A Study on the Method of Extraction for 40 Hz EEG Bursts

The bursts of 40HzEEG are a reliable indicator of the " focused arousal ". It hasan active significance in diagnosis of cognitive dysfunction.But, the bursts of 40 Hz EEG are seriously interfered by the high amplitude broad-band EMG signals from the scalp. It causes the difficulty of detection, This paper give a new method uf the combination of threshold extraction in frequency domain and neural network pattern recognition to realize the effective detection of 40 Hz EEGbursts , and reports the mean peak power of the 40 HZ EEG bursts for 11 subjects and analyses theirthinking states, The reaults indicate that the method given in this paper can be used feasibly and support the theory given by D,E, Sheer.     

A Study on Chaotic State of EEG Signal

In this paper.the chaotic theory is described briefly and the chaotic state ofEEG on thinking are studied. In this study. the method of detecting short bursts of 40 Hz EEG ac-tivity is firstly usedto dedermine the segments to be computed for analysis of the chaotic state ofEEG. therefore the reliability and stability of the analysis results are assured In this paper.thechaotic para meters of qa n titative analysis are studied and more then 30 subjects EEG are analysedfor the value of the fractional dimension and the maximal Lyapunov exponent.The results of exper-iments indicate that the value of the fractional dimension is valuable for clincal diagnosis and themaximal Lyapunov exponent is not sufficient for dignosis of disease.     

Study on Nonlinear Dynamical Method for EEG Signal Processing

After studying the meaning of EEG signal processing using systems theory,the basic difficulties of the research on the EEG system and signal analysis are given,showing that it is hard to build model of the EEG system and signal.Hence,we introuduce the method of reconstructing the attractor in state spaces.Through the analysis of some experiments,the meaning of the presented method has been discussed.     

Analysis on 40 Hz ERP During Brain Transient Cognitive Performance

In this investigation 50 normal subjects were observed. Activities of EEG of 8 areas on both hemisphere during six different states were recorded. The author analyzed time ( T ), area( A ) and amplitude ( Amp ) of burst 40 Hz ERP recorded during different states and investigate the correlation's between 40 Hz ERP and age, sex,education background and scores of IQ. The time, area and amplitude of burst 40 Hz ERP are all significantly lower during rest states than those during orientation test, calculation test and visual spatial task test. During thinking process the increase of burst 40 Hz ERP is positive correlated with the scores in MMSE and WAIS. T is the most stable and sensitive marker. A affected by T and Amp, is stable but less sensitive than T. Amp is less sensitive than T and A.     

Temporal-spatial Filtering in the Preprocessing of Clinical EEG Signals

EEG spot-recording for clinical patients in the bed is always advocated due to easing patients and making them comfortable. But EEG signals recorded in the ward exposed to various noisesand interference is less effective in the EEG analysis. Atemporal-spatial filter is designed for noise removing of clinical EEG signals, the first filtering is temporal filtering using band-pass filter, and the second filtering is spatial filtering with ICA-based spatial filter. Experimental results indicate that various noises and interferences, such as power interference, blinking, eyes movements, muscle moments, ECG artifacts, etc., are removed effectively, individually or simultaneously.     

Multiresolution Wavelet Transform for Detection of EEG Abnormal Rhythms

The signal of brain activity is a non-stationary random signal including lots of physiology and disease information, which is of important action for doctors to judge pathological changes in brain. So the analysis and process of the EEG signals are always attended. In this paper, the authors take account of the time-frequency localization of wavelet transform and use multiresolution wavelet transform to detect EEG abnormal rhythms. The signals of different scales after EEG signals are transformed by multiresolution wavelet transform not only reflect the frequency information of the signals, namely the more great scale is the lower of the frequency of the signals,but also reflect the time information of the signals, namely EEG state at that time. The test results indicate that the abnormal rhythms of the EEG signals can be detected effectively if right wavelet basis is selected.     

EEG Signal Denoising Based on Wavelet Transform

The article focuses on the method of noise cancellation for EEG signal. The method of notch filter is discussed. According to the frequency of noise and the principle of notch filter, the design result of the notch filter and the denoised signal are presented. Then, the analysis of EEG signal are proposed based on wavelet transform (WT) and noise cancellation using WT. Wavelet transform is a multi-resolution time-frequency analysis method. It can decompose mixed signal into signals at different frequency bands. The EEG signal is analyzed and denoised using WT, then the results are presented respectively. Comparing the experiment results shows that WT can detect and process noise in the EEG signal effectively.     

