三自由度前臂假手的肌电控制系统

生物电应用于控制早在五十年代就有人研究,只是随着电子工业的发展才逐步走向实用阶段。对于假手来说,目前国际上已有商品化的单自由度假手出现,而多自由度假手正在许多国家的实验室里进行研究。虽然有些国家的假肢行业以为肌电信号不稳定、易受干扰,而不欢喜肌电控制。但是随着对肌电信号的深入研究和电子工业中大规模集成电路的发展,以及人们期望有更完善的假手,因而肌电控制的多自由度假手仍然成为人们竞相研究的对象。我们所研制的是肌电控制三自由度前臂假手,是在手腕关节部位实现手指的开闭,腕的伸屈和腕的内外旋。假手的肌电控制系统包括肌电信号源的选定、控制逻辑的组成、表面导引电极和肌电信号放大器及数字逻辑控制电路等部分。     

基于独立元分析和非线性指数分析的脑电信号中伪迹分量的自动去除

脑电(electroencephalography,EEG)信号中不可避免地存在着眼动、心跳、肌电信号以及线性噪声等伪迹干扰,这些伪迹的存在极大地影响了脑电信号分析的准确性,因此在进行脑电信号分析前需要去除伪迹干扰。为了有效地去除伪迹,结合独立元分析和非线性指数分析,提出一种自动识别并去除脑电信号中伪迹分量的方法。该方法还可同时用于提取脑电信号中的基本节律如!波等。相应的模拟与实际脑电数据的实验结果表明所提议的方法具有很好的识别和去除脑电信号伪迹分量的性能。     

基于脑电信号识别采集的假肢控制系统

本文介绍了脑电信号（EEG）的模式识别和步骤,分析了EEG采集领域的发展和医学原理。通过研究脑电信号和假肢运动的联系,总结脑电控制假肢的可行性结论。设计出从头皮电极到模／数转换器的基于脑电信号识别采集的假肢控制系统,能够满足脑电假肢的各种要求。     

脑死亡与脑昏迷脑电信号的复杂度研究

脑死亡诊断是有关病人生死的重要问题.许多国家都把脑电平坦列为脑死亡诊断的基本条件,但研究发现并非所有的脑死亡患者均表现为脑电平坦,同时脑昏迷患者在部分情况下也会表现出脑电平坦的现象,从而有可能在临床中造成误判.C0复杂度判断指标能够利用脑电信号中的复杂度特性帮助临床诊断中对于脑死亡和脑昏迷状况的鉴别.运用C0复杂度算法对22位脑死亡和脑昏迷病例进行分析实验,可以发现脑死亡脑电信号的复杂度明显高于脑昏迷脑电信号的复杂度.实验表明C0复杂度可以用来有效地区分脑死亡和脑昏迷脑电信号,具有潜在的重要临床价值.     

含被动关节的仿人型塑料电子假手研究

为了突破传统假手的不足,使假手在形状和功能上更像人手、重量进一步减轻,设计了包含被动关节的仿人型塑料电子假手。在健全手的帮助下,假手手指关节可被动弯曲和旋转;与传统单自由度假手相比,可以完成更加复杂的动作。仿真塑料假手样机重量较轻、外形美观,通过配置肌电控制系统实验,实现了对假手实现准确可靠的控制。     

肌电控制假手电池选型原则及容量计算

本文在分析肌电控制电动假手电池选型原则的基础上,提出一个这种假手的电池容量简单计算方法,并附有二个实例加以说明。     

基于小波变换的单次诱发电位信号时频分析

比较小波变换和平均叠加两种方法提取“模拟自然阅读”刺激模式下的诱发脑电信号,分析其时频特性,并进行脑功能源分布定位分析。结果显示,采用平均叠加法来提取和分析诱发电位信号,损失了某些重要的诱发电位成分,且其功能源分布定位反映的只是等效功能源的静态过程;而使用小波变换和脑功能源定位来提取和分析单次诱发电位信号,既能观察到丰富的诱发电位成分,又能反映脑功能源的实时动态活动过程。这表明,小波变换下的时频分析是脑电信号处理的一种可行的新方法。     

