The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatl...The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatly. Robot localization and decisionmaking are the most important cognitive processes during navigation. However, most of these algorithms are not efficient and are challenging tasks while robots navigate through complex environments. In this paper,we propose a biologically inspired method for robot decision-making, based on rat’s brain signals. Rodents accurately and rapidly navigate in complex spaces by localizing themselves in reference to the surrounding environmental landmarks. Firstly, we analyzed the rats’ strategies while navigating in the complex Y-maze, and recorded local field potentials(LFPs), simultaneously.The recorded LFPs were processed and different features were extracted which were used as the input in the artificial neural network(ANN) to predict the rat’s decision-making in each junction. The ANN performance was tested in a real robot and good performance is achieved. The implementation of our method on a real robot, demonstrates its abilities to imitate the rat’s decision-making and integrate the internal states with external sensors, in order to perform reliable navigation in complex maze.展开更多
The paper presents the neural decoding result of finger or wrist movements using the primary motor cortex(M1)neural activities prior to its movement.It is well known that the observations of motor commands in brain ar...The paper presents the neural decoding result of finger or wrist movements using the primary motor cortex(M1)neural activities prior to its movement.It is well known that the observations of motor commands in brain are in advance before motor movements in the central nerve system.Readiness potential(RP)for electroencephalogram(EEG)has become an important domain of research.Likewise,pre-movement neural responses in M1 primary motor cortex have been observed.The neural activity data before 1 s.were used for neural decoding when the actual movements happened around 1 s.The obtained decoding accuracy in novel method reaches as high as 95% with 30 randomly selected neurons.展开更多
We characterize the hemodynamic response changes near-infrared spectroscopy （NIRS） during the presentation of in the main olfactory bulb （MOB） of anesthetized rats with three different odorants： （i） plain air a...We characterize the hemodynamic response changes near-infrared spectroscopy （NIRS） during the presentation of in the main olfactory bulb （MOB） of anesthetized rats with three different odorants： （i） plain air as a reference （Blank）, （ii） 2-heptanone （HEP）, and （iii） isopropylbenzene （Ib）. Odorants generate different changes in the concentrations of oxy- hemoglobin. Our results suggest that NIRS technology might be useful in discriminating various odorants in a non-invasive manner using animals with a superb olfactory system.展开更多
脑机接口系统通过大脑—计算机接口技术和控制理论的组合来弥补由于肌体的受损部分而造成的信息缺失.本研究基于心理生理皮质神经元放电率电路模型,在脑机接口控制理论分析的基础上进行自发单关节运动任务,采用自适应ESN(echo state net...脑机接口系统通过大脑—计算机接口技术和控制理论的组合来弥补由于肌体的受损部分而造成的信息缺失.本研究基于心理生理皮质神经元放电率电路模型,在脑机接口控制理论分析的基础上进行自发单关节运动任务,采用自适应ESN(echo state network)设计非线性解码器,并引入FORCE(First Order Reduced and Contrdled Error learning)算法更新网络输出权值,通过仿真有无自然本体反馈信息情况下的解码器的性能来验证所设计的解码器的有效性.最后,通过基于遗传算法LS-SVM(least squares support vector machine)的直接逆模型框架,设计近似大脑皮层感觉区神经元放电率的最佳人工本体反馈去刺激大脑皮层感觉区神经元.仿真结果发现,所设计的闭环脑机接口(BMI)系统框架能够很好地恢复在线自发单关节自然运动任务性能,这也为当系统模型未知时,根据对象的输入输出数据恢复闭环系统的性能提供了新的研究思路.展开更多
This paper presents the first report of a system of human's speech interaction with rats via integration of brain–machine interfaces and automatic speech recognition technologies. We propose a novel human–rat sp...This paper presents the first report of a system of human's speech interaction with rats via integration of brain–machine interfaces and automatic speech recognition technologies. We propose a novel human–rat speech interaction paradigm by incorporating speech translator module, which translates human's speech commands into suitable electrical brain stimulation to steer the rat to induce expected locomotor behaviors. The preliminary results show that we can guide a rat's movement by speech commands. We further look into the future application scenarios together with forthcoming challenges facing this newly evolved cyborg intelligent system. This work will pave the way for natural interaction with animal robots.展开更多
基金supported by the Japanese Government,Grants-in-Aid for Scientific Research 2014 to 2016 under Grant No.26330296
文摘The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatly. Robot localization and decisionmaking are the most important cognitive processes during navigation. However, most of these algorithms are not efficient and are challenging tasks while robots navigate through complex environments. In this paper,we propose a biologically inspired method for robot decision-making, based on rat’s brain signals. Rodents accurately and rapidly navigate in complex spaces by localizing themselves in reference to the surrounding environmental landmarks. Firstly, we analyzed the rats’ strategies while navigating in the complex Y-maze, and recorded local field potentials(LFPs), simultaneously.The recorded LFPs were processed and different features were extracted which were used as the input in the artificial neural network(ANN) to predict the rat’s decision-making in each junction. The ANN performance was tested in a real robot and good performance is achieved. The implementation of our method on a real robot, demonstrates its abilities to imitate the rat’s decision-making and integrate the internal states with external sensors, in order to perform reliable navigation in complex maze.
