便携式脑电无线采集系统的设计

设计了一种基于单片机、无线芯片n RF24L01和TFT液晶显示屏的便携式脑电无线采集系统,系统控制器采用STC12C5A60S2单片机。发送端的单片机对预处理后的脑电信号进行采集和存储,通过n RF24L01芯片进行无线传输,接收端单片机再将信号波形送至液晶显示屏显示并进行进一步的分析。该系统不需要采用PC机,因此具有体积小、轻便、功耗低等特点。     

一种新型脑电信号的采集方法和应用

基于脑电信号控制假手的研究,分析了脑电信号和假手运动之间的联系,得出脑电控制假手的可行性结论。为获得人体脑电信息,提出了一种完整的从头皮电极到ADC的脑电信号调理电路。此放大电路输入阻抗高(1011Ω)、噪声低、共模抑制比高(KCMRR≥90 dB)、体积小,便于携带,满足脑电假手的使用要求。     

New classification techniques for electroencephalogram (EEG) signals and a real-time EEG control of a robot

This paper studies the state-of-the-art classification techniques for electroencephalogram (EEG) signals. Fuzzy Functions Support Vector Classifier, Improved Fuzzy Functions Support Vector Classifier and a novel technique that has been designed by utilizing Particle Swarm Optimization and Radial Basis Function Networks (PSO-RBFN) have been studied. The classification performances of the techniques are compared on standard EEG datasets that are publicly available and used by brain–computer interface (BCI) researchers. In addition to the standard EEG datasets, the proposed classifier is also tested on non-EEG datasets for thorough comparison. Within the scope of this study, several data clustering algorithms such as Fuzzy C-means, K-means and PSO clustering algorithms are studied and their clustering performances on the same datasets are compared. The results show that PSO-RBFN might reach the classification performance of state-of-the art classifiers and might be a better alternative technique in the classification of EEG signals for real-time application. This has been demonstrated by implementing the proposed classifier in a real-time BCI application for a mobile robot control.     

脑电信号在线采集系统设计与实现

为了实现对脑电信号的实时采集,设计了一种基于ADS1299的无线脑电信号采集系统。该系统由硬件和软件两部分构成,硬件部分由以ADS1299为核心构成的信号采集模块、ATmega328主控制模块、RFD22301数据传输模块组成;软件部分则使用Java编写上位机检序,能够实时显示与处理脑电信号。高共模抑制比和高信噪比的前置放大器设计是信号采集模块设计的关键。同时,对经典的双T陷波电路进行改进,使得Q因子可调。通过一系列的采集和处理,最终实现脑电信号在上位机上的实时显示。     

基于Android的脑电信号无线采集与分析系统设计

提出一种基于Android平台的脑电无线采集与警觉度监测终端的设计。采用Wi-Fi作为无线通信方案,以Android手机作为上位机,在手机上设计应用程序,通过手机应用程序可以方便地实现对采集设备的参数设置、无线连接、数据接收、波形显示、数据分析和文件存储。Android手机端通过Wi-Fi与下位机建立通信,实时接收Wi-Fi模块发送的脑电数据,绘成脑电图,并能通过手机端向下位机发送控制命令,再将基于极限学习机的脑电信号分类算法通过Java编码移植到手机内部,分析脑电信号所携带的警觉度信息。立足便携式脑电信号无线采集系统,在系统中加入基于Android系统的传输控制方法,并植入训练速度快、分类效果好的算法程序,为便携式脑电信号采集提供了一个新方案。     

变化噪声下GNSS卫星信号的捕获算法的研究

当今社会,越来越多的不确定性噪声在城市中出现,这些噪声会被掺杂到有效的卫星信号中,造成信号的捕获失败。许多算法中设定的噪声比较固定,没有考虑到每个地方噪声变化的情况。文中基于快速傅里叶变换的并行捕获技术,结合前差分技术和相干积分,尝试消除这种不确定影响。其次,在此基础上进行各种噪声下的仿真,寻求捕获的判决门限的合理值。通过仿真表明,该算法可以有效地捕获卫星信号,并减少了计算的复杂度。     

