神经外科手术中脑电监测的应用

脑电活动是客观反映脑机能变化的客观指标，脑电活动有自发脑电和诱发脑电，随着科学技术的发展和进步，对自发脑电及诱发脑电的提取及处理有很大的发展，如自发脑电的提取已发展有动态脑电，数字化脑电，图像与脑电信号同步的数字化视频脑电，功能与形态结合的功能定位脑电。如视、听、体感，脑干听觉诱发电位，事件相关诱发电位等的提取均有专门的仪器，它们不仅用于科学研究近十余年已用于临床，尤其是用于神经外科手术中，     

基于自适应动态特征库的P300诱发脑电单次提取算法研究

目的 诱发电位的单次提取技术一直是脑电信息处理领域的难题之一,为进一步提高单次提取算法的时间准确性和特征精度,针对体感诱发脑电数据信噪比低、试次间参数变化大的特点,研究诱发脑电参数单次提取新算法,保留试次间诱发脑电的动态特性,并提高估计准确率.方法 基于小波滤波和多元线性分析技术,加入自适应动态特征库并由此提出的诱发脑电P300参数单次提取新方法.随机选取4组小波滤波(WF)后诱发脑电数据,分别叠加平均后进行主成分分析(PCA)组成特征库.单次提取时,针对每试次数据从特征库中选择与当次诱发脑电信号相关系数最高的成分作为自变量开展多元线性回归分析,由回归分析结构重构出单次诱发电位信号并自动提取潜伏期和幅值等关键特征.结果 与专家判定的基准数值相比,新算法预测的P300成分潜伏期与幅值参数更准确,两者的平均差值分别为(11.16±8.60) ms和(1.40±1.34)μV;与常用的叠加平均法结果亦更为接近,平均差值分别为(23.26±25.76) ms和(2.52±2.50) μV,新算法相比传统多元线性回归分析算法具有显著优势.结论 将动态更新的诱发脑电数据主成分样本库应用于小波滤波与多元线性回归方法,能有效保留单次诱发脑电数据中的动态特征,从而提升参数估计的准确率.     

脑机接口控制的下肢功能性电刺激系统研究

研发基于脑机接口控制的功能性电刺激系统,服务于下肢的运动康复。脑机接口采用的是基于稳态视觉诱发电位的脑电信号技术。使用线性判别分析分类器来处理脑电信号的频域特征,实现对下肢5种运动状态的控制意图识别,即开始、快速、慢速、停止和空闲状态。识别的意图转化为指令触发电刺激系统,刺激下肢的相关肌肉产生运动,并测量关节角度。设计的系统在6位正常受试者上进行了单纯的脑机接口实验,其中2位分别进行了脑机接口控制的小腿摆动与下肢行走的电刺激实验。在小腿摆动实验中刺激的是股直肌,行走运动实验中刺激的是两条腿的髂腰肌、臀大肌、股直肌和腘绳肌。实验分析了电刺激尾迹对脑电信号的影响,结果表明设计的脑机接口可以准确地识别运动意图(平均识别率高于85%),并能够实现电刺激作用下与该意图相对应的下肢期望运动。     

α频率光刺激脑电信号同步化的初步研究

研究α频率（8-13Hz）闪光刺激是否能引起人脑枕区同频率脑电信号的增加。采集15名正常志愿者在平静、光刺激状态下的脑电信号,利用脑地形图、小波分析和功率谱估计的方法对α频率光刺激前后的脑电信号进行处理,将归一化后的数据分为平静-激活组、男女对照组、两个不同电极位置组进行对比分析,对组间信号的差异进行了讨论,并对受试者精神状态的变化进行初步探讨。结果表明：周期性α频率光刺激能引起大脑枕区同频率脑电的显著增加。通过分析实验数据,初步得到了大脑在外部闪光刺激时脑电的变化规律,本实验结果对研究外部刺激对脑电的影响有一定的参考价值。     

α频率光刺激脑电信号同步化的初步研究

研究α频率(8～13Hz)闪光刺激是否能引起人脑枕区同频率脑电信号的增加.采集15名正常志愿者在平静、光刺激状态下的脑电信号,利用脑地形图、小波分析和功率谱估计的方法对α频率光刺激前后的脑电信号进行处理,将归一化后的数据分为平静-激活组、男女对照组、两个不同电极位置组进行对比分析,对组间信号的差异进行了讨论,并对受试者精神状态的变化进行初步探讨.结果表明:周期性α频率光刺激能引起大脑枕区同频率脑电的显著增加.通过分析实验数据,初步得到了大脑在外部闪光刺激时脑电的变化规律,本实验结果对研究外部刺激对脑电的影响有一定的参考价值.     