Detecting EEG Sleep Spindles by Digital Filtering with Zero Phase Error

EEG signal is a kind of very complex non stationary signal. It is of important clinical values for using EEG most efficiently to distinguish all kinds of components in EEG or getting needed components to judge some pathology features effectively. The phase moving of the system can change the relative phase relation between the components in the inputed signal, so there can be a great change of the feature in time domain of inputed signal. The FRR and RRF digital filtering methods and their realization are discussed. By using these methods, the system can be of the feature of zero phase error, and the filtering of EEG signals with zero phase error can be realized. Based on the definition of sleep spindles, we can detect sleep spindles in time domain directly by using digital filtering with zero phase error.     

New Design of Pre-amplification Circuit for EEG Signals

In the measurement of EEG signals ,the pre - amplification electrical signal of brain is the most importantant and key link. Whether the design is excellent or inferior is related to the electrical signal detection system of brain directly. The design of this circuit has been paid attention to by domestic and international expert all the time. On the basis of existing biological electricity amplifier which is studied, the authors consult the electric amplifier circuit of living beings designed , such as reference , improve and design a new-type and high performance electrical signal characteristic of brain in . The circuit has prevented the traditional design from enlarging the miscellaneous simulation of the circuit and straining the wave link, otherwise, this circuit is of simple structure , low cost, and convenient debugging. The paper has detailed theoritic analysis and experimental results to prove this circuit, which offered a new train of thought for reseach and development of brain electrical amplifier and other faint biological electricity amplifiers . This curcuit has be expected to win extensive application.     

Chaotic behavior of stick slip in rock bursts

Two types of rock bursts, strain rock bursts and fault-slip rock bursts, are defined briefly in terms of their instability mechanisms. Justification for stick-slip instability to account for a fault-slip rock burst is presented. A model we call     

Frequency domain analysis and the application for extracting electroencephalogram signal characteristic waves

A new quantitative analysis method to describe the dynamic variation of electroencephalogram (EEG) signals was proposed. Based on the Fourier transformation, the method is called Fourier multi resolution analysis (FMRA). FMRA decomposes the frequency domain with a binary system and can resolve EEG signals into the basic rhythms of the four waves to study the dynamic characteristics of EEG signal rhythms. FMRA has clear physical meaning, and can obtain more information than wavelet multi resolution analysis does. FMRA can extract perfectly the rhythmic characteristics of EEG signals in the time and frequency domains.     

Signal Processing of Electroencephalogram and It''s Application

Electroencephalogram (EEG) signals are the electrical activities in the cortex or on the surface of scalp caused by the physiological activities of the brain which play a key role in the diagnosis of brain and the functional determination of brain. The authors discuss some methods for processing EEG signals from the view of the frequency domain and time domain, especially introduce some time-frequency analysis methods such as Wigner distribution, wavelet transform and matching pursuit etc. , ANN and non-linear analysis for EEG signals processing.     

Application of Trap Filter of 50 Hz in Brainwave Data Acquisition System

It's always difficult in technology to eliminate power interference of 50 Hz in Brainwave Data Acquisition System .Though traditional ways to eliminate it can also gain some effects, they have drawbacks such as high cost and low currency at all time. The use of trap filter of 50 Hz may solve the question radically. Through the authors' repetitious experimentation, it is proved that trap filter of VCVS not only eliminate power interference of 50 Hz farthest but also have merits of simple circuit structure, convenient coupling and upper reliability. The authors offer collected brainwave in strong power interference environment. It may be seen that it is very clear and clean. Because power interference of (50 Hz) exists in almost all Data Acquisition System, this type of trap filter possesses better currency, it is expected to win extensive application in medical apparatus of measurement of other biological electricity signals and industrial observation and control system.     

Research Methods of Brain-computer Interface Technology

Brain computer interface(BCI) is an EEG based communication and control system between human and computer or other electric devices, which does not depend on the brain's normal output channels of peripheral nerves and muscles. It is a novel kind of human interface, may have applications in rehabilitation, control and other fields. The developming speed of brain computer research in recent years is high. Methods of how to use P300, SSVEP, ERS/ERD, SCP, spontaneous EEG and implanted electrode to realize BCI are introduced. Characteristic and limitation of different methods are discussed. Status, problems and application prospects of BCI technology are also included.     