Interactive analysis and display of the electroencephalogram (EEG) in real time

The development of fast, relatively inexpensive graphics terminals permits the integration of interactive display with on-line, real time signal analysis in dedicated minicomputer systems. Interactive display in the context of electroencephalogram (EEG) analysis includes: (1) control over form and parameters of an isometric plot using hidden line suppression and floating level; (2) selection of function and transform to graph; and (3) expansion or contraction of coordinate axes and timerscale, from ‘wide angle’ views of the course of several hours of recording of the entire scalp, to ‘telescopic’ blow-ups of the details of two or three seconds of activity in a single channel.

The analysis paradigms function on-line and in real time, and incorporate parallel time domain and spectral analyses on data from eight or fewer electrode placements. Spectral estimates of power and simple coherences are typically formed using ensemble averages of non-overlapping one-half to two second periodograms obtained by Fast Fourier Transform. Transient analysis uses heuristic strategies to isolate clinically significant patterns, such as the sharp paroxysmal wave forms associated with the epilepsies. Instrumental and extra-cerebral artifact are partially eliminated by algorithms based upon heuristic criteria. Parameters of spectral and transient analysis are interactively alterable to facilitate varying experimental paradigms.

To implement such systems, which can be both compute and I/O bound, it is necessary to optimize systems software. A simple and multitasking executive coordinates program task modules. Memory space and computing time are conserved through extensive use of dynamic buffer allocation and double precision integer arithmetic.     

Low power architectures for digital signal processing

Low power architectures for digital signal processing algorithms requiring inner product computation are presented. In the first step a power efficient memory organization exploiting data reuse is determined. In the second step an order of evaluation of the partial products that reduces the switching activity at the inputs of the computational units is derived. Information related to both coefficients which are static and data which are dynamic, is used to drive the reordering of computation. Experimental results for several signal processing algorithms prove that the proposed techniques lead to significant savings in net switching activity and thus in power consumption.     

An efficient soft-computing technique for extraction of EEG signal from tainted EEG signal

Electroencephalography (EEG) is the recording of electrical activity of neurons within the brain and is used for the evaluation of brain disorders. But, EEG signals are contaminated with various artifacts which make interpretation of EEGs clinically difficult. In this research paper, we use a soft-computing technique called ANFIS (Adaptive Neuro-Fuzzy Inference System) for the removal of EOG artifact, combined EOG and EMG artifact. Improvement in the output signal to noise ratio and minimum mean square error are used as the performance measures. The outputs of the proposed technique are compared with the outputs of techniques such as neural network, based on ADALINE (Adaptive Linear Neuron) and adaptive filtering method, which makes use of RLS (Recursive Least Squares) algorithm through wavelet transform (RLS-Wavelet). The obtained results show that the proposed method could significantly detect and suppress the artifacts.     

Classification of brain signal (EEG) induced by shape-analogous letter perception

Visual perception of English letters involves different underlying brain processes including brain activity alteration in multiple frequency bands. However, shape analogous letters elicit brain activities which are not obviously distinct and it is therefore difficult to differentiate those activities. In order to address discriminative feasibility and classification performance of the perception of shape-analogous letters, we performed an experiment in where EEG signals were obtained from 20 subjects while they were perceiving shape analogous letters (i.e., ‘p’, ‘q’, ‘b’, and ‘d’). Spectral power densities from five typical frequency bands (i.e., delta, theta, alpha, beta and gamma) were extracted as features, which were then classified by either individual widely-used classifiers, namely k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF) and AdaBoost (ADA), or an ensemble of some of them. The F-score was employed to select most discriminative features so that the dimension of features was reduced. The results showed that the RF achieved the highest accuracy of 74.1% in the case of multi-class classification. In the case of binary classification, the best performance (Accuracy 86.39%) was achieved by the RF classifier in terms of average accuracy across all possible pairs of the letters. In addition, we employed decision fusion strategy to exert complementary strengths of different classifiers. The results demonstrated that the performance was elevated from 74.10% to 76.63% for the multi-class classification and from 86.39% to 88.08% for the binary class classification.     