独立分量分析在脑电信号处理中的应用及研究进展

独立分量分析(independent component analysis,ICA)方法是从一组观测信号中提取统计独立分量的方法.因为用这种方法分解出的各信号分量之间是相互独立的,而测得的脑电信号往往包含若干相对独立的成分,所以用它来分解脑电信号,所得的结果更具有生理意义,有利于去除干扰和伪差.本文简要地回顾了ICA的发展历史和主要算法,综述了它在脑电信号处理中的应用及研究进展,并指出了需要进一步研究解决的问题.     

稳态视觉诱发脑机接口特征提取方法的研究

目的为有效提取稳态视觉诱发脑机接口(SSVEP-based brain-computer interface)中的脑电特征,提出一种基于独立成分分析(independent component analysis,ICA)与希尔伯特黄变换(HilbertHuang transform,HHT)的特征提取方法。方法对采集得到的脑电信号进行带通滤波,得到预处理的脑电信号,将滤波后的脑电信号作为ICA的输入,经过ICA实现独立成分的快速获取。引入HHT对独立成分进行经验模态分解(EMD),分解获取固有模态函数(intrinsic mode function,IMF),通过对IMF的频域分析,即可提取出特征。将ICA和HHT法同WT法、ICA法以及HHT法等常用的特征提取方法在频域、功率谱估计、在时间消耗等多方面进行比对分析。结果频域分析和功率谱估计中,本文提出的方法明显优于WT法和ICA法,略优于HHT法。时间消耗方面,本文提出的方法略优于HHT法。结论基于ICA和HHT的特征提取方法在稳态视觉诱发脑机接口的特征提取中是可行的,并有效去除了脑电信号中的噪声。     

基于复杂性测度的光诱发下alpha波脑电特性研究

目的:利用Lempel-Ziv复杂度算法,研究光诱发下α波脑电特性。方法:记录20名正常受试者(平均年龄23.2岁)在闭眼状态下接受12 Hz闪光刺激的脑电信号,分析光刺激前后α波脑电复杂度变化。结果:发现12 Hz光刺激能够引起17名受试者α波脑电复杂度的显著增加(P<0.05);其中在额部、右中央、顶部、枕部、右中颞、左右后颞α波脑电复杂度变化较为显著,以顶部与枕部变化最为明显;光刺激后,左右半脑平均α波复杂度均出现增加,其中右脑区α波复杂度变化更为显著。结论:12 Hz频率光诱发能够引起同频率段的脑波信号的显著响应,该结论有助于研究光诱发下的脑功能特性,且对于探索外部刺激对人脑认知活动的影响具有参考价值。     

观看3D电视中正负视差图片的脑电信号研究

采集记录志愿者在观看正负视差图片时的脑电信号,探究3D产品的相关参数。用自行搭建的测试系统,对10名志愿者用正负视差图片刺激,记录其脑电信号数据,采用独立成分分析方法(ICA)来处理其脑电信号,并用统计学的相关方法分析脑电与图片刺激之间的关系。结果表明,志愿者在分别观看正负视差的3D图片后,其脑电信号变化表出现明显不同,经分析发现,观看负视差图片时所有导联的Pα/Pβ比值比观看正视差图片时要小,且导联T4、FCz、C3、Pz具有统计学意义。观看负视差图片比正视差图片更能引起人体兴奋;Pα/Pβ的比值可以作为检测3D影像作品的一个参考指标。     

参数模型法提取单次诱发脑电

本文提出一种诱发脑电的参数模型处理方法,充分利用刺激前脑电(EEG)信号及迭加平均诱发电位信号,可得到单次诱发电位的无偏、最小方差估计,提高信噪比近15dB。用计算机仿真检验了模型的性能。用本文所提方法处理实际的听觉诱发脑电,得到了诱发脑电幅度与刺激强度的正相关结果,证实了在实际应用中的可靠性。     

