基于EEG脑机接口的研究现状及在康复中的应用

脑机接口(brain computer interface,BCI)是一项不依赖大脑常规信息通路就可实现与外界环境交流的技术,通过该技术可在人脑与计算机或其他电子设备之间建立一种直接的联系,使人不依赖正常的骨骼肌肉系统就可直接控制外部设备,这就为那些思维正常但有严重功能障碍的患者带来了新的希望。近20年来BCI实现了从无到有、脑电信号从单一到混合等技术上的飞速发展,另外随着识别任务数目的增多、信息传输率及识别准确性的提高,BCI也逐渐走出了实验室,朝着实时、实用的方向发展。本文主要针对基于EEG的单一模式和混合模式脑机接口的研究现状及其在康复医学中的实际应用进行了综述,总结了不同模式BCI的功能特点及其在康复医学领域的应用潜力,以期促进BCI在康复应用中进一步发展,并提高目前康复治疗技术。     

基于CSP的多类运动想象脑电特征自动选择算法

目的在基于协方差矩阵近似联合对角化(joint approximation diagonalization,JAD)的多类共空间模式(common spatial pattern,CSP)运动想象检测滤波器的设计过程中,需要对关键特征向量进行选择。较常用的基于"最高得分特征值准则"的特征向量选择方法会出现不同类数据的最高得分特征值对应同一个特征向量,因此导致无效CSP滤波器的出现,进而影响系统识别率。本文在传统JAD方法上提出一种特征值自动选择方法以解决特征值选择无效问题。方法基于BCI Competition 2005data IIIa(BCI2005)和实验室自主采集三类运动想象脑电(EEG)数据集,对不同想象类别数据对应同一个特征向量的异常现象进行实验分析。结果在两个数据集自测试下,本方法的三类运动想象平均识别率分别达到82.78%和85.92%,比传统JAD提高3.44%和3.25%。结论基于CSP的多类运动想象脑电特征自动选择算法能够有效解决特征值选择无效问题,进而提升运动想象BCI系统的分类识别率。     

基于认知脑区的脑-机接口技术及其在康复中的应用研究进展

脑-机接口(BCI)技术通过解码大脑信号可实现人类和外部设备的交互,近年来取得了一些重要的突破,但其应用推广目前还存在许多障碍。当前常见的BCI控制信号一般来源于与感觉运动相关的脑区,这些信号仅能反映肢体运动意图的有限部分。因此,需要探索更多可用于控制BCI系统的脑信号源。基于认知脑区的脑信号具有更加直观、有效的特点,可作为拓展脑BCI信号源的新途径。本文综述了基于单一脑区和多脑区混合的认知BCI的研究现状,并归纳了其在康复医学领域的应用研究,以期将基于认知的BCI技术作为未来BCI康复应用的突破口。     

脑-机接口:大脑对外信息交流的新途径

基于脑电(EEG)的脑-机接口(BCI)是在人脑和计算机或其它电子设备之间建立不依赖于常规大脑信息输出通路(外周神经和肌肉组织)的全新对外信息交流和控制技术,概述了基于EEG的BCI技术的科学意义与应用前景,并介绍了BCI主要研究方法和类型。     

脑-机接口：大脑对外信息交流的新途径

基于脑电(EEG)的脑-机接口(BCI)是在人脑和计算机或其它电子设备之间建立不依赖于常规大脑信息输出通路(外周神经和肌肉组织)的全新对外信息交流和控制技术，概述了基于EEG的BCI技术的科学意义与应用前景，并介绍了BCI主要研究方法和类型。     

面向运动意图解码的共空间模式及其扩展算法研究综述

运动想象脑-机接口(MI-BCI)在人体运动功能康复、替代、增强等方面具有重要研究意义与应用价值。共空间模式(CSP)算法旨在增强MI诱导头皮脑电(EEG)特征的差异性,是当前使用最广泛的MI范式特征提取算法之一。但因其未考虑EEG的时、频域等信息,且对噪声和偏离值敏感,导致分类器识别性能有限、鲁棒性低。回顾CSP及其扩展算法的发展历程,从多模态信息优化、正则优化以及其他空间映射优化方法等三方面详细介绍相关扩展算法的基本原理和关键步骤,并探讨其实际面临的挑战和预测其未来发展趋势,以期促进相关BCI技术的深入研究与开发应用。     

