新型生物信号采集和处理系统——ASB240U

成都遨生电子有限公司以电子科技大学测试技术研究所做为研发中心,将大量科研的最新技术成果成功的应用于新一代的生物信号采集与处理系统--ASB240U。该系统抛弃了目前市面上基于单片机和低速总线的体系结构,采用基于大规模可编程芯片FPGA和片上系统SOC（SystemonChip是一种高度集成化、同件化的系统集成技术,也就是把整个应用电子系统全部集成在一个芯片中）设计技术的全新体系结构,突破了数据传输和处理速度的瓶颈,使得系统整体性能获得了突破性进展。     

一种动物实验监控系统的设计

本文主要设计动物生命体征参数（体温、呼吸率和脉搏率）的监控系统。硬件电路采用MSP430F5438芯片作为微控制器,体温、呼吸和脉搏生理参数的信号采集使用了相应的信号调理电路和处理电路,主要由电信号转换电路、放大滤波电路、AD转换电路、脉冲转换电路、LCD显示电路、加热垫和蜂鸣器驱动电路等组成。     

一个新的脑电信号分析系统：小波分析理论的运用

小波变换是一种把时间、频率（或尺度）两域结合起来的分析方法。它被誉为“分析信号的数学显微镜”。本系统将小波变换用于脑电信号分析,是一个在Ｗｉｎｄｏｗｓ３．１下开发的脑电分析系统。     

新型生物信号采集和处理系统——ASB240U

成都遨生电子有限公司以电子科技大学测试技术研究所做为研发中心,将大量科研的最新技术成果成功的应用于新一代的生物信号采集与处理系统——ASB240U。该系统抛弃了目前市面上基于单片机和低速总线的体系结构,采用基于大规模可编程芯片FPGA和片上系统SOC（System on Chip是一种高度集成化、固件化的系统集成技术,也就是把整个应用电子系统全部集成在一个芯片中）设计技术的全新体系结构,突破了数据传输和处理速度的瓶颈,使得系统整体性能获得了突破性进展,实现了高速的数据采集、实时高速数字信号处理、数据传输、设备级联和外挂专用放大器接口（如微电级放大器…）等强大的功能,从而使ASB240U采集分析系统在其组成的灵活性、功能的扩展性、数据的精确性、宴时性上达到了一个前所未有的高度。     

新型生物信号采集和处理系统——ASB240U

成都邀生电子有限公司以电子科技大学测试技术研究所做为研发中心,将大量科研的最新技术成果成功的应用于新一代的生物信号采集与处理系统--ASB240U。该系统抛弃了目前市面上基于单片机和低速总线的体系结构,采用基于大规模可编程芯片FPGA和片上系统SOC（Syste．monChip是一种高度集成化、固件化的系统集成技术,也就是把整个应用电子系统全部集成在一个芯片中）设计技术的全新体系结构,突破了数据传输和处理速度的瓶颈,使得系统整体性能获得了突破性进展,实现了高速的数据采集、实时高速数字信号处理、数据传输、设备级联和外挂专用放大器接口（如微电级放大器…）等强大的功能,从而使ASB240U采集分析系统在其组成的灵活性、功能的扩展性、数据的精确性、实时性上达到了一个前所未有的高度。     

新型生物信号采集和处理系统——ASB240U

成都遨生电子有限公司以电子科技大学测试技术研究所做为研发中心,将大量科研的最新技术成果成功的应用于新一代的生物信号采集与处理系统-ASB240U。该系统抛弃了目前市面上基于单片机和低速总线的体系结构,采用基于大规模可编程芯片FPGA和片上系统SOC（SystemonChip是一种高度集成化、固件化的系统集成技术,也就是把整个应用电子系统全部集成在一个芯片中）设计技术的全新体系结构,突破了数据传输和处理速度的瓶颈,使得系统整体性能获得了突破性进展,实现了高速的数据采集、实时高速数字信号处理、数据传输、设备级联和外挂专用放大器接口（如微电级放大器…）等强大的功能,从而使ASB240U采集分析系统在其组成的灵活性、功能的扩展性、数据的精确性、实时性上达到了一个前所未有的高度。     