癫痫病人脑电信号的奇异谱

癫痫是一种常见的神经系统疾患,其唯一客观证据为脑电图的癫痫样发放。在癫痫发作间期,仅有偶发的很难辨别的癫痫样放电,为了正确诊断癫痫病,往往需要医生长时间监测病人的脑电信号,在对脑电信号进行相空间重构,进而对其进行奇异系统分析,发现癫痫病人无论在癫痫发作前、发作中、发作后,其脑电信号的奇异谱曲线不存在噪声平台,明显区别于正常人。是否可以认为脑电信号的奇异谱正代表着大脑的一种基本状态,癫痫患者在未发作时,大脑的基本状态已经处于异常。无论如休,奇异系统分析方法使得可以利用很短的一段脑电数据诊断癫痫。无疑为癫痫病人的临床诊断提供了一条简单、有效的途径。     

Labview在动物脑电信号采集分析中的应用

分析动物行为活动中的脑电特征是脑机接口(Brain-computer interface,BCI)研究中的一个重要内容.本文利用最新测控软件-虚拟仪器技术(LabVIEW)进行脑电信号采集与处理,实现了信号实时显示、中值滤波、小渡消噪的设计.实验结果显示提取出了与特定行为(抓食)相关的脑电活动特征信号,为研究大脑如何控制行为提供了一个有效的方法.     

铅染毒大鼠脑电信号的分形特性研究

利用混沌时间序列的分析方法,对铅中毒情况下的大鼠的脑电波进行了测试和分析,求出了其分形维数。从研究结果中得出了铅中毒情况与脑电信号的分形维数之间的关系,结果表明,铅中毒会引起脑电信号的分形维数的显著变化。通过这一工作,能够为临床上重金属中毒的早期诊断提供新的方便、灵敏的指标。     

基于小波包熵的运动意识任务分类研究

提出了以小波包熵作为脑电特征向量的左右手运动意识任务分类方法,对被测试者想象左右手运动时的脑电小波包熵动态变化情况及分析窗口长度的选择进行了研究.结果表明,小波包熵能很好地反映左右手运动想象的脑电特征变化,用线性判别式算法对脑电特征进行识别,分类正确率达到92.14%.由于小波包熵的计算比较简单,稳定性好,识别率高,为大脑运动意识任务的分类提供了新思路.     

Decoding Individual Finger Movements from One Hand Using Human EEG Signals

Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.     

A review on directional information in neural signals for brain-machine interfaces

Brain-machine interfaces (BMIs) can be characterized by the technique used to measure brain activity and by the way different brain signals are translated into commands that control an effector. We give an overview of different approaches and focus on a particular BMI approach: the movement of an artificial effector (e.g. arm prosthesis to the right) by those motor cortical signals that control the equivalent movement of a corresponding body part (e.g. arm movement to the right). This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single-units. Here, we review recent findings showing that analog neuronal population signals, ranging from intracortical local field potentials over epicortical ECoG to non-invasive EEG and MEG, can also be used to decode movement direction and continuous movement trajectories. Therefore, these signals might provide additional or alternative control for this BMI approach, with possible advantages due to reduced invasiveness.     

Time-Dependent Statistical and Correlation Properties of Neural Signals during Handwriting

