基于精细运动想象的脑机接口技术研究进展

基于运动想象的脑机接口(Motor Imagery based Brain-Computer Interface,MI-BCI)对运动康复具有重大意义.然而,现有MI-BCI指令集有限,在高维精细运动控制时,运动意图与动作输出无法匹配,不利于构建反馈真实运动意图的闭环运动康复系统.近年来,精细运动想象编解码研究逐渐受到关注与重视,在范式与解码方法两方面取得了一些研究进展.范式方面,一些研究对想象单一肢体不同关节、同一关节不同自由度的动作进行了尝试;解码方法方面,传统机器学习、深度学习与基于领域自适应的迁移学习方法等被引入到了精细运动想象解码研究中.本综述讨论了基于脑电信号的精细运动想象范式与解码方法的研究进展,并展望了其未来前景,以期促进精细运动想象脑机接口技术的深入研究及未来应用.     

基于小波变换的运动想象脑电信号分类

在脑-机接口的研究中,针对运动想象的两种思维任务的脑电信号的特征提取,提出了一种基于小波包变换的特征提取方法。该方法利用想象运动中,脑电信号Mu/Beta节律事件相关同步化/去同步化特性,采用BCI2003竞赛数据,输入Matlab的Classify分类函数进行分类,正确率达到88.57%。     

基于Hilbert-Huang变换的思维脑电分类技术研究

研究基于Hilbert-Huang变换的思维脑电分类方法.对思维脑电信号进行Hilbert-Huang时频预处理,经经验模式分解后,得到多阶固有模态分量.然后将经HHT变换后的时频窗口内的振幅标准差作为不同心理作业信号特征,再应用K-近邻对思维脑电信号进行分类决策.通过对Colorado州立大学EEG研究中心的三类思维脑电心理作业样本进行分类,平均正确率达到82.54%.经Hilbert-Huang变换得到的脑电信号特征,可以作为思维脑电分类的有效依据.     

基于CNN-LSTM的运动想象脑电四分类研究

脑机接口通过对运动想象脑电信号的分类识别，帮助运动神经严重受损的患者实现与外部设备的直接交互。为了提高多分类运动想象脑电信号的分类准确率，提出一种基于卷积神经网络（Convolutional Neural Network,CNN）和长短时记忆（Long Short-Term Memory,LSTM）网络的脑电信号识别方法。首先，对原始脑电信号进行预处理，并利用快速傅里叶变换提取出相关频带的能量值；其次，对处理后的脑电信号采用一维卷积神经网络提取空间特征，再利用LSTM网络在序列上的建模能力，将空间特征组成特征序列输入到LSTM网络，提取时间特征；最后，将LSTM网络输出的特征输入到Softmax分类器，把结果分为左手、右手、舌头和脚四种类型。文中使用BCI Competition Ⅳ竞赛的数据集2a对所提的方法进行验证，实验结果表明，所提方法能够有效地提高多分类精度，分类结果的平均准确率达到90.38%。     

羽毛球运动员表面肌电信号采集与提取仿真

为更精确地分析羽毛球运动员上肢运动细节,提出基于表面肌电信号的采集与提取方法。采集不同的肌电信号反映动作状态,提取上肢局部动作特征向量,利用小波变换结合自适应滤波的方法对采集的不同动作对应的表面肌电信号进行预处理,获得纯净的表面肌电信号。利用神经网络算法比较表面肌电信号时域特征、频域特征以及时频特征对羽毛球运动员上肢运动的影响。通过计算机仿真验证上肢运动模拟模型合理性,结果表明,所提方法能有效采集羽毛球运动员上肢运动的规律,识别正确率高,动作执行完整度高。     

色彩感知中的脑电信号多域特征选择算法研究

针对目前在不同色彩感知中的脑电信号识别方面的研究还不多见,本文提出采用随机森林算法对信号的时域特征和频域特征进行最优组合的方法对不同色彩感知中的脑电信号进行识别。首先采用小波变换,对脑电信号进行7层分解,提取脑电信号在delta、theta、alpha和beta节律频带上的小波能量,并结合脑电信号在时域上的统计量偏度和峰度组成特征向量。然后通过基于随机森林的特征选择算法提取最优的特征组合方案,删除冗余的特征量。使用自适应增强算法进行分类识别,识别的平均正确率可达到85.07%。该结果表明使用本文所提出的特征提取与选择方法用于不同色彩感知中的脑电信号识别上是可行的,并且能够取得较好的识别率。      

左右手运动想象脑电信号的特征分析1

针对运动想象的两种思维任务的脑电信号,本文提出了一种基于能量的特征提取方法。该方法利用想象运动中,脑电信号Mu/Beta节律事件相关同步化/去同步化特性,采用BCI2003竞赛数据,对C3/C4通道脑电信号进行时域分析,分析得到特征较为明显的脑电信号时间段。     

