基于ADS1299的可穿戴式脑电信号采集系统前端设计

设计了一款可穿戴式脑电采集前端,具有采集精度高、体积小、功耗低、抗干扰性强等特点。采用ADS1299内部集成的可编程放大器(PGA)实现微弱信号的放大;同时为了消除干扰,使用限幅滤波预处理电路和ADS1299内部集成的偏置驱动放大器。实验测试表明,该脑电采集前端设计能较好地把微弱的脑电信号提取出来,并且具有较好的抗干扰能力和实用价值。     

ADS1298模拟前端的便携式生理信号采集系统

介绍了一种便携式多功能生理信号采集装置,用户通过简单设置及选择相应电极,可分别进行脑电和心电数据的实时采集,并能对数据进行显示和存储。它具有精度高、体积小、功耗低等特点。该系统下位机主要由ST公司的STM32单片机STM32F103和TI公司的ADS1298模拟前端IC构成,省去了大量的外围电路。下位机通过USB2．0...     

基于模拟前端ADS1299的脑电信号采集系统

设计了一套脑电信号采集系统,能够便捷地采集人体脑电信号,具有体积小、精度高、简单易用的特点.系统包括硬件和软件两大部分,硬件部分主要由最新的24位模拟前端ADS1299和AVR单片机构成.系统能够采集8通道的脑电数据,通过USB转串口模块上传数据至上位机.软件部分使用Java编程实时显示与存储采集到的脑电信号,随后通过Matlab对采集到的脑电信号进行后续分析.实验结果显示:本系统能够精确显示出眼电波形,当被试者闭眼时,枕叶电极的α波段波形尤为明显.     

脑电信号采集系统设计方法

分析了脑电信号的特点,根据系统性能的要求,提出了一整套系统设计方案,具有高输入阻抗、高共模抑制比的特点,具有良好的实时性和通用性。其中,信号采集模块是将电极记录的脑电信号进行三级的滤波放大,然后进行模/数转换。     

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。     

基于USB的脑电信号采集系统的设计

设计了基于USB接口的脑电信号采集系统,并给出了系统硬件组成和软件设计。系统采用了带有USB设备控制器的C8051F320单片机,具有丰富的内部资源,可以很好的满足系统的实际需要。     

基于LABVIEW的脑电信号虚拟采集系统设计

在神经科学研究领域,对大脑的观察主要来源于对脑电信号的收集与分析;当前对脑电信号收集的方法是通过专业脑电设备将信号收集保存,再由专业软件处理;由于这类仪器非常昂贵,系统体积也比较大,软件更新快,现在只能用在科学研究上,根本无法用于有规模的实验教学,更不可能一人一机;为此,提出了一种基于LABVIEW的脑电信号虚拟采集系统设计方法,使脑电收集与分析可以广泛地应用于教学;该方法首先对脑电信号虚拟采集系统的硬件进行构造,然后以硬件构造为依据,利用AR模型功率谱估计对脑电信号进行特征提取,在特征提取过程中,对模型类型与模型系数算法以及模型最佳阶数进行分析,最后通过将二阶低通滤波器与二阶高通滤波器进行串联,形成4阶Bessel带通滤波器,实现脑电信号的滤波,并以脑电信号传输电路的设计完成脑电信号虚拟采集系统的设计;实验结果证明,所提方法可以快速地对脑电信号虚拟采集系统进行设计,并为该领域的研究发展提供支撑。     

基于AD574A的脑电信号采集及在线仿真

脑电信号EEG是一种微弱的低频生理信号,它由脑部神经活动产生的自发性电位活动,含有非常丰富的大脑活动信息,是进行临床脑疾病诊断的一种重要方法,因此获取脑电信号具有非常重要的现实意义。介绍了高速12位A/D转换器AD574A,及其低功耗,高精度等优点。根据其转换原理,论述其在脑电信号采集系统中的应用。系统采用FPGA芯片EP2C8Q208C8来控制AD574A的转换,给出了硬件连接电路和软件实现。在Quartus II 9.0中采用VHDL进行程序设计,针对系统的高速和可靠性要求,软件设计采用有限状态机FSM控制,并进行仿真验证,具有实际应用参考价值。     

低功耗便携式多导联心电信号采集系统设计

针对家用医疗监护领域的需求,设计了一种低功耗、便携式多导联心电信号采集系统。采用直流耦合方法,通过生物电位测量ADS1298模拟前端,实现微弱心电信号的采集。在模拟前端中,心电信号经过可编程仪表放大器进行放大,进入24位高分辨率模/数转换器(ADC)转换成数字信号;采用STM32F103微处理器进行控制,通过低功耗ZigBee实现心电信号的无线传输;设计了预处理电路、右腿驱动和屏蔽线驱动电路减小了高频和共模干扰;系统采用锂电池供电,体积小(7 cm×8 cm×2 cm),功耗低(214. 5 m W)。实验结果表明:设计的系统能长时间、稳定、有效地提取心电信号。     