西藏青稞鸡爪谷杂粮粉挤压膨化工艺优化

青稞在西藏长期以来的产业发展中,基本以糌粑、青稞米等初级产品为主,为开拓更多的青稞加工方式,丰富产品类别,对青稞粉进行挤压膨化及工艺的优化,并结合当地杂粮鸡爪谷等,通过挤压膨化提升青稞的口感及相关产品的营养价值。青稞杂粮粉挤压膨化后,对不同膨化参数组合的青稞杂粮粉的吸水性、水溶性、堆积密度、沉降性、分散性、润湿性等指标进行了测定,发现膨化参数组合之间没有表现出明显的速溶差异特性。青稞杂粮膨化率的大小不跟速溶性指标呈正比关系。根据膨化效果最佳膨化工艺参数为物料水分含量为12%,Ⅲ区温度170℃,主轴转速20 Hz,喂料速度8 Hz;从速溶粉的生产工艺来看,最佳工艺为物料水分含量为16%,Ⅲ区温度170℃,主轴转速23 Hz,喂料速度8 Hz。     

玉米新品种DUS测试中数量性状的适宜样本容量研究

DUS（Distinctness, Uniformity and Stability）测试是对申请品种权的植物新品种的特异性、一致性和稳定性进行测试。DUS测试结果直接影响到植物新品种是否能被授予保护权。在DUS测试中,数量性状是完成品种描述和进行特异性判定的重要部分,而数量性状的调查花费人工最多、工作量最大,怎样能缩小工作量又能反映数量性状调查的准确性,这是本试验研究的目的。目前,玉米测试指南已经成为国家指南,指南要求每个数量性状测量40个数据。本试验利用10个测试品种、全株10个数量性状,对每个性状的40个已测样本进行统计分析,根据抽样分布理论和区间估计的方法确定每个性状的最小取样容量。     

Evaluation of grape flesh texture by an acoustic vibration method

Destructive acoustic vibrations produced by probe penetration were measured by a texture measurement device to clarify the characteristic flesh texture of nine grape cultivars and to examine methods for quantitative evaluation of grape flesh texture. The texture index (TI) is based on the energy density between 10 Hz and 3.2 kHz and was calculated using signals obtained with a piezoelectric sensor attached to a wedge-tipped probe. Using this index, we successfully classified nine grape cultivars according to flesh texture as crisp, non-crisp, or intermediate. The breaking force measured by a conventional puncture test did not identify these three types of texture. The breaking force was correlated with the TI calculated using 0-50 Hz vibrations but not with that calculated using the 10-3.2 Hz vibrations. This suggests that the former represents flesh firmness, and TI values measured at frequencies greater than 10 Hz evaluate characteristic textures of grape flesh that could not be evaluated using a load sensor installed in a conventional stress-strain analyzer.     

林板纸一体化的动力、模式与障碍

在对福建省林纸林板企业办原料林基地调查的基础上,运用资产专用性理论及契约理论,从微观行为层次揭示了南方集体林区林纸林板企业40多年来林板纸一体化的动力、模式与障碍等行为机理;得出林纸林板企业林板纸一体化动力的收益最大化机制,分析了企业林板纸一体化历年来主要实施的六种模式的机理及特点,指出阻碍企业实施林板纸一体化的3个深层次原因。     

黑龙江省雷暴空间分布规律及预测研究

笔者利用信息扩散理论对黑龙江省1971-2005年81个气象站的雷暴空间分布特征及变化规律进行分析,并结合GIS制图技术绘成雷暴日数分别为25、30、35、40天的概率区划图,同时应用灰色GM（1,1）模型针对黑龙江省的哈尔滨、大庆、齐齐哈尔等重要城市未来50年发生雷暴日数≥30天的多雷暴天气进行预测,以期通过预报和采取适当对策最大程度减少雷暴灾害对城市的影响。结果表明：（1）35年来,黑龙江省年平均雷暴日数,中部及中南部较多,东部平原地区较少;（2）全省雷暴发生概率最高值区位于中部和中南部,最低值区位于东部。（3）在未来50年里,哈尔滨、齐齐哈尔、牡丹江、佳木斯、大庆出现雷暴日数≥30的多雷暴年份的次数分别为9、7、6、4、8次,其出现的概率值分别为18%、14%、12%、8%、16%。     

马铃薯腋芽快繁微型薯技术研究

应用脱毒微型薯是当前马铃薯生产中最有效的增产措施之一,平均增产幅度在40%~80%。但由于脱毒微型薯价格较高,在生产实际中很难大面积推广应用。根据植物生长点分生组织,在激素诱导下生长极性可以发生改变的原理,在马铃薯现蕾至开花前期,截取脱毒马铃薯中下部带有腋芽的羽状复叶,用一定浓度的萘乙酸（NAA）、吲哚乙酸（IAA）、吲哚丁酸（IBA）和6-苄基腺嘌呤（6-BA1）处理后,经过4~5周的培育,诱导出生产用微型薯。采用马铃薯离体腋芽结薯技术生产微型薯,生产周期短、成本低、操作简便、种薯质量高,是脱毒马铃薯微型薯扩繁生产的一条新途径。