Circuit design and simulation of a CMOS-based preamplifier for brain neural signals

A novel CMOS-based preamplifier for amplifying brain neural signal obtained by scalp electrodes in brain-computer interface (BCI) is presented in this paper. By means of constructing effective equivalent input circuit structure of the preamplifier, two capacitors of 5 pF are included to realize the DC suppression compared to conventional preamplifiers. Then this preamplifier is designed and simulated using the standard 0.6 mu m MOS process technology model parameters with a supply voltage of 5 volts. With differential input structures adopted, simulation results of the preamplifier show that the input impedance amounts to more than 2 Gohm with brain neural signal frequency of 0.5 Hz-100 Hz. The equivalent input noise voltage is 18 nV/Hz(1/2). The common mode rejection ratio (CMRR) of 112 dB and the open-loop differential gain of 90 dB are achieved.

A low power integrated CMOS transmitter for BCI system

The prototype wafer of a low power integrated CMOS Transmitter for short-range biotelemetry application has been designed and fabricated, which is prospective to be implanted in the human brain to transfer the extracted neural information to the external computer. The transmitter consists of five parts, a bandgap current regulator, a ring oscillator, a buffer, a modulator and a power transistor. High integration and low power are the most distinct criteria for such an implantable integrated circuit. The post-simulation results show that under a 3.3 V power supply the transmitter provides 100.1 MHz half-wave sinusoid current signal to drive the off-chip antenna, the output peak current range is -0.155 mA similar to 1.250 mA, and on-chip static power dissipation is low to 0.374 mW. All the performances of the transmitter satisfy the demands of wireless real-time BCI system for neural signals recording and processing.

基于支持向量多分类机的多类复杂手操作EEG信号模式识别

针对用于服务机器人的脑机接口系统中脑电信号模式识别精度不高,不能满足机器人多任务要求的问题,提出一种基于C-支持向量多分类机的多类复杂手操作EEG信号模式识别方法,并将其应用到复杂手操作的EEG信号模式识别试验中,实现一个4类复杂手操作的模式识别,实验结果表明,与之前用BP神经网络进行识别相比,识别率由85%提高到了90%。

Design and path planning for a remote-brained service robot

This paper presents a novel robot named "TUT03-A" with expert systems, speech interaction, vision systems etc. based on remote-brained approach. The robot is designed to have the brain and body separated. There is a cerebellum in the body. The brain with the expert systems is in charge of decision and the cerebellum control motion of the body. The brain-body. interface has many kinds of structure. It enables a brain to control one or more cerebellums. The brain controls all modules in the system and coordinates their work. The framework of the robot allows us to carry out different kinds of robotics research in an environment that can be shared and inherited over generations. Then we discuss the path planning method for the robot based on ant colony algorithm. The mathematical model is established and the algorithm is achieved with the Starlogo simulating environment. The simulation result shows that it has strong robustness and eligible pathfinding efficiency.

使用概率规则文法评估人机界面可用性

提出一种在界面系统设计规约的基础上使用的可用性评估方法.首先使用有限状态自动机抽象界面系统设计,根据概率规则文法对有限状态自动机的状态转换概率进行预测;然后结合用户的熟练程度提出了界面可用性评估算法;最后讨论了一个手机界面的可用性计算实例.文中方法能够在界面系统生命周期的早期使用,以较早地对不同设计方案进行比较,降低开发风险.

基于OMAP的废墟搜救机器人监控终端

针对废墟搜救机器人的实际需要和当前监控终端的不足,设计开发了一种新的监控终端。这种监控终端基于OMAP架构,包含了人机界面、遥控、无线通讯、数据处理等功能,实现了对机器人本体的无线操控,并实现了与指挥中心的远程无线连接。由于在功耗与性能之间取得了平衡,这种监控终端减小了体积,提高了便携性。

排爆作业机器人模拟训练系统研究

介绍了一种排爆机器人模拟训练系统.该系统提供了友好的人机交互界面,使操作人员可以进行各种模拟训练,并提高操作水平.重点介绍了该模拟训练系统的体系结构及关键实现技术,包括排爆机器人及其工作环境的建模方法、机器人运动学和动力学简化模型、碰撞检测和技能评定等.通过实验,证明了该模拟训练系统的可行性和有效性.

用光学阵列传感器的机器人物体分类系统

本文介绍用光学阵列传感器的机器人物体分类系统。传感器直接安装在机器人的两个手指上。被抓物体的阴影通过光导纤维传到安放在“安全区”的光敏元件上。计算机识别物体的轮廓后命令机器人抓握物体,并把它运送到指定的地点从而达到物体分类的目的。