Direct effects of audio-visual stimulation on EEG

In the course of 2 months, 25 repetitions of a 20 min audio-visual stimulation (AVS) program with stimulations at 17, 9, 4, and 2 Hz were applied to 6 volunteers. EEG data were recorded from 6 scalp locations prior, during and after AVS. In order to identify direct and transient changes in EEG under influence of AVS, total power, relative frequency band powers and magnitude-squared coherences were estimated. Intense brain wave entrainment as a direct reaction to AVS was significant through increase of spectral powers and coherences around the stimulating frequency bands in the occipital areas, spreading also to the central and frontal regions. However, these excitations were ‘short-lived’. On the other hand some signs of interhemispheric cooperation (coherences in the narrow bands around 2, 4, and 17 Hz at parieto-occipital areas) remained increased during the investigated 3 min after AVS. As going through further AVS sessions the driving response progressively enhanced for 2 and 4 Hz stimulation in centro-parietal locations. Progress was also found in the left and right hemisphere synchronization examined by coherences. In perspective, the results contribute to deeper comprehension of photic stimulation approaches as a technique of guided entrainment of the brain waves or intermediate increase of hemispheres’ synchronization.     

基于局部模式的癫痫脑电信号自动分类方法

为有效地检测脑电图(EEG)中的癫痫信号,设计一维局部三值模式(1D-LTP)算子提取信号特征,并结合主成分分析(PCA)和极限学习机(ELM)对特征进行分类。通过1D-LTP算子计算信号点的顶层模式和底层模式下的特征变换码以准确滤除干扰信号,并对变换码直方图PCA降维后采用ELM进行分类,以10折交叉验证评估分类性能。实验结果表明,该方法能有效识别在癫痫发作期的EEG信号,其准确率可达99.79%。     

Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface

In order to characterize the non-Gaussian information contained within the EEG signals, a new feature extraction method based on bispectrum is proposed and applied to the classification of right and left motor imagery for developing EEG-based brain-computer interface systems. The experimental results on the Graz BCI data set have shown that based on the proposed features, a LDA classifier, SVM classifier and NN classifier outperform the winner of the BCI 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate.     

EEG信号动态演化过程的研究

脑电图(EEG)信号的研究是诊断脑疾患的重要手段。以癫痫脑电为例,针对癫痫发作过程的复杂性,对其演化过程进行研究。利用本征正交分解(POD)对EEG信号实行特征压缩,选取能够反映EEG脑电病理特征的多个变量,通过改进的Fisher判别方法判别分解后的信号数据,以最终确定EEG信号动态演化过程的关键点。实验结果表明,将POD分解与Fisher判别方法相结合,不仅能减少数据分析的工作量,而且能够有效判别分析EEG信号动态演化过程。     

Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition

Epilepsy is one of the most common neurological disorders characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities, containing information relating to the different physiological states of the brain. It is a very effective tool for understanding the complex dynamical behavior of the brain. This paper presents the application of empirical mode decomposition (EMD) for analysis of EEG signals. The EMD decomposes a EEG signal into a finite set of bandlimited signals termed intrinsic mode functions (IMFs). The Hilbert transformation of IMFs provides analytic signal representation of IMFs. The area measured from the trace of the analytic IMFs, which have circular form in the complex plane, has been used as a feature in order to discriminate normal EEG signals from the epileptic seizure EEG signals. It has been shown that the area measure of the IMFs has given good discrimination performance. Simulation results illustrate the effectiveness of the proposed method.     

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.     

EEG signal classification using wavelet feature extraction and a mixture of expert model

Mixture of experts (ME) is modular neural network architecture for supervised learning. A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME network structure for detection of epileptic seizure. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. EEG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT). Then these sub-band frequencies were used as an input to a ME network with two discrete outputs: normal and epileptic. In order to improve accuracy, the outputs of expert networks were combined according to a set of local weights called the “gating function”. The invariant transformations of the ME probability density functions include the permutations of the expert labels and the translations of the parameters in the gating functions. The performance of the proposed model was evaluated in terms of classification accuracies and the results confirmed that the proposed ME network structure has some potential in detecting epileptic seizures. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network model.     

基于K近邻互信息估计的EEG伪迹消除方法

针对脑电信号中的眼电和心电串扰伪迹,提出一种基于最小相依成分分析的互信息(MILCA)算法的伪迹消除方法.在提升小波硬阈值法对多路原始脑电信号去噪基础上,运用MILCA算法对各通道信号进行盲源分离,同时采用信号间互相关系数和互信息量作为指标,分析伪迹分离程度.与Extend Infomax、FastICA 2种常见盲源分离算法的对比结果表明,运用MILCA算法对脑电信号中的眼电及心电伪迹的分离结果最理想.