基于C-LSTM模型的端到端多粒度运动想象脑电信号分析方法

脑电信号（Electroencephalography, EEG）是人的大脑在不同状态下产生的生物电信号。运动想象脑电信号是其中较为典型的一类信号，广泛应用于脑机接口技术中。对运动想象脑电信号分析的研究由来已久，目前主要采用公共空间模式等特征提取方法，对于如何提取更加有效的脑电信号特征以及如何对时序信息进行建模仍然是需要解决的问题。因此，本文设计了基于C-LSTM（Convolutional-Long Short Term Memory）模型的端到端多粒度脑电分析方法。并利用空间信息以及小波脑网络方法进行了改进，在BCI2008数据集上，相较传统方法提高了近10%，到达了93.6%的识别率。      

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戏型脑-机接口技术进行了总结,将其按控制信号类型分为主动式、反应式、被动式以及混合范式,介绍不同类型游戏BCI的控制策略和应用场景,并对比分析了游戏BCI中不同类型脑电信号常采用的分类算法,最后讨论了该领域目前存在的问题和未来的发展方向。     

Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms

Noninvasive electroencephalogram (EEG) recordings provide for easy and safe access to human neocortical processes which can be exploited for a brain-computer interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. We systematically analyze and develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: 1) the combination of classifiers, each specifically tailored for different physiological phenomena, e.g., slow cortical potential shifts, such as the pre-movement Bereitschaftspotential or differences in spatio-spectral distributions of brain activity (i.e., focal event-related desynchronizations) and 2) behavioral paradigms inducing the subjects to generate one out of several brain states (multiclass approach) which all bare a distinctive spatio-temporal signature well discriminable in the standard scalp EEG. We derive information-theoretic predictions and demonstrate their relevance in experimental data. We will show that a suitably arranged interaction between these concepts can significantly boost BCI performances.     

基于深度时空特征融合的多通道运动想象EEG解码方法

脑电(EEG)是一种在临床上广泛应用的脑信息记录形式,其反映了脑活动中神经细胞放电产生的电场变化情况。脑电广泛应用于脑-机接口(BCI)系统。然而,研究表明脑电信息空间分辨率较低,这种缺陷可以综合分析多通道电极的脑电数据来弥补。为了从多通道数据中高效地获取到与运动想象任务相关的辨识特征,该文提出一种针对多通道脑电信息的卷积神经网络(MC-CNN)解码方法,先对预先选取好的多通道数据预处理后送入2维卷积神经网络(CNN)进行时间-空间特征提取,然后利用自动编码(AE)器把这些特征映射为具有辨识度的特征子空间,最后指导识别网络进行分类识别。实验结果表明,该文所提多通道空间特征提取和构建方法在运动想象脑电任务识别性能和效率上都具有较大优势。

     

xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface

A brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin. An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected into the estimated signal subspace. Data recorded on three subjects were used to evaluate the proposed method. The results, which are presented using a Bayesian linear discriminant analysis classifier, show that the proposed method is efficient and accurate.     

脑机接口中脑电图-近红外光谱联合分析进展研究

脑机接口(BCI)能将受试者意图相关的大脑活动转化为外部设备控制指令,在神经疾病治疗、运动康复等方面具有较高应用潜力。BCI的实现需从人脑获取有意义的信号,而脑电图(EEG)可以反映神经电活动,主要用于对反映实时性要求较高的BCI系统;近红外光谱(NIRS)主要反映血流动力学水平,一般用于神经生理状态等需要精确定位脑活跃区域的研究。EEG和NIRS因其非侵入、方便穿戴、成本较低等优点,成为BCI的重要信号获取方法。相比于单模态BCI系统,基于EEG-NIRS 联合分析的混合BCI系统由于具有更丰富的信号特征,在生理状态检测、运动想象等领域得到了越来越多的关注与研究。该文从EEG-NIRS联合分析在脑机接口中应用的研究现状出发,在数据和特征融合程度、层面上归纳最近的相关领域研究现状,并对EEG-NIRS信号处理手段的研究前景进行了展望。     

Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver's drowsiness detection and warning

Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.     

Support vector channel selection in BCI

Designing a brain computer interface (BCI) system one can choose from a variety of features that may be useful for classifying brain activity during a mental task. For the special case of classifying electroencephalogram (EEG) signals we propose the usage of the state of the art feature selection algorithms Recursive Feature Elimination and Zero-Norm Optimization which are based on the training of support vector machines (SVM). These algorithms can provide more accurate solutions than standard filter methods for feature selection. We adapt the methods for the purpose of selecting EEG channels. For a motor imagery paradigm we show that the number of used channels can be reduced significantly without increasing the classification error. The resulting best channels agree well with the expected underlying cortical activity patterns during the mental tasks. Furthermore we show how time dependent task specific information can be visualized.     