基金MKE(the Ministry of Knowledge Economy),Korea,under the ITRC(Infor mation Technology Research Center)support program supervised by the NIPA(National ITIndustry Promotion Agency)(NIPA-2011-C1090-1121-0010)
文摘The paper presents the neural decoding result of finger or wrist movements using the primary motor cortex(M1)neural activities prior to its movement.It is well known that the observations of motor commands in brain are in advance before motor movements in the central nerve system.Readiness potential(RP)for electroencephalogram(EEG)has become an important domain of research.Likewise,pre-movement neural responses in M1 primary motor cortex have been observed.The neural activity data before 1 s.were used for neural decoding when the actual movements happened around 1 s.The obtained decoding accuracy in novel method reaches as high as 95% with 30 randomly selected neurons.
基金The MKE(The Ministry of Knowledge Economy),Korea,under the ITRC(Information Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2012-H0301-12-2006)Brain Research Center(BRC)(2012K001127),The MKE(10033634-2012-21)National Research Foundation of Korea(NRF)(2012-0005787)
文摘We characterize the hemodynamic response changes near-infrared spectroscopy （NIRS） during the presentation of in the main olfactory bulb （MOB） of anesthetized rats with three different odorants： （i） plain air as a reference （Blank）, （ii） 2-heptanone （HEP）, and （iii） isopropylbenzene （Ib）. Odorants generate different changes in the concentrations of oxy- hemoglobin. Our results suggest that NIRS technology might be useful in discriminating various odorants in a non-invasive manner using animals with a superb olfactory system.
文摘脑机接口系统通过大脑—计算机接口技术和控制理论的组合来弥补由于肌体的受损部分而造成的信息缺失.本研究基于心理生理皮质神经元放电率电路模型,在脑机接口控制理论分析的基础上进行自发单关节运动任务,采用自适应ESN(echo state network)设计非线性解码器,并引入FORCE(First Order Reduced and Contrdled Error learning)算法更新网络输出权值,通过仿真有无自然本体反馈信息情况下的解码器的性能来验证所设计的解码器的有效性.最后,通过基于遗传算法LS-SVM(least squares support vector machine)的直接逆模型框架,设计近似大脑皮层感觉区神经元放电率的最佳人工本体反馈去刺激大脑皮层感觉区神经元.仿真结果发现,所设计的闭环脑机接口(BMI)系统框架能够很好地恢复在线自发单关节自然运动任务性能,这也为当系统模型未知时,根据对象的输入输出数据恢复闭环系统的性能提供了新的研究思路.
基金supported by the National Basic Research Program of China (2013CB329504)
文摘This paper presents the first report of a system of human's speech interaction with rats via integration of brain–machine interfaces and automatic speech recognition technologies. We propose a novel human–rat speech interaction paradigm by incorporating speech translator module, which translates human's speech commands into suitable electrical brain stimulation to steer the rat to induce expected locomotor behaviors. The preliminary results show that we can guide a rat's movement by speech commands. We further look into the future application scenarios together with forthcoming challenges facing this newly evolved cyborg intelligent system. This work will pave the way for natural interaction with animal robots.