基于脑机接口技术的上下肢康复系统研究

本研究以脑卒中后存在运动功能性障碍的患者为研究对象,开发了基于脑机接口技术的上下肢康复系统,并对其康复效果进行了分析.根据患者的康复需求,系统设计了主动、被动和脑控三种康复模式,其中脑控康复训练利用脑机接口(brain-computer interface,BCI)技术,通过采集患者大脑的脑电信号,在人体外仿生构造一条...     

运动想象脑-机接口新进展与发展趋势

基于运动想象(MI)的脑-机接口(BCI)作为一项新的运动损伤康复手段,在帮助改善和恢复丧失的身体功能方面具有重要的作用。然而,目前MI-BCI走向实用化仍面临着许多问题和挑战,包括MI诱发生理信号空间分辨率低、使用者训练时间较长和异步控制MI-BCI系统难以有效实现等。简要阐述MI相关的机制研究,然后针对以上问题从信号采集、信号处理算法分析、范式设计和异步控制研究等,综述相关解决方案及其研究现状,最后概述MI-BCI的应用,并展望MI-BCI未来的发展方向。     

一种运动想象脑电分类算法的研究

为了解决脑机接口(BCI)中不同意识任务下脑电信号分类问题,针对运动想象脑电(EEG)的事件相关去同步/同步(ERD/ERS)现象,提出一种基于支持向量机(SVM)的实用分类算法。该算法首先对脑电信号进行滤波,获得对运动想象比较敏感的频段,对滤波后的脑电信号,通过去均值减小由于均值不同所造成的误差,然后,再提取基于ERD/ERS的脑电能量场强特征,对提取的特征,运用支持向量机(SVM)进行分类,得到了满意的效果。结果表明,此方法可为脑机接口技术的应用提供有效的手段。     

想象左右手运动的脑电特征提取及分类研究

针对想象运动的脑机接口(BCI)系统存在分类准确率低、抗干扰能力差等不足,提出一种将离散小波变换(DWT)和BP神经网络相结合的脑电识别方法(DWT-BP法).通过计算想象左、右手运动的C3、C4的平均功率,合理确定时间窗设置,对时间窗内的平均功率信号进行离散小波变换,并选取尺度6上的逼近系数A6的组合信号作为脑电信号特征,以BP神经网络为分类器实现对脑电观测数据的分析.实验结果表明,DWT-BP方法能够较准确地提取脑电信号的本质特征,具有较好的抗干扰能力和分类性能,以及识别运动想象脑电信号的有效性,同时为实现运动想象在线BCI系统打下基础.     

基于LabVIEW运动想象的脑机接口系统

背景：基于事件相关电位的脑-机接口,可广泛应用于残障患者的康复,显示出其重要性和未来实现的可行性。 目的：提出基于LabVIEW环境下的运动想象脑-机接口系统的实现方案。 方法：研究的关键部分是视觉刺激器的设计和脑电特征信号的特征提取两部分。测试者通过观察视觉刺激器上的左右手连续播放图像刺激产生脑电信号,采用带通滤波提高信噪比,用滑动窗截取脑电数据并且对截取的数据从能量的角度分析得到运动想象特征,同时可以在线提取特征,为实现实时系统打下了基础。 结果与结论：该方案能有效地提取出运动想象特征,并且通过离线模式识别进行了有效的分类,分类效果达到了82%。     

Electro-encephalogram based brain–computer interface: improved performance by mental practice and concentration skills