To elucidate the cortical control of handwriting, we examined time-dependent statistical and correlational properties of simultaneously recorded 64-channel electroencephalograms (EEGs) and electromyograms (EMGs) of intrinsic hand muscles. We introduced a statistical method, which offered advantages compared to conventional coherence methods. In contrast to coherence methods, which operate in the frequency domain, our method enabled us to study the functional association between different neural regions in the time domain. In our experiments, subjects performed about 400 stereotypical trials during which they wrote a single character. These trials provided time-dependent EMG and EEG data capturing different handwriting epochs. The set of trials was treated as a statistical ensemble, and time-dependent correlation functions between neural signals were computed by averaging over that ensemble. We found that trial-to-trial variability of both the EMGs and EEGs was well described by a log-normal distribution with time-dependent parameters, which was clearly distinguished from the normal (Gaussian) distribution. We found strong and long-lasting EMG/EMG correlations, whereas EEG/EEG correlations, which were also quite strong, were short-lived with a characteristic correlation durations on the order of 100 ms or less. Our computations of correlation functions were restricted to the spectral range (13–30 Hz) of EEG signals where we found the strongest effects related to handwriting. Although, all subjects involved in our experiments were right-hand writers, we observed a clear symmetry between left and right motor areas: inter-channel correlations were strong if both channels were located over the left or right hemispheres, and 2–3 times weaker if the EEG channels were located over different hemispheres. Although we observed synchronized changes in the mean energies of EEG and EMG signals, we found that EEG/EMG correlations were much weaker than EEG/EEG and EMG/EMG correlations. The absence of strong correlations between EMG and EEG signals indicates that (i) a large fraction of the EEG signal includes electrical activity unrelated to low-level motor variability; (ii) neural processing of cortically-derived signals by spinal circuitry may reduce the correlation between EEG and EMG signals.     

Des sources neuronales aux signaux EEG : modèle et interprétation des activités épileptiques

This work deals with the interpretation of electrophysiological patients recorded in epileptic patients candidate to surgery. This issue is addressed through a physiologically relevant model for the generation of scalp and intracerebral electroencephalographic (EEG) signals. The proposed model is based on a spatiotemporal representation of the sources of brain activity, which combines a distributed dipole source model and a model of coupled neuronal populations. Signals recorded by sensors (scalp and intracerebral) are then computed by solving the forward problem in the head volume conductor. In this paper, the EEG generation model is used to study the influence of some source-related parameters (spatial extent, position, synchronization) on simulated signals, during epileptic transient activity (interictal spikes). Results show that the model allows for studying, on the one hand, the relationship between the spatiotemporal organization of neuronal sources and the properties of the observed signals and, on the other hand, the relationship between surface and depth EEG signals.     

Effects of modulated RF energy on the EEG of mammalian brains

The effects of modulated radio frequency fields on mammalian EEGs were investigated using acute and chronic irradiations at non-thermal level. The EEG signals were computer processed to obtain power spectra. Rabbits were exposed to the field for 2 h a day for 6 weeks at 1-10 MHz (15 Hz modulation) at the level of 0.5-1 kV/M. Silver electrodes placed on the skull surface were used for recording of the EEG. Usually they were removed immediately after initial recordings of the EEG and reinserted before the final and intermediate EEG recordings. With this arrangement, modulated RF fields produced a change in EEG patterns by enhancing the low frequency components and decreasing high frequency activities. On the other hand, acute irradiations did not produce noticeable changes in the EEG at the level of 0.5-1 kV/M (1-30 MHz, 60 Hz modulation) as long as the use of intracranial electrodes was avoided.     

Functional Corticospinal Projections from Human Supplementary Motor Area Revealed by Corticomuscular Coherence during Precise Grip Force Control

The purpose of the present study was to investigate whether corticospinal projections from human supplementary motor area (SMA) are functional during precise force control with the precision grip (thumb-index opposition). Since beta band corticomuscular coherence (CMC) is well-accepted to reflect efferent corticospinal transmission, we analyzed the beta band CMC obtained with simultaneous recording of electroencephalographic (EEG) and electromyographic (EMG) signals. Subjects performed a bimanual precise visuomotor force tracking task by applying isometric low grip forces with their right hand precision grip on a custom device with strain gauges. Concurrently, they held the device with their left hand precision grip, producing similar grip forces but without any precision constraints, to relieve the right hand. Some subjects also participated in a unimanual control condition in which they performed the task with only the right hand precision grip while the device was held by a mechanical grip. We analyzed whole scalp topographies of beta band CMC between 64 EEG channels and 4 EMG intrinsic hand muscles, 2 for each hand. To compare the different topographies, we performed non-parametric statistical tests based on spatio-spectral clustering. For the right hand, we obtained significant beta band CMC over the contralateral M1 region as well as over the SMA region during static force contraction periods. For the left hand, however, beta band CMC was only found over the contralateral M1. By comparing unimanual and bimanual conditions for right hand muscles, no significant difference was found on beta band CMC over M1 and SMA. We conclude that the beta band CMC found over SMA for right hand muscles results from the precision constraints and not from the bimanual aspect of the task. The result of the present study strongly suggests that the corticospinal projections from human SMA become functional when high precision force control is required.     