基于信号投影能量特征的脑电意识动态分类

针对脑电意识任务动态分类问题,本文提出了一种基于投影能量的特征提取方法来提取反映不同思维状态的脑电特征,并结合信息累积后验贝叶斯方法进行分类以提高脑-机接口系统的分类正确率。该方法通过使两类信号在投影基上的平均投影能量比达到极值,从而达到提高脑电信号分类准确度的作用。实验结果表明两个运动想象数据集上的最大正确率都达到90%左右,最大分类准确率、kappa系数和最大互信息等评价指标的比较也表明该方法能够有效提高BCI系统的性能,具有较好的实用性。     

一种基于WRCE-CSP的运动想象电位识别方法

针对脑一机接口系统中运动想象脑电信号（Electroencephalography,EEG）的模式识别问题,提出了加权节律成分提取（WeightedRhythmicComponentExtraction,WRCE）与共空间模式（CommonSpacePattern,CSP）相结合的特征提取方法,并使用Fisher线性判别分析进行分类。采用2003年的BCI竞赛数据Datasetm对该方法进行评估,测试数据的分类正确率达到86．13％,比使用传统CSP方法进行特征提取时的分类正确率提高了5．71％,表明该方法可有效地应用于运动想象EEG的模式识别中。     

脑电信号中眼电伪迹自动去除方法的研究

 针对实际采集的脑电信号受眼电干扰较大,提出一种基于离散小波变换(DWT)与独立分量分析(ICA)的自动去除眼电伪迹的方法(DWICA).对采集的多导脑电和眼电信号进行离散小波变换,获取多尺度小波系数,将串接小波系数作为ICA的输入;利用基于负熵判据的FastICA算法实现独立成分的快速获取,引入夹角余弦准则自动识别眼迹成分,并经过ICA逆变换将剔除眼迹后的独立成分投影返回到原脑电信号各个电极;通过DWT逆变换重构信号,即可得到去除眼迹的各导脑电信号.实验结果表明,DWICA方法极大地提高了脑电信号的信噪比,抗噪能力强且实时性好,为脑电信号的在线预处理提供了新思路.     

Target selection with hybrid feature for BCI-based 2-D cursor control

To control a cursor on a monitor screen, a user generally needs to perform two tasks sequentially. The first task is to move the cursor to a target on the monitor screen (termed a 2-D cursor movement), and the second task is either to select a target of interest by clicking on it or to reject a target that is not of interest by not clicking on it. In a previous study, we implemented the former function in an EEG-based brain-computer interface system using motor imagery and the P300 potential to control the horizontal and vertical cursor movements, respectively. In this study, the target selection or rejection functionality is implemented using a hybrid feature from motor imagery and the P300 potential. Specifically, to select the target of interest, the user must focus his or her attention on a flashing button to evoke the P300 potential, while simultaneously maintaining an idle state of motor imagery. Otherwise, the user performs left-/right-hand motor imagery without paying attention to any buttons to reject the target. Our data analysis and online experimental results validate the effectiveness of our approach. The proposed hybrid feature is shown to be more effective than the use of either the motor imagery feature or the P300 feature alone. Eleven subjects attended our online experiment, in which a trial involved sequential 2-D cursor movement and target selection. The average duration of each trial and average accuracy of target selection were 18.19 s and 93.99% , respectively, and each target selection or rejection event was performed within 2 s.     

基于脑功能网络和样本熵的脑电信号特征提取

针对脑-机接口(BCI)研究中采用单一特征对运动想象脑电信号(EEG)识别率不高的问题,该文提出一种结合脑功能网络和样本熵的特征提取方法。根据事件相关同步/去同步(ERS/ERD)现象以及皮层与肢体运动想象间的对侧映射机制,选取小波包变换消噪重构后的\begin{document}$ \mu$\end{document}节律脑电信号,用左侧27个通道、右侧27个通道分别对左半球脑区和右半球脑区构建脑功能网络,计算网络的平均节点度和平均聚集系数作为运动想象的脑功能网络特征,并结合C3, C4通道节律的样本熵构筑分布性和指向性相结合的特征向量。选用支持向量机(SVM)对左右手运动想象脑电信号进行分类,结果表明基于脑功能网络和样本熵的特征提取方法能够实现更优的分类效果,分类准确率最高可达90.27%。     

Asymmetric hemisphere modeling in an offline brain-computerinterface

Classification of the electroencephalogram (EEG) during motor imagery of the left or right hand can be performed using a classifier comprising two hidden Markov models (HMMs) describing the spatio-temporal patterns related to the imagination. Due to the known asymmetries during motor imagery of rightand left-hand movement, an HMM-based classifier allowing asymmetrical structures is introduced. The comparison between such a system and a symmetrical one is based on the error rate of classification. The results for EEG data collected during 20 sessions from five subjects demonstrate a significant improvement of 9% for the classification accuracy for the asymmetric classifiers. The selection of the DAM for classification is done using a variant of genetic algorithms (GAs); namely, the adaptive reservoir genetic algorithm (ARGA)     