新型生物信号采集和处理系统——ASB240U

成都遨生电子有限公司以电子科技大学测试技术研究所做为研发中心,将大量科研的最新技术成果成功的应用于新一代的生物信号采集与处理系统--ASB240U。该系统抛弃了目前市面上基于单片机和低速总线的体系结构,采用基于大规模可编程芯片FPGA和片上系统SOC（SystemonChip是一种高度集成化、固件化的系统集成技术,也就是把整个应用电子系统全部集成在一个芯片中）设计技术的全新体系结构,突破了数据传输和处理速度的瓶颈,使得系统整体性能获得了突破性进展,实现了高速的数据采集、实时高速数字信号处理、数据传输、设备级联和外挂专用放大器接口（如微电级放大器…）等强大的功能,从而使ASB240U采集分析系统在其组成的灵活性、功能的扩展性、数据的精确性、实时性上达到了一个前所未有的高度。     

新型生物信号采集和处理系统——ASB240U

成都邀生电子有限公司以电子科技大学测试技术研究所做为研发中心,将大量科研的最新技术成果成功的应用于新一代的生物信号采集与处理系统——ASB240U。该系统抛弃了目前市面上基于单片机和低速总线的体系结构,采用基于大规模可编程芯片FPGA和片上系统SOC（Systemon Chip是一种高度集成化、固件化的系统集成技术,也就是把整个应用电子系统全部集成在一个芯片中）设计技术的全新体系结构,突破了数据传输和处理速度的瓶颈,使得系统整体性能获得了突破性进展,实现了高速的数据采集、实时高速数字信号处理、数据传输、设备级联和外挂专用放大器接口（如微电级放大器…）等强大的功能,从而使ASB240U采集分析系统在其组成的灵活性、功能的扩展性、数据的精确性、实时性上达到了一个前所未有的高度。     

新型生物信号采集和处理系统——ASB240U

成都遨生电子有限公司以电子科技大学测试技术研究所做为研发中心,将大量科研的最新技术成果成功的应用于新一代的生物信号采集与处理系统-ASB240U。该系统抛弃了目前市面上基于单片机和低速总线的体系结构,采用基于大规模可编程芯片FPGA和片上系统SOC（Syste—monChip是一种高度集成化、固件化的系统集成技术,也就是把整个应用电子系统全部集成在一个芯片中）设计技术的全新体系结构,突破了数据传输和处理速度的瓶颈,使得系统整体性能获得了突破性进展,实现了高速的数据采集、实时高速数字信号处理、数据传输、设备级联和外挂专用放大器接口（如微电级放大器…）等强大的功能,从而使ASB240U采集分析系统在其组成的灵活性、功能的扩展性、数据的精确性、实时性上达到了一个前所未有的高度。     

新型生物信号采集和处理系统——ASB240U

成都遨生电子有限公司以电子科技大学测试技术研究所做为研发中心,将大量科研的最新技术成果成功的应用于新一代的生物信号采集与处理系统——ASB240U。该系统抛弃了目前市面上基于单片机和低速总线的体系结构,采用基于大规模可编程芯片FPGA和片上系统SOC（SystemonChip是一种高度集成化、固件化的系统集成技术,也就是把整个应用电子系统全部集成在一个芯片中）设计技术的全新体系结构,突破了数据传输和处理速度的瓶颈,使得系统整体性能获得了突破性进展,实现了高速的数据采集、实时高速数字信号处理、数据传输、设备级联和外挂专用放大器接口（如微电级放大器…）等强大的功能,从而使ASB240U采集分析系统在其组成的灵活性、功能的扩展性、数据的精确性、实时性上达到了一个前所未有的高度。     

Selecting EEG components using time series analysis in brain death diagnosis

In diagnosis of brain death for human organ transplant, EEG (electroencephalogram) must be flat to conclude the patient’s brain death but it has been reported that the flat EEG test is sometimes difficult due to artifacts such as the contamination from the power supply and ECG (electrocardiogram, the signal from the heartbeat). ICA (independent component analysis) is an effective signal processing method that can separate such artifacts from the EEG signals. Applying ICA to EEG channels, we obtain several separated components among which some correspond to the brain activities while others contain artifacts. This paper aims at automatic selection of the separated components based on time series analysis. In the flat EEG test in brain death diagnosis, such automatic component selection is helpful.     

