Single-Trial EEG Source Reconstruction for Brain–Computer Interface

A new way to improve the classification rate of an EEG-based brain-computer interface (BCI) could be to reconstruct the brain sources of EEG and to apply BCI methods to these derived sources instead of raw measured electrode potentials. EEG source reconstruction methods are based on electrophysiological information that could improve the discrimination between BCI tasks. In this paper, we present an EEG source reconstruction method for BCI. The results are compared with results from raw electrode potentials to enable direct evaluation of the method. Features are based on frequency power change and Bereitschaft potential. The features are ranked with mutual information before being fed to a proximal support vector machine. The dataset IV of the BCI competition II and data from four subjects serve as test data. Results show that the EEG inverse solution improves the classification rate and can lead to results comparable to the best currently known methods.     

Combining spatial filters for the classification of single-trial EEG in a finger movement task

Brain-computer interface (BCI) is to provide a communication channel that translates human intention reflected by a brain signal such as electroencephalogram (EEG) into a control signal for an output device. In recent years, the event-related desynchronization (ERD) and movement-related potentials (MRPs) are utilized as important features in motor related BCI system, and the common spatial patterns (CSP) algorithm has shown to be very useful for ERD-based classification. However, as MRPs are slow nonoscillatory EEG potential shifts, CSP is not an appropriate approach for MRPs-based classification. Here, another spatial filtering algorithm, discriminative spatial patterns (DSP), is newly introduced for better extraction of the difference in the amplitudes of MRPs, and it is integrated with CSP to extract the features from the EEG signals recorded during voluntary left versus right finger movement tasks. A support vector machines (SVM) based framework is designed as the classifier for the features. The results show that, for MRPs and ERD features, the combined spatial filters can realize the single-trial EEG classification better than anyone of DSP and CSP alone does. Thus, we propose an EEG-based BCI system with the two feature sets, one based on CSP (ERD) and the other based on DSP (MRPs), classified by SVM.     

Single-trial EEG source reconstruction for brain-computer interface.

A new way to improve the classification rate of an EEG-based brain-computer interface (BCI) could be to reconstruct the brain sources of EEG and to apply BCI methods to these derived sources instead of raw measured electrode potentials. EEG source reconstruction methods are based on electrophysiological information that could improve the discrimination between BCI tasks. In this paper, we present an EEG source reconstruction method for BCI. The results are compared with results from raw electrode potentials to enable direct evaluation of the method. Features are based on frequency power change and Bereitschaft potential. The features are ranked with mutual information before being fed to a proximal support vector machine. The dataset IV of the BCI competition II and data from four subjects serve as test data. Results show that the EEG inverse solution improves the classification rate and can lead to results comparable to the best currently known methods.     

Preprocessing and meta-classification for brain-computer interfaces

A brain-computer interface (BCI) is a system which allows direct translation of brain states into actions, bypassing the usual muscular pathways. A BCI system works by extracting user brain signals, applying machine learning algorithms to classify the user's brain state, and performing a computer-controlled action. Our goal is to improve brain state classification. Perhaps the most obvious way to improve classification performance is the selection of an advanced learning algorithm. However, it is now well known in the BCI community that careful selection of preprocessing steps is crucial to the success of any classification scheme. Furthermore, recent work indicates that combining the output of multiple classifiers (meta-classification) leads to improved classification rates relative to single classifiers (Dornhege et al., 2004). In this paper, we develop an automated approach which systematically analyzes the relative contributions of different preprocessing and meta-classification approaches. We apply this procedure to three data sets drawn from BCI Competition 2003 (Blankertz et al., 2004) and BCI Competition III (Blankertz et al., 2006), each of which exhibit very different characteristics. Our final classification results compare favorably with those from past BCI competitions. Additionally, we analyze the relative contributions of individual preprocessing and meta-classification choices and discuss which types of BCI data benefit most from specific algorithms.     