On the Usage of Linear Regression Models to Reconstruct Limb Kinematics from Low Frequency EEG Signals

Several works have reported on the reconstruction of 2D/3D limb kinematics from low-frequency EEG signals using linear regression models based on positive correlation values between the recorded and the reconstructed trajectories. This paper describes the mathematical properties of the linear model and the correlation evaluation metric that may lead to a misinterpretation of the results of this type of decoders. Firstly, the use of a linear regression model to adjust the two temporal signals (EEG and velocity profiles) implies that the relevant component of the signal used for decoding (EEG) has to be in the same frequency range as the signal to be decoded (velocity profiles). Secondly, the use of a correlation to evaluate the fitting of two trajectories could lead to overly-optimistic results as this metric is invariant to scale. Also, the correlation has a non-linear nature that leads to higher values for sinus/cosinus-like signals at low frequencies. Analysis of these properties on the reconstruction results was carried out through an experiment performed in line with previous studies, where healthy participants executed predefined reaching movements of the hand in 3D space. While the correlations of limb velocity profiles reconstructed from low-frequency EEG were comparable to studies in this domain, a systematic statistical analysis revealed that these results were not above the chance level. The empirical chance level was estimated using random assignments of recorded velocity profiles and EEG signals, as well as combinations of randomly generated synthetic EEG with recorded velocity profiles and recorded EEG with randomly generated synthetic velocity profiles. The analysis shows that the positive correlation results in this experiment cannot be used as an indicator of successful trajectory reconstruction based on a neural correlate. Several directions are herein discussed to address the misinterpretation of results as well as the implications on previous invasive and non-invasive works.     

Method for Simultaneous fMRI/EEG Data Collection during a Focused Attention Suggestion for Differential Thermal Sensation

In the present work, we demonstrate a method for concurrent collection of EEG/fMRI data. In our setup, EEG data are collected using a high-density 256-channel sensor net. The EEG amplifier itself is contained in a field isolation containment system (FICS), and MRI clock signals are synchronized with EEG data collection for subsequent MR artifact characterization and removal. We demonstrate this method first for resting state data collection. Thereafter, we demonstrate a protocol for EEG/fMRI data recording, while subjects listen to a tape asking them to visualize that their left hand is immersed in a cold-water bath and referred to, here, as the cold glove paradigm. Thermal differentials between each hand are measured throughout EEG/fMRI data collection using an MR compatible temperature sensor that we developed for this purpose. We collect cold glove EEG/fMRI data along with simultaneous differential hand temperature measurements both before and after hypnotic induction. Between pre and post sessions, single modality EEG data are collected during the hypnotic induction and depth assessment process. Our representative results demonstrate that significant changes in the EEG power spectrum can be measured during hypnotic induction, and that hand temperature changes during the cold glove paradigm can be detected rapidly using our MR compatible differential thermometry device.     

基于脑电四阶累积量的运动意识分类研究

提出了基于四阶累积量为脑电特征的意识任务分类思想.对被测试者想象左右手运动时的脑电归一化四阶累积量（峭度）及其动态变化情况进行了研究.结果表明,归一化四阶累积量能较好地反映左右手运动想象的脑电特征变化.在此基础上,进行了基于脑电四阶累积量的左右手运动意识识别和分类研究,实验结果表明,正确识别率能达到87.5%.由于四阶累积量的计算比较简单,而且可在线计算,因此可以认为,基于脑电四阶累积量为特征的运动意识分类及其在脑机接口技术中的应用,具有较高的实际应用价值.