Asynchronous P300-based brain-computer interfaces: a computational approach with statistical models

Asynchronous control is an important issue for brain-computer interfaces (BCIs) working in real-life settings, where the machine should determine from brain signals not only the desired command but also when the user wants to input it. In this paper, we propose a novel computational approach for robust asynchronous control using electroencephalogram (EEG) and a P300-based oddball paradigm. In this approach, we first address the mathematical modeling of target P300, nontarget P300, and noncontrol signals, by using Gaussian distribution models in a support vector margin space. Furthermore, we derive a method to compute the likelihood of control state in a time window of EEG. Finally, we devise a recursive algorithm to detect control states in ongoing EEG for online application. We conducted experiments with four subjects to study both the asynchronous BCI's receiver operating characteristics and its performance in actual online tests. The results show that the BCI is able to achieve an averaged information transfer rate of approximately 20 b/min at a low false positive rate (one event per minute).     

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.     

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.     

Design of an EEG Preamplifier for Brain-Computer Interface

As a non-invasive neurophysiological index for brain-computer interface (BCI), electroencephalogram (EEG) attracts much attention at present. In order to have a portable BCI, a simple and efficient pre-amplifier is crucial in practice. In this work, a preamplifier based on the characteristics of EEG signals is designed, which consists of a highly symmetrical input stage, low-pass filter, 50 Hz notch filter and a post amplifier. A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment. The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.     

Design of an EEG Preamplifier for Brain-Computer Interface

As a non-invasive neurophysiologieal index for brain-computer interface （BCI）, electroencephalogram （EEG） attracts much attention at present. In order to have a portable BCI, a simple and efficient pre-amplifier is crucial in practice. In this work, a preamplifier based on the characteristics of EEG signals is designed, which consists of a highly symmetrical input stage, low-pass filter, 50 Hz notch filter and a post amplifier. A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment. The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.     

Automatic ocular artifact removal from EEG data using a hybrid CAE-RLS approach

Traditional methods for removing ocular artifacts (OAs) from electroencephalography (EEG) signals often involve a large number of EEG electrodes or require electrooculogram (EOG) as the reference, these constraints make subjects uncomfortable during the acquisition process and increase the complexity of brain-computer interfaces (BCI). To address these limitations, a method combining a convolutional autoencoder (CAE) and a recursive least squares (RLS) adaptive filter is proposed. The proposed method consists of offline and online stages. In the offline stage, the peak and local mean of the four-channel EOG signals are automatically extracted to obtain the CAE model. Once the model is trained, the EOG channels are no longer needed. In the online stage, by using the CAE model to identify the OAs from a single-channel raw EEG signal, the identified OAs and the given raw EEG signal are used as the reference and input for an RLS adaptive filter. Experiments show that the root mean square error (RMSE) of the CAE-RLS algorithm and independent component analysis (ICA) are 1.253 3 and 1.254 6 respectively, and the power spectral density (PSD) curve for the CAE-RLS is similar to the original EEG signal. These experimental results indicate that by using only a couple of EEG channels, the proposed method can effectively remove OAs without parallel EOG records and accurately reconstruct the EEG signal. In addition, the processing time of the CAE-RLS is shorter than that of ICA, so the CAE-RLS algorithm is very suitable for BCI system.     

基于BP神经网络的想象动作电位模式识别研究

脑-机接口是一种全新的信息交换与控制技术,其关键技术是要准确及时地识别出思维脑电的预期动作模式及其特征参数并将之转化为控制命令.本文通过分析和提取左、右手两种想象动作诱发的事件相关去同步(ERD)/同步(ERS)信号特征,并采用BP神经网络对特征量进行模式分类识别,以区分想象动作的意图,进而形成可用来控制轮椅、鼠标等外部机电设备的控制指令.研究结果表明该方法识别率高,速度快,可用于实时脑-机接口系统中.     

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.     

CSSD＋AAR模型在脑电信号处理中的应用

针对BCI技术中的脑电信号处理方法和事件相关去同步化的特点,提出了一种结合时、频、空域的特征提取方法。结合CSSD和AAR模型来提取脑电特征,并对基于AAR模型系数的特征提取方法进行了探讨,最终选择卡尔曼平滑算法提取模型系数,然后将提取的特征用简单的线性分类器进行分类。实验结果表明测试集的分类正确率达到了94．08％,而且这种特征提取方法有很好的时间分辨率,适合于在线分类。这是一种正确率高,时间分辨率高,适合在线分类的好方法。     

Listen to the Brain in Real Time-The Chengdu Brainwave Music

Electroencephalogram (EEG) provides a window for the activity of the human brain. As a novel form of the brain-computer interface (BCI), the online/offline EEG data may be interpreted through its auditory representation which can be considered as a specific tool in EEG monitoring and analysis. In this work, after a comprehensive comparison of the various designs of brainwave music generations, a waveform event mapping system for music display in real time-the Chengdu Brainwave Music (CBM) is proposed, which is a special on-line BCI system. In CBM, the user datagram protocol (UDP) is adopted to transport EEG data from the recorder to a music generator. The CBM could possibly be used as an audio feedback tool in BCI, or a monitoring tool in clinic EEG, and a subject specified music therapy method.     

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。