基于头皮脑电的游戏型脑机接口应用研究综述

游戏型脑机接口(game-BCI)是指将脑机接口与游戏结合,通过对脑电信号的识别,使用户完成对游戏的直接控制,不仅为健康人提供了新型的游戏交互方式,也为残障人士提供了新的康复治疗途径.相比于其它侵入性BCI,基于头皮脑电的BCI有无创、时间分辨率高、成本低、便携性好等优势,具有更加广泛的应用前景.该文对基于头皮脑电的游...     

基于连续小波变换的多数据心率提取方法

针对多普勒雷达生命体征检测时强谐波干扰问题,提出一种基于连续小波变换特征提取的改进方法。首先,针对特征信号选择多组不同时间长度的数据集;然后,在小波域分别进行连续小波变换。根据频谱峰值位置是否与数据时间长度相关,识别呼吸信号、呼吸谐波和心跳信号提取心率信息,并对频率分辨率进行分析。实验结果表明:采用文中提出的方法利用4 s~5 s的数据即可检测出心率,当频率分辨率要求为0.1 Hz 时,该方法在有效消除谐波和快速识别心率方面得到了较为满意的效果。     

Multiclass common spatial patterns and information theoretic feature extraction

We address two shortcomings of the common spatial patterns (CSP) algorithm for spatial filtering in the context of brain--computer interfaces (BCIs) based on electroencephalography/magnetoencephalography (EEG/MEG): First, the question of optimality of CSP in terms of the minimal achievable classification error remains unsolved. Second, CSP has been initially proposed for two-class paradigms. Extensions to multiclass paradigms have been suggested, but are based on heuristics. We address these shortcomings in the framework of information theoretic feature extraction (ITFE). We show that for two-class paradigms, CSP maximizes an approximation of mutual information of extracted EEG/MEG components and class labels. This establishes a link between CSP and the minimal classification error. For multiclass paradigms, we point out that CSP by joint approximate diagonalization (JAD) is equivalent to independent component analysis (ICA), and provide a method to choose those independent components (ICs) that approximately maximize mutual information of ICs and class labels. This eliminates the need for heuristics in multiclass CSP, and allows incorporating prior class probabilities. The proposed method is applied to the dataset IIIa of the third BCI competition, and is shown to increase the mean classification accuracy by 23.4% in comparison to multiclass CSP.     

Research on recognition of O-MI based on CNN combined with SST and LSTM

Recognition algorithms have been widely used in brain computer interface (BCI) for neural paradigms classification. To improve the classification and recognition effect of motor imagery with motor observation (O-MI) in BCI rehabilitation technology, this paper explores the function of convolutional neural network (CNN) combined with synchrosqueezed wavelet transform (SST) and long short-term memory (LSTM) in the recognition and classification of neural activities in the brain motor area. Combining the advantages of SST in signal feature extraction in the pretreatment stage and the ability of LSTM network in time series information modeling, the purpose is to make up for CNN''s shortcomings in both aspects. This paper verifies the algorithm on the self-collected O-MI experimental datasets and the public datasets (BCI competition IV datasets 2a). The results show that the composite CNN algorithm incorporating SST and LSTM achieves higher classification accuracy than classic algorithms and the similar new method which is CNN combined with discrete wavelet transform (DWT) and power spectral density (PSD), so it is convenient for practical application in O-MI BCI system.     

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。     

MSI和CCA算法对稳态视觉诱发电位信号分类的比较研究

脑机接口（Brain Computer Interface, BCI）系统能让那些有运动障碍的病人用脑信号与外界设备交互。稳态视觉诱发电位（Steady State Visual Evoked Potential,SSVEP）具有分析正确率高,不用训练等优点而倍受重视。如何高效地对SSVEP信号频率识别是SSVEP-BCI的关键问题,并关系到BCI的系统优劣。本文采用多变量同步指数与典型相关分析方法对SSVEP信号分类进行比较研究,探讨了两种方法在数据长度、导联数量、导联位置以及参考信号的谐波数量对SSVEP信号分类效果的影响。六位被试者参与实验采集数据,实验结果证实,在时间窗较小,数据长度较少的条件下,多变量同步指数方法较典型相关分析方法性能更优。而对于SSVEP信号分析来说,导联位置的准确性是影响频率分析算法的最根本因素。      