Estimation of individual evoked potential components using iterative independent component analysis

Independent component analysis (ICA) has been successfully employed in the study of single-trial evoked potentials (EPs). In this paper, we present an iterative temporal ICA methodology that processes multielectrode single-trial EPs, one channel at a time, in contrast to most existing methodologies which are spatial and analyze EPs from all recording channels simultaneously. The proposed algorithm aims at enhancing individual components in an EP waveform in each single trial, and relies on a dynamic template to guide EP estimation. To quantify the performance of this method, we carried out extensive analyses with artificial EPs, using different models for EP generation, including the phase-resetting and the classical additive-signal models, and several signal-to-noise ratios and EP component latency jitters. Furthermore, to validate the technique, we employed actual recordings of the auditory N100 component obtained from normal subjects. Our results with artificial data show that the proposed procedure can provide significantly better estimates of the embedded EP signals compared to plain averaging, while with actual EP recordings, the procedure can consistently enhance individual components in single trials, in all subjects, which in turn results in enhanced average EPs. This procedure is well suited for fast analysis of very large multielectrode recordings in parallel architectures, as individual channels can be processed simultaneously on different processors. We conclude that this method can be used to study the spatiotemporal evolution of specific EP components and may have a significant impact as a clinical tool in the analysis of single-trial EPs.     

A novel approach for the averaging of magnetocardiographically recorded heart beats

Performing signal averaging in an efficient and correct way is indispensable since it is a prerequisite for a broad variety of magnetocardiographic (MCG) analysis methods. One of the most common procedures for performing the signal averaging to increase the signal-to-noise ratio (SNR) in magnetocardiography, as well as in electrocardiography (ECG), is done by means of spatial or temporal techniques. In this paper, an improvement of the temporal averaging method is presented. In order to obtain an accurate signal detection, temporal alignment methods and objective classification criteria are developed. The processing technique based on hierarchical clustering is introduced to take into account the non-stationarity of the noise and, to some extent, the biological variability of the signals reaching the optimum SNR. The method implemented is especially designed to run fast and does not require any interaction from the operator. The averaging procedure described in this work is applied to the averaging of MCG data as an example, but with its intrinsic properties it can also be applied to the averaging of ECG recording, averaging of body-surface-potential mapping (BSPM) and averaging of magnetoencephalographic (MEG) or electroencephalographic (EEG) signals.     

Median averaging of electromyographic motor unit action potentials: comparison with other techniques

A technique of extracting individual motor unit action potentials (MUAPs) from EMG signals by median averaging, a modification of an existing method, is presented. To compare different techniques of MUAP extraction, 89 MUAPs were recorded with a concentric needle electrode in the brachial biceps muscle of normal subjects and patients with nerve and muscle diseases. MUAPs were also extracted by another method, called split-sweep median averaging, in which alternate MUAP discharges are averaged independently in two computer buffers until the two averaged signals appear equal on visual inspection by the operator. The amplitude, area, area: amplitude ratio, duration and number of phases and turns of each extracted MUAP were determined by each technique. Overall, there was a strong correlation between all features of the MUAPs extracted by median and splitsweep averaging, although the latter method required, on average, twice as many MUAP discharges to produce acceptable signals. We thus conclude that median averaging is a fast and accurate method that requires relatively few MU discharges to extract MUAP signals from spurious background signals.     

Wavelet-based enhancement of signalaveraged electrocardiograms for late potential detection

An optimal wavelet filter to improve the signal-to-noise ratio (SNR) of the signal-averaged electrocardiogram is described. As the averaging technique leads to the best unbiased estimator, the challenge is to attenuate the noise while preserving the low amplitude signals that are usually embedded in it. An optimal, in the meansquare sense, wavelet-based filter has been derived from the model of the signal. However, such a filter needs exact knowledge of the noise statistic and the noise-free signal. Hence, to implement such a filter, a method based on successive subaveraging and wavelet filtering is proposed. Its performance was evaluated using simulated and real ECGs. An improvement in SNR of between 6 and 10 dB can be achieved compared to a classical averaging technique which uses an ensemble of 64 simulated ECG beats. Tests on real ECGs demonstrate the utility of the method as it has been shown that by using fewer beats in the filtered ensemble average, one can achieve the same noise reduction. Clinical use of this technique would reduce the ensemble needed for averaging while obtaining the same diagnostic result.     