Mental imagination is the essential part of the most EEG-based communication systems. Thus, the quality of mental rehearsal, the degree of imagined effort, and mind controllability should have a major effect on the performance of electro-encephalogram (EEG) based brain-computer interface (BCI). It is now well established that mental practice using motor imagery improves motor skills. The effects of mental practice on motor skill learning are the result of practice on central motor programming. According to this view, it seems logical that mental practice should modify the neuronal activity in the primary sensorimotor areas and consequently change the performance of EEG-based BCI. For developing a practical BCI system, recognizing the resting state with eyes opened and the imagined voluntary movement is important. For this purpose, the mind should be able to focus on a single goal for a period of time, without deviation to another context. In this work, we are going to examine the role of mental practice and concentration skills on the EEG control during imaginative hand movements. The results show that the mental practice and concentration can generally improve the classification accuracy of the EEG patterns. It is found that mental training has a significant effect on the classification accuracy over the primary motor cortex and frontal area.     

基于脑电alpha波及运动想象的在线脑机接口系统设计

背景：目前在线脑机接口系统绝大多数采用同步式设计,无法区分“工作”状态与“空闲”状态。 目的：设计一种能够自由在“工作”与“空闲”状态间切换,方便灵活的脑机接口系统。 方法：设计了综合睁眼产生的alpha波阻断现象,以及进行运动想象时产生事件相关同步及去同步现象这些生理特征的在线脑机接口系统。通过检测使用者枕部脑电信号alpha波状况,来切换“空闲”与“工作”状态;在“工作”状态下,通过想象不同的肢体运动,分析运动皮质脑电信号的频率特征,来实现对外界的信息传输。 结果与结论：实验证明,经过训练的使用者在该在线脑机接口平台上可以自如的在不同状态间进行切换,并且能以很高的分类正确率发出控制命令。采用此方法进行设计,脑机接口系统的实用性得到了增强。     

一种运动想象脑机接口训练系统的设计

目的 为提高运动想象的脑机接口训练速度和效率,本文设计了一种训练系统.系统功能主要包括参数设置、EEG采集、特征提取、分类及其结果反馈、分类器模型建立.方法 在训练系统设计中,首先使用VC＋＋编写的脑电信号采集软件获取脑电信号,而后通过TCP/IP实现与MATLAB之间的数据传输,在MATLAB中实现特征提取与分类识别,并将识别结果实时反馈给受试者,使受试者能够及时调整自身状态,并选择合适的反馈方式,从而在较短时间内生成有效的分类器模型.结果 该系统具有接口方便、功能强大、界面友好的特点,通过建立的在线系统对训练系统进行了初步检验.结论 该系统可使使用者进行方便有效的训练,进而缩短训练时间并提高脑机接口系统的识别正确率,为脑机接口应用系统的实现奠定了基础.     

基于脑-机接口技术的虚拟现实康复训练平台

对神经损伤的瘫痪病人进行功能恢复训练时应强调患者的主动参与。开发了一套基于脑-机接口技术的虚拟现实康复训练平台。该平台采用患者在想象运动时的脑电信号作为虚拟人运动的控制信号,从而把想象运动与运动功能恢复训练结合在一起。由于虚拟现实系统的实时性与沉浸感能给受试者提供较好的训练反馈信息,因此,使用本平台有望改善患者的训练效果。详细介绍了快速在线脑-机接口算法以及虚拟现实的实时交互技术,并提供了三名受试者的实测结果。初步实验证明了该平台设计的可行性。     

Brain–computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation

Brain–computer interfaces (BCIs) are a rehabilitation tool for tetraplegic patients that aim to improve quality of life by augmenting communication, control of the environment, and self-care. The neurobiology of both rehabilitation and BCI control depends upon learning to modify the efficacy of spared neural ensembles that represent movement, sensation and cognition through progressive practice with feedback and reward. To serve patients, BCI systems must become safe, reliable, cosmetically acceptable, quickly mastered with minimal ongoing technical support, and highly accurate even in the face of mental distractions and the uncontrolled environment beyond a laboratory. BCI technologies may raise ethical concerns if their availability affects the decisions of patients who become locked-in with brain stem stroke or amyotrophic lateral sclerosis to be sustained with ventilator support. If BCI technology becomes flexible and affordable, volitional control of cortical signals could be employed for the rehabilitation of motor and cognitive impairments in hemiplegic or paraplegic patients by offering on-line feedback about cortical activity associated with mental practice, motor intention, and other neural recruitment strategies during progressive task-oriented practice. Clinical trials with measures of quality of life will be necessary to demonstrate the value of near-term and future BCI applications.     