听觉诱发的“是”与“非”脑电信号研究

脑机接口(BCI)作为一种新兴技术,把脑电活动转化成电脑、假肢、机器人等设备控制信号的新交流通道。通过脑机接口研究可以提高患有严重的神经肌肉损伤病人的生活质量。文中介绍了听觉刺激诱发的“是”或“非”脑电信号的信号处理。信号处理采用共空间模式(CSP)的特征提取方法以及支持向量机(SVM)的分类方法。健康受试者实验数据离线分析的分类准确率达到80.2%。实验结果表明了这种方法对神经肌肉系统损伤患者和外部交流的可行性和有效性,为进一步的研究奠定了基础。     

Steady-State Movement Related Potentials for Brain–Computer Interfacing

An approach for brain-computer interfacing (BCI) by analysis of steady-state movement related potentials (ssMRPs) produced during rhythmic finger movements is proposed in this paper. The neurological background of ssMRPs is briefly reviewed. Averaged ssMRPs represent the development of a lateralized rhythmic potential, and the energy of the EEG signals at the finger tapping frequency can be used for single-trial ssMRP classification. The proposed ssMRP-based BCI approach is tested using the classic Fisher's linear discriminant classifier. Moreover, the influence of the current source density transform on the performance of BCI system is investigated. The averaged correct classification rates (CCRs) as well as averaged information transfer rates (ITRs) for different sliding time windows are reported. Reliable single-trial classification rates of 88%-100% accuracy are achievable at relatively high ITRs. Furthermore, we have been able to achieve CCRs of up to 93% in classification of the ssMRPs recorded during imagined rhythmic finger movements. The merit of this approach is in the application of rhythmic cues for BCI, the relatively simple recording setup, and straightforward computations that make the real-time implementations plausible.     

A comparison of common spatial patterns with complex band power features in a four-class BCI experiment

We report on the offline analysis of four-class brain-computer interface (BCI) data recordings. Although the analysis is done within defined time windows (cue-based BCI), our goal is to work toward an approach which classifies on-going electroencephalogram (EEG) signals without the use of such windows (un-cued BCI). To that end, we provide some elements of that analysis related to timing issues that will become important as we pursue this goal in the future. A new set of features called complex band power (CBP) features which make explicit use of phase are introduced and are shown to produce good results. As reference methods we used traditional band power features and the method of common spatial patterns. We consider also for the first time in the context of a four-class problem the issue of variability of the features over time and how much data is required to give good classification results. We do this in a practical way where training data precedes testing data in time.     

Regularized common spatial pattern with aggregation for EEG classification in small-sample setting

Common spatial pattern (CSP) is a popular algorithm for classifying electroencephalogram (EEG) signals in the context of brain-computer interfaces (BCIs). This paper presents a regularization and aggregation technique for CSP in a small-sample setting (SSS). Conventional CSP is based on a sample-based covariance-matrix estimation. Hence, its performance in EEG classification deteriorates if the number of training samples is small. To address this concern, a regularized CSP (R-CSP) algorithm is proposed, where the covariance-matrix estimation is regularized by two parameters to lower the estimation variance while reducing the estimation bias. To tackle the problem of regularization parameter determination, R-CSP with aggregation (R-CSP-A) is further proposed, where a number of R-CSPs are aggregated to give an ensemble-based solution. The proposed algorithm is evaluated on data set IVa of BCI Competition III against four other competing algorithms. Experiments show that R-CSP-A significantly outperforms the other methods in average classification performance in three sets of experiments across various testing scenarios, with particular superiority in SSS.     

