, : 6619 - 6622