Evaluation of feature extraction methods for EEG-based brain–computer interfaces in terms of robustness to slight changes in electrode locations

To date, most EEG-based brain–computer interface (BCI) studies have focused only on enhancing BCI performance in such areas as classification accuracy and information transfer rate. In practice, however, test–retest reliability of the developed BCI systems must also be considered for use in long-term, daily life applications. One factor that can affect the reliability of BCI systems is the slight displacement of EEG electrode locations that often occurs due to the removal and reattachment of recording electrodes. The aim of this study was to evaluate and compare various feature extraction methods for motor-imagery-based BCI in terms of robustness to slight changes in electrode locations. To this end, EEG signals were recorded from three reference electrodes (Fz, C3, and C4) and from six additional electrodes located close to the reference electrodes with a 1-cm inter-electrode distance. Eight healthy participants underwent 180 trials of left- and right-hand motor imagery tasks. The performance of four different feature extraction methods [power spectral density (PSD), phase locking value (PLV), a combination of PSD and PLV, and cross-correlation (CC)] were evaluated using five-fold cross-validation and linear discriminant analysis, in terms of robustness to electrode location changes as well as regarding absolute classification accuracy. The quantitative evaluation results demonstrated that the use of either PSD- or CC-based features led to higher classification accuracy than the use of PLV-based features, while PSD-based features showed much higher sensitivity to changes in EEG electrode location than CC- or PLV-based features. Our results suggest that CC can be used as a promising feature extraction method in motor-imagery-based BCI studies, since it provides high classification accuracy along with being little affected by slight changes in the EEG electrode locations.     

共同空间模式在少通道分类问题中的应用

在目前以运动想像为基础的脑机接口(BCI)系统中,共同空间模式(CSP)方法作为一种有效的处理方法被广泛使用.但这种基于多通道的空间滤波方法并不能对频域信息进行处理,而且在通道数较少的情况下也无法应用.将每个通道的多个频段看成是新的通道运用CSP,并以此方法获得了2008年BCI竞赛中数据集IIb的第二名,平均Kappa系数达到0.58.该方法充分利用信号频域信息,以解决通道数过少的情况下基于想像运动模式分类的难题.     

Discrimination of left and right leg motor imagery for brain–computer interfaces

This article reports on a study to identify electroencephalography (EEG) signals with potential to provide new BCI channels through mental motor imagery (MMI). Leg motion was assessed to see if left and right leg MMI could be discriminated in the EEG. The study also explored simultaneous observation of leg movement as a means to enhance MMI evoked EEG signals. The results demonstrate that MMI of the left and right leg produce a contralateral preponderance of EEG alpha band desynchronization, which can be spatially discriminated. This suggests that lower extremity MMI could provide signals for additional BCI channels. The study also shows that movement imitation enhances alpha band desynchronization during MMI, and might provide a useful aid in the identification and training of BCI signals.     

A self-paced brain–computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training

Due to the non-stationarity of EEG signals, online training and adaptation are essential to EEG based brain–computer interface (BCI) systems. Self-paced BCIs offer more natural human–machine interaction than synchronous BCIs, but it is a great challenge to train and adapt a self-paced BCI online because the user’s control intention and timing are usually unknown. This paper proposes a novel motor imagery based self-paced BCI paradigm for controlling a simulated robot in a specifically designed environment which is able to provide user’s control intention and timing during online experiments, so that online training and adaptation of the motor imagery based self-paced BCI can be effectively investigated. We demonstrate the usefulness of the proposed paradigm with an extended Kalman filter based method to adapt the BCI classifier parameters, with experimental results of online self-paced BCI training with four subjects.