Modulation of sensorimotor rhythms for brain-computer interface using motor imagery with online feedback

This research studies the impact of the imagination of movements and associated feedbacks on the modulation of sensorimotor electroencephalographic (EEG) rhythms, for the online controls of a brain-computer interface (BCI). Nine subjects with no physical or mental impairments were selected. The number of sessions was five: one calibration and four feedback sessions. A computer screen’s cursor movement was controlled in one dimension using EEG-based four-class BCI involving motor imagery tasks of moving the right hand, the left hand, both hands, or both feet. Our findings reveal that the visual feedback applied during motor imagery movement modulates sensorimotor EEG rhythms clearly in the mu and beta bands. The analyses of event-related desynchronization/synchronization (ERD/ERS) suggest significant differences between brain activities in the calibration and feedback sessions; large ERDs during the online feedback sessions compared to that in the calibration session have been observed. The increasing ERDs in the online feedback session were noticed over the mu1 (8–10 Hz) and upper beta (18–24 Hz) rhythms, resulting in the cursor control success rate at 73.3%.     

基于可调Q因子小波变换的识别左右手运动想象脑电模式研究

针对识别左右手运动想象脑电图信号(EEG)模式精度和互信息不高的问题,该文采用基于可调Q因子小波变换(TQWT)算法来处理脑电信号。首先,利用TQWT对脑电图信号进行分解;随后,提取子频带信号的小波系数能量、自回归模型(AR)系数以及分形维数;最后,利用线性判别分析(LDA)对提取的脑电特征进行识别。采用BCI2003和BCI2005竞赛数据对所提出的算法进行验证,4名受试者的最高识别率分别为88.11%, 89.33%, 77.13%和78.80%,最大互信息分别为0.95, 0.96, 0.43和0.45。实验结果表明,所提算法取得了高分类精度及互信息值,验证了其有效性。     

Motor imagery and direct brain-computer communication

Motor imagery can modify the neuronal activity in the primary sensorimotor areas in a very similar way as observable with a real executed movement. One part of EEG-based brain-computer interfaces (BCI) is based on the recording and classification of circumscribed and transient EEG changes during different types of motor imagery such as, e.g., imagination of left-hand, right-hand, or foot movement. Features such as, e.g., band power or adaptive autoregressive parameters are either extracted in bipolar EEG recordings overlaying sensorimotor areas or from an array of electrodes located over central and neighboring areas. For the classification of the features, linear discrimination analysis and neural networks are used. Characteristic for the Graz BCI is that a classifier is set up in a learning session and updated after one or more sessions with online feedback using the procedure of “rapid prototyping.” As a result, a discrimination of two brain states (e.g., leftversus right-hand movement imagination) can be reached within only a few days of training. At this time, a tetraplegic patient is able to operate an EEG-based control of a hand orthosis with nearly 100% classification accuracy by mental imagination of specific motor commands     

Design of electrode layout for motor imagery based brain?computer interface

A simple electroencephalogram (EEG) electrode layout is proposed to implement a motor imagery based brain-computer interface (BCI). The design was derived from investigation of EEG synchronisation in the motor cortex. A significant improvement in BCI performance was obtained in the new system     

Motor Imagery EEG Fuzzy Fusion of Multiple Classification

Due to the volume conduction, electroencephalogram (EEG) gives a rather blurred image of brain activities. It is a challenge for generating satisfactory performance with EEG. This paper studies the multiple areas fusion of EEG classifiers to improve the motor imagery EEG classification performance. Two feature extraction methods are employed to extract the feature from three different areas of EEG. One is power spectral density (PSD), and the other is common spatial patterns (CSP). Classifiers are designed based on the well-known linear discrimination analysis (LDA). The fusion of the individual classifiers is realized by means of the Choquet fuzzy integral. It is demonstrated that the proposed method comes with better performance compared with the individual classifier.     

Optimizing the channel selection and classification accuracy in EEG-based BCI

Multichannel EEG is generally used in brain-computer interfaces (BCIs), whereby performing EEG channel selection 1) improves BCI performance by removing irrelevant or noisy channels and 2) enhances user convenience from the use of lesser channels. This paper proposes a novel sparse common spatial pattern (SCSP) algorithm for EEG channel selection. The proposed SCSP algorithm is formulated as an optimization problem to select the least number of channels within a constraint of classification accuracy. As such, the proposed approach can be customized to yield the best classification accuracy by removing the noisy and irrelevant channels, or retain the least number of channels without compromising the classification accuracy obtained by using all the channels. The proposed SCSP algorithm is evaluated using two motor imagery datasets, one with a moderate number of channels and another with a large number of channels. In both datasets, the proposed SCSP channel selection significantly reduced the number of channels, and outperformed existing channel selection methods based on Fisher criterion, mutual information, support vector machine, common spatial pattern, and regularized common spatial pattern in classification accuracy. The proposed SCSP algorithm also yielded an average improvement of 10% in classification accuracy compared to the use of three channels (C3, C4, and Cz).