基于相关性和稀疏表示的运动想象脑电通道选择方法

在基于运动想象(MI)的脑机接口(BCI)中,通常采用较多通道的脑电信号(EEG)来提高分类精度,但其中会有包含与MI任务无关或冗余信息的通道,从而影响BCI的性能提升。该文针对运动想象脑电分类中的通道选择问题,提出一种采用相关性和稀疏表示对通道进行选择的方法(CSR-CS)。首先计算训练样本每个通道的皮尔逊相关系数来选择显著通道,然后提取显著通道所在区域的滤波器组共空间模式特征拼接成字典,利用由字典所得到的非零稀疏系数的个数表征每个区域的分类能力,选出显著区域所包含的显著通道作为最优通道,最后采用共空间模式和支持向量机分别进行特征提取与分类。在对BCI第3次竞赛数据集IVa和BCI第4次竞赛数据集I两个二分类MI任务的分类实验中,平均分类精度达到了88.61%和83.9%,表明所提通道选择方法的有效性和鲁棒性。     

Combined optimization of spatial and temporal filters for improving brain-computer interfacing

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms.     

Transfer Learning of BCI Using CUR Algorithm

The brain computer interface (BCI) are used in many applications including medical, environment, education, economy, and social fields. In order to have a high performing BCI classification, the training set must contain variations of high quality subjects which are discriminative. Variations will also drive transferability of training data for generalization purposes. However, if the test subject is unique from the training set variations, BCI performance may suffer. Previously, this problem was solved by introducing transfer learning in the context of spatial filtering on small training set by creating high quality variations within training subjects. In this study however, it was discovered that transfer learning can also be used to compress the training data into an optimal compact size while improving training data performance. The transfer learning framework proposed was on motor imagery BCI-EEG using CUR matrix decomposition algorithm which decomposes data into two components; C and UR which is each subject’s EEG signal and common matrix derived from historical EEG data, respectively. The method is considered transfer learning process because it utilizes historical data as common matrix for the classification purposes. This framework is implemented in the BCI system along with Common Spatial Pattern (CSP) as features extractor and Extreme Learning Machine (ELM) as classifier and this combination exhibits an increase of accuracy to up to 26% with 83% training database compression.

     

融合高分夜光和Landsat OLI影像的不透水面自动提取方法

针对监督分类提取不透水面需要人工获取大量训练样本的制约,提出了一种亚米级高空间分辨率夜光遥感影像引导下的不透水面自动提取方法。以夜光强度信息作为先验知识,判别对应地理位置的Landsat8 OLI影像像元为不透水面正负训练样本后,提取OLI影像的光谱和纹理特征构建特征集,利用集成ELM分类器提取不透水面。选择全球4个具有代表性的城市作为试验区进行验证,结果显示,该方法在4个试验区的不透水面提取精度均超过93%,Kappa系数均在0.87以上。对比BCI指数与人工选取训练样本的不透水面提取结果,发现该方法在4个试验区的总体精度均优于指数法,主要原因是该方法相较于BCI指数法可以更有效地区分裸土和不透水面。提出的自动提取方法在3个试验区的总体精度高于或接近人工样本分类方法,但在哈尔滨试验区的总体精度略低,主要是因为在自动选择样本过程中灯光强度弱的不透水面未被选为正样本导致部分漏提。研究表明,高分辨率夜光数据可以作用遥感影像解译与地物提取的先验知识,引导自动分类提取模型的构建,具有较高的实用性。     

BCI Competition 2003--Data set Ia: combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals

In one type of brain-computer interface (BCI), users self-modulate brain activity as detected by electroencephalography (EEG). To infer user intent, EEG signals are classified by algorithms which typically use only one of the several types of information available in these signals. One such BCI uses slow cortical potential (SCP) measures to classify single trials. We complemented these measures with estimates of high-frequency (gamma-band) activity, which has been associated with attentional and intentional states. Using a simple linear classifier, we obtained significantly greater classification accuracy using both types of information from the same recording epochs compared to using SCPs alone.