EEG signal preprocessing for biometric recognition

Electroencephalography (EEG) has been recently investigated as a biometric modality for automatic people recognition purposes. Several studies have shown that brain signals possess subject-specific traits that allow distinguishing people. Nonetheless, extracting discriminative characteristics from EEG recordings may be a challenging task, due to the significant presence of artifacts in the acquired data. In order to cope with such issue, in this paper we evaluate the effectiveness of some preprocessing techniques in automatically removing undesired EEG contributions, with the aim of improving the achievable recognition rates. Specifically, methods based on blind source separation and sample entropy estimation are here investigated. An extensive set of experimental tests, performed over a large database comprising recordings taken from 50 healthy subjects during three distinct sessions spanning a period of about one month, in both eyes-closed and eyes-open conditions, is carried out to analyze the performance of the proposed approaches.     

一种新的BOC(n,n)型信号的精确捕获算法

针对BOC(n,n)型信号自相关函数存在三个峰值导致伪码捕获模糊性的问题,提出了一种新的基于相关函数的精确无模糊的捕获算法。该算法将信号进行频域的多普勒补偿,通过本地增设QBOC(n,n)支路,利用BOC(n,n)信号自相关以及BOC(n,n)和QBOC(n,n)互相关,经移位±TC/4、取模、求和、平方运算而实现。理论与仿真分析表明,该算法在不改变自相关主峰宽度的情况下完全消除相关副峰的同时,显著提高了主峰峰值;比传统捕获算法的性能好4.5 dB左右,并且捕获精度良好、易于实现。     

头皮脑电采集技术研究

脑电能反映人脑的健康及认知活动状况,是脑疾病诊治及认知神经科学的重要参数。脑电监测也是脑机接口的重要手段。其中,头皮脑电采集技术相对于颅内脑电采集技术具备无创的优点,相对于前额脑电采集能提供多通道多脑区的脑电信号,用途最广泛。然而头发遮蔽影响脑电采集性能,从而限制其应用。对头皮脑电采集技术的电极器件及可穿戴系统进行了综述研究,分析该领域国内外科研及产业化进展情况,并从新型材料、先进结构和加工工艺、系统集成三方面对头皮脑电采集技术的发展进行展望。研究对于头皮脑电采集技术的发展具有指导价值与参考意义。     

基于FPGA的阵列信号数据采集系统

针对阵列信号数据采集系统设计要求具有幅相一致、速度快、大数据量的特点,设计了一种基于FPGA的阵列信号数据采集系统。该系统以同步采样A/D转换器为核心,配合基于FPGA的控制单元,完成128路阵列信号同步采样功能。同时采用88E1111作为网络PHY芯片,完成与上位机之间的千兆位UDP通信,实现大数据量高速传输功能。经测试,系统实现了对128路阵列信号的采集与传输,具有良好的幅度一致性、相位一致性以及快速、稳定的特点。     

Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking

Application of neuroscience methods to analyze and understand human behavior related to markets and marketing exchange has recently gained research attention. The basic aim is to guide design and presentation of products to optimize them to be as compatible as possible with consumer preferences. This paper investigates physiological decision processes while participants undertook a choice task designed to elicit preferences for a product. The task required participants to choose their preferred crackers described by shape (square, triangle, round), flavor (wheat, dark rye, plain) and topping (salt, poppy, no topping). The two main research objectives were (1) to observe and evaluate the cortical activity of the different brain regions and the interdependencies among the Electroencephalogram (EEG) signals from these regions; and (2) unlike most research in this area that has focused mainly on liking/disliking certain products, we provide a way to quantify the importance of different cracker features that contribute to the product design based on mutual information. We used the commercial Emotiv EPOC wireless EEG headset with 14 channels to collect EEG signals from participants. We also used a Tobii-Studio eye tracker system to relate the EEG data to the specific choice options (crackers). Subjects were shown 57 choice sets; each choice set described three choice options (crackers). The patterns of cortical activity were obtained in the five principal frequency bands, Delta (0–4 Hz), Theta (3–7 Hz), Alpha (8–12 Hz), Beta (13–30 Hz), and Gamma (30–40 Hz). There was a clear phase synchronization between the left and right frontal and occipital regions indicating interhemispheric communications during the chosen task for the 18 participants. Results also indicated that there was a clear and significant change (p < 0.01) in the EEG power spectral activities taking a place mainly in the frontal (delta, alpha and beta across F3, F4, FC5 and FC6), temporal (alpha, beta, gamma across T7), and occipital (theta, alpha, and beta across O1) regions when participants indicated their preferences for their preferred crackers. Additionally, our mutual information analysis indicated that the various cracker flavors and toppings of the crackers were more important factors affecting the buying decision than the shapes of the crackers.     

基于USB接口的电感传感器信号采集系统的研究

介绍了一种基于通用串行总线(USB)接口的数据采集系统,它以电感传感器作为敏感元件,以单片机P89C61为控制核心,利用USB接口实现数据通信。介绍了系统的结构与工作原理,详细分析了电感传感器的测量电路、USB接口电路及软件设计。系统软件采用Ke ilC51语言编写芯片固件程序、采用VC++语言编写USB设备驱动程序和应用程序。实验结果表明:在±10μm的测量范围内,系统测量精度可以达到0.01μm。该系统用于表面粗糙度、圆度等信号的数据采集,性能稳定、效果良好、使用方便。     

一种高动态环境下 COMPASS 卫星信号快速捕获算法

在对传统的卫星信号捕获算法分析的基础上,提出了一种惯性导航系统(INS)辅助的基于部分匹配滤波器和快速傅里叶变换(FFT)相结合的捕获算法。在此算法中,利用惯性导航设备提供的信息计算多普勒频率,通过部分匹配滤波器和 FFT 相结合的算法并行搜索载波频率和码相位。该算法不仅能缩小多普勒频率的搜索范围,而且能够快速搜索码相位。仿真结果表明,此算法能够在高动态环境下成功捕获 COMPASS 卫星信号,并且明显减少捕获时间。     

适用于GPS软件接收机的弱信号捕获方法

为了解决全球定位系统(GPS)软件接收机中弱信号捕获存在灵敏度和运算效率低的问题,提出了一种基于快速傅里叶变换(FFT)改进的差分相干累积算法。通过对去载波后的中频信号进行块累加处理,解决了相干积分时间的限制;根据FFT频移特性,采用多普勒圆周移位搜索替代频率补偿搜索,减少了FFT运算量;同时采用了不同的下变频,降低了频域分量间的损耗;对相干积分结果进行了差分相干累积,相对于传统的非相干累积,提高了信噪比。实验结果表明,该算法在-39dB的低信噪比环境下仍能捕获到所有微弱信号,具有较高的灵敏度和运算效率。     

一种直扩MSK信号的二维联合捕获方法

针对直扩MSK信号的特殊性以及高动态环境下载波多普勒频偏对伪码捕获的影响,提出了一种直扩MSK信号的二维联合捕获方法。将直扩MSK信号构造成近似直扩BPSK的信号形式,在高动态环境下采用基于多普勒补偿-FFT的二维联合捕获算法对伪码相位和载波频率进行搜索,得到二者的估计值。最后通过MATLAB仿真得到了二维捕获的相关输出图形以及恒虚警条件下系统的检测概率和虚警概率随信噪比变化的曲线。仿真结果表明,由该方案设计的直扩MSK信号的捕获方法能够快速而准确地对伪码相位和载波多普勒频偏进行二维捕获。