Centrale de mesure ambulatoire biomédicale sur PSoC

Improvement in quality and efficiency of health and medicine, at home and in hospital, has become of paramount importance. The solution to this problem would require the continuous monitoring of several key patient parameters, including the assessment of autonomic nervous system (ANS) activity using non-invasive sensors, providing information for emotional, sensorial, cognitive and physiological analysis of the patient. Recent advances in embedded systems, microelectronics, sensors and wireless networking enable the design of wearable systems capable of such advanced health monitoring. The subject of this article is an ambulatory system comprising of a small wrist device connected to several sensors for the detection of the autonomic nervous system activity. It affords monitoring of skin resistance, skin temperature and heart activity. It is also capable of recording the data on a removable media or sending it to computer via a wireless communication. The wrist device is based on a programmable system-on-chip (PSoC) from Cypress.     

High-performance hardware implementation of a parallel database search engine for real-time peptide mass fingerprinting

Motivation: Peptide mass fingerprinting (PMF) is a method for protein identification in which a protein is fragmented by a defined cleavage protocol (usually proteolysis with trypsin), and the masses of these products constitute a 'fingerprint' that can be searched against theoretical fingerprints of all known proteins. In the first stage of PMF, the raw mass spectrometric data are processed to generate a peptide mass list. In the second stage this protein fingerprint is used to search a database of known proteins for the best protein match. Although current software solutions can typically deliver a match in a relatively short time, a system that can find a match in real time could change the way in which PMF is deployed and presented. In a paper published earlier we presented a hardware design of a raw mass spectra processor that, when implemented in Field Programmable Gate Array (FPGA) hardware, achieves almost 170-fold speed gain relative to a conventional software implementation running on a dual processor server. In this article we present a complementary hardware realization of a parallel database search engine that, when running on a Xilinx Virtex 2 FPGA at 100 MHz, delivers 1800-fold speed-up compared with an equivalent C software routine, running on a 3.06 GHz Xeon workstation. The inherent scalability of the design means that processing speed can be multiplied by deploying the design on multiple FPGAs. The database search processor and the mass spectra processor, running on a reconfigurable computing platform, provide a complete real-time PMF protein identification solution.     

SSVEP Extraction Based on the Similarity of Background EEG

Steady-state Visual Evoked Potential (SSVEP) outperforms the other types of ERPs for Brain-computer Interface (BCI), and thus it is widely employed. In order to apply SSVEP-based BCI to real life situations, it is important to improve the accuracy and transfer rate of the system. Aimed at this target, many SSVEP extraction methods have been proposed. All these methods are based directly on the properties of SSVEP, such as power and phase. In this study, we first filtered out the target frequencies from the original EEG to get a new signal and then computed the similarity between the original EEG and the new signal. Based on this similarity, SSVEP in the original EEG can be identified. This method is referred to as SOB (Similarity of Background). The SOB method is used to detect SSVEP in 1s-length and 3s-length EEG segments respectively. The accuracy of detection is compared with its peers computed by the widely-used Power Spectrum (PS) method and the Canonical Coefficient (CC) method. The comparison results illustrate that the SOB method can lead to a higher accuracy than the PS method and CC method when detecting a short period SSVEP signal.     