Wavelet-based enhancement of signal-averaged electrocardiograms for late potential detection

An optimal wavelet filter to improve the signal-to-noise ratio (SNR) of the signal-averaged electrocardiogram is described. As the averaging technique leads to the best unbiased estimator, the challenge is to attenuate the noise while preserving the low amplitude signals that are usually embedded in it. An optimal, in the mean-square sense, wavelet-based filter has been derived from the model of the signal. However, such a filter needs exact knowledge of the noise statistic and the noise-free signal. Hence, to implement such a filter, a method based on successive sub-averaging and wavelet filtering is proposed. Its performance was evaluated using simulated and real ECGs. An improvement in SNR of between 6 and 10 dB can be achieved compared to a classical averaging technique which uses an ensemble of 64 simulated ECG beats. Tests on real ECGs demonstrate the utility of the method as it has been shown that by using fewer beats in the filtered ensemble average, one can achieve the same noise reduction. Clinical use of this technique would reduce the ensemble needed for averaging while obtaining the same diagnostic result.     

一新型的脑电有线传输技术

本文介绍了一种新的脑电信号有线传输技术：经公共电话网络、通过一个调频信道、利用时分多路复用方式可同步传输多达20导联的脑电信号;通过另一个调频信道,接收方还能实时获知受测试者的当前意识状态以及实现收发双方的同步。经在此基础上开发的脑电遥测系统的使用验证：该技术能涵盖大部分临床诊断的实际需要、并且信号的传输质量较好、失真度小、无明显噪声污染、无基漂。     

一种新型的脑电有线传输技术

本文介绍了一种新的脑电信号有线传输技术；经公共电话网络、通过一个调频信道、利用时分多路复用方式可同步传输多达20导联的脑电信号；通过另一个调频信道，接收方还能实时获知受测试者的当前意识状态以及实现收发双方的同步。经在此基础上开发的脑电遥测系统的使用验证：该技术能涵盖大部分临床诊断的实际需要、并且信号的传输质量较好、失真度小、无明显噪声污染、无基漂。     

用奇异性检测技术提取诱发电位

本文介绍了用 小波变换模极大 值检测信号奇 异性的方法， 讨论了 信号与 白噪声 的小 波变换 和奇 异性指数之间区 别，模极大值沿 尺度 传递 的不同 特点 ，并 利用它 们的 区别 消除 诱发 电位 信号 中的噪 声，重 构出消 噪后 的信号， 取得 了较好 的效果 ，利用 较少的 刺激 次数就 可获取 诱发电 位信 号，有 效地提高 了信 噪比，大 大减 少了迭 加次数 ，特征 波的潜 伏期 及幅值 容易辨 认，易 于测量 ，且无 信号 失真。奇异性检 测技术有望成为 临床实用的诱 发电位提取 技术，并 可应用 于其他 生物 医学信 号的 消噪。     

Measurement of signal and noise characteristics in ongoing auditory brainstem response averaging

Although diagnostic testing with auditory evoked potentials (EPs) has become routine, quantitative measurements of signal and noise are still lacking. In this study, current signal, power, noise power, and signal-to-noise ratio (SNR) estimation formulas are reviewed and applied to auditory brainstem response averaging. Single-sweep responses to individual sound stimuli are recorded and estimation formulas are evaluated during off-line averaging under various sound level and noise conditions. The results show that the quality of the averaged EP can be quantitatively assessed by the continuous display of the SNR and residual noise estimates during the averaging process. This method also allows the study of different types of averaging techniques to improve EP response acquisition.     

基于独立分量分析的诱发电位信号噪声分离方法

诱发电位(EP)信号的检测与分析技术是临床医学诊断神经系统损伤及病变的重要手段之一。但是，从人体体表所得到的EP信号含有大量的噪声，最典型的噪声是人体自发产生的脑电图信号(EEG)。因此，为利用EP信号诊断神经系统的损伤和病变，需要从混合信号中去除EEG等噪声。独立分量分析(ICA)是一种新近发展起来的统计信号处理方法。本文把ICA方法应用于EP信号的噪声消除，并与传统的自适应滤波方法进行了比较。计算机模拟表明，采用ICA方法进行信号噪声分离的结果明显优于自适应滤波方法。