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。     

Biogeography based hybrid scheme for automatic detection of epileptic seizures from EEG signatures

Biogeography Based Optimization (BBO) algorithm is one of the nature-inspired optimization methods, based on the study of geographical distribution of species on earth. The present research work is based upon decomposition of human electroencephalograms (EEG) signal by Wavelet Packet Transform, computation of a complete feature set using statistical and non-statistical properties followed by optimal selection of features by BBO; the optimality criterion being classification rate. The stopping criterion for BBO is set to 100% correct classification rate. The proposed algorithm is novel in terms of TWSVM computing the Habitat Suitability Index (HSI) values for BBO, perfect classification rate, low computation time, and feature selection mechanism. The proposed scheme outperforms several previous results reported in literature in that it gives a feature subset which gives 100% classification accuracy for different classification instances.     

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%。     

Multi-class classification of construction hazards via cognitive states assessment using wearable EEG

Improving workers’ safety and health is one of the most critical issues in the construction industry. Research attempts have been made to better identify construction hazards on a jobsite by analyzing workers’ physical responses (e.g., stride and balance) or physiological responses (e.g., brain waves and heart rate) collected from the wearable devices. Among them, electroencephalogram (EEG) holds unique potential since it reveals abnormal patterns immediately when a hazard is perceived and recognized. Unfortunately, the unproven capacity of EEG signals for multi-hazard classification is a primary barrier towards ubiquitous hazard identification in real-time on jobsites. This study correlates EEG signal patterns with construction hazard types and develops an EEG classifier based on the experiments conducted in an immersive virtual reality (VR) environment. Hazards of different types (e.g., fall and slip/trip) were simulated in a VR environment. EEG signals were collected from subjects who wore both wearable EEG and VR devices during the experimentation. Two types of EEG features (time-domain/frequency-domain features and cognitive features) were extracted for training and testing. A total of eighteen advanced machine learning algorithms were used to develop the EEG classifier. The initial results showed that the LightGBM classifier achieved 70.1% accuracy based on the cognitive feature set for the 7-class classification. To improve the performance, the input data was relabeled, and three strategies were designed and tested. As a result, the combined approach (two-step ensemble classification) achieved 82.3% accuracy. As such, this study not only demonstrates the feasibility of coupling wearable EEG, VR, and machine learning to differentiate jobsite hazards but also provides strategies to improve multi-class classification performance. The research results support ubiquitous hazard identification and thereby contribute to the safety of the construction workplace.     

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。     

基于总体经验模态分解的多类特征的运动想象脑电识别方法研究

人的脑电信号（Electroencephalogram,EEG）复杂且具有非线性及非平稳性的特点使其不易分析处理,其识别效果也依赖于数据集的不同,而表现不稳定.本文中应用的总体经验模态分解（Ensemble empirical mode decomposition,EEMD）是一种具有强自适应性的信号处理方法,其在时频域展现的良好分辨率特别适合脑电识别任务处理.本文提出利用EEMD分解后得到的较具影响能力的固有模态函数（Intrinsic mode functions,IMFs）,利用希尔伯特变换提取边际谱（Marginal spectrum,MS）及瞬时能谱（Instantaneous energy spectrum,IES）时频特征,同时通过加窗的方法提取非线性动力学特征近似熵特征,利用线性判别分类器（Linear discriminant analysis,LDA）作为分类器,实验结果得出,对于被试S2和被试S3可达到识别率分别为79.60%和87.77%,实验中9名被试的平均识别率为82.74%,得到平均识别率也高于近期使用相同数据集文献的其他方法.     

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.     

基于眼动辅助脑电信号的手部动作分类方法

提出一种利用小波包变换和支持向量机对手部动作的运动想象脑电信号进行分类的方法。在相关眼动辅助情况下采集想象手部动作时的C3、C4 、P3和P4通道脑电信号,用小波包变换的方法提取4种特征节律波,分别计算每种节律波能量占4种节律波能量之和的比值作为特征,然后将16维特征向量输入支持向量机分类器进行手部动作分类。对上翻、下翻、展拳、握拳4种手部动作的分类实验中平均识别率为82。3%,表明眼动辅助能有效提高运动想象脑电信号可分性。