Motor imaginary-based brain–machine interface design using programmable logic controllers for the disabled

Aiming at the implementation of brain–machine interfaces (BMI) for the aid of disabled people, this paper presents a system design for real-time communication between the BMI and programmable logic controllers (PLCs) to control an electrical actuator that could be used in devices to help the disabled. Motor imaginary signals extracted from the brain's motor cortex using an electroencephalogram (EEG) were used as a control signal. The EEG signals were pre-processed by means of adaptive recursive band-pass filtrations (ARBF) and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC. A real-time test system was designed using MATLAB for signal processing, KEP-Ware V4 OLE for process control (OPC), a wireless local area network router, an Omron Sysmac CPM1 PLC and a 5 V/0.3 A motor. This paper explains the signal processing techniques, the PLC's hardware configuration, OPC configuration and real-time data exchange between MATLAB and PLC using the MATLAB OPC toolbox. The test results indicate that the function of exchanging real-time data can be attained between the BMI and PLC through OPC server and proves that it is an effective and feasible method to be applied to devices such as wheelchairs or electronic equipment.     

基于EMD的左右手运动想象脑电信号分析研究

基于经验模态分解（EMD）理论,提出一种左右手运动想象脑电信号分析方法。首先利用时间窗对脑电信号数据进行划分,对每段数据通过经验模态分解法将其分解为一组固有模态函数IMF,提取主要信号所在的IMF层去除信号中的噪声。对含有主要信号的几层IMF进行Hilbert变换,得到瞬时频率与对应的瞬时幅值。再提取左右手想象的特定频段mu节律和beta节律的能量信号作为特征,分别利用支持向量机（SVM）和Fisher进行了分类比较。对EMD和小波包在去噪和特征提取进行了比较。结果表明,EMD是一种很有效的去噪方法,经过EMD分解后提取的能量信号在区分左右手想象上更具有优势,识别率高。     

Hidden pattern discovery on event related potential EEG signals

EEG signals are important to capture brain disorders. They are useful for analyzing the cognitive activity of the brain and diagnosing types of seizure and potential mental health problems. The Event Related Potential can be measured through the EEG signal. However, it is always difficult to interpret due to its low amplitude and sensitivity to changes of the mental activity. In this paper, we propose a novel approach to incrementally detect the pattern of this kind of EEG signal. This approach successfully summarizes the whole stream of the EEG signal by finding the correlations across the electrodes and discriminates the signals corresponding to various tasks into different patterns. It is also able to detect the transition period between different EEG signals and identify the electrodes which contribute the most to these signals. The experimental results show that the proposed method allows the significant meaning of the EEG signal to be obtained from the extracted pattern.     

Linear correlation between fractal dimension of EEG signal and handgrip force

Fractal dimension (FD) has been proved useful in quantifying the complexity of dynamical signals in biology and medicine. In this study, we measured FDs of human electroencephalographic (EEG) signals at different levels of handgrip forces. EEG signals were recorded from five major motor-related cortical areas in eight normal healthy subjects. FDs were calculated using three different methods. The three physiological periods of handgrip (command preparation, movement and holding periods) were analyzed and compared. The results showed that FDs of the EEG signals during the movement and holding periods increased linearly with handgrip force, whereas FD during the preparation period had no correlation with force. The results also demonstrated that one method (Katz’s) gave greater changes in FD, and thus, had more power in capturing the dynamic changes in the signal. The linear increase of FD, together with results from other EEG and neuroimaging studies, suggest that under normal conditions the brain recruits motor neurons at a linear progress when increasing the force.     

针刺测力仪及脉象仪在针灸治疗中的应用

目的设计一套在体实时监测针刺针体受力情况和同步观察针刺前后脉象的系统。方法利用设计的针刺测力仪及脉象仪采集针刺时针体受力的力学信号及人体脉象的信号并将这些信号传换成计算机能识别的信号,把脉象的信号与针刺信号相对应运用软件进行分析整理。结果针刺前后脉象有比较明显的变化。结论利用脉象仪和针刺测力仪能够观察针刺前后脉象的变化,将为针刺选穴,配穴及疗效监测提供有效的手段。