An Algorithm for Idle-State Detection and Continuous Classifier Design in Motor-Imagery-Based BCI

The development of asynchronous braincomputer interface (BCI) based on motor imagery (MI) poses the research in algorithms for detecting the nontask states (i.e., idle state) and the design of continuous classifiers that classify continuously incoming electroencephalogram (EEG) samples. An algorithm is proposed in this paper which integrates two two-class classifiers to detect idle state and utilizes a sliding window to achieve continuous outputs. The common spatial pattern (CSP) algorithm is used to extract features of EEG signals and the linear support vector machine (SVM) is utilized to serve as classifier. The algorithm is applied on dataset IVb of BCI competition III, with a resulting mean square error of 0.66. The result indicates that the proposed algorithm is feasible in the first step of the development of asynchronous systems.     

A brain-controlled switch for asynchronous control applications

Asynchronous control applications are an important class of application that has not received much attention from the brain-computer interface (BCI) community. This work provides a design for an asynchronous BCI switch and performs the first extensive evaluation of an asynchronous device in attentive, spontaneous electroencephalographic (EEG). The switch design [named the low-frequency asynchronous switch design (LF-ASD)] is based on a new feature set related to imaginary movements in the 1-4 Hz frequency range. This new feature set was identified from a unique analysis of EEG using a bi-scale wavelet. Offline evaluations of a prototype switch demonstrated hit (true positive) rates in the range of 38%-81% with corresponding false positive rates in the range of 0.3%-11.6%. The performance of the LF-ASD was contrasted with two other ASDs: one based on mu-power features and another based on the outlier processing method (OPM) algorithm. The minimum mean error rates for the LF-ASD were shown to be significantly lower than either of these other two switch designs.     

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).     

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。     

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.     

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.     

Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation

This paper proposes the use of variational Kalman filtering as an inference technique for adaptive classification in a brain computer interface (BCI). The proposed algorithm translates electroencephalogram segments adaptively into probabilities of cognitive states. It, thus, allows for nonstationarities in the joint process over cognitive state and generated EEG which may occur during a consecutive number of trials. Nonstationarities may have technical reasons (e.g., changes in impedance between scalp and electrodes) or be caused by learning effects in subjects. We compare the performance of the proposed method against an equivalent static classifier by estimating the generalization accuracy and the bit rate of the BCI. Using data from two studies with healthy subjects, we conclude that adaptive classification significantly improves BCI performance. Averaging over all subjects that participated in the respective study, we obtain, depending on the cognitive task pairing, an increase both in generalization accuracy and bit rate of up to 8%. We may, thus, conclude that adaptive inference can play a significant contribution in the quest of increasing bit rates and robustness of current BCI technology. This is especially true since the proposed algorithm can be applied in real time.     

自适应脑机接口研究综述

脑机接口(BCI)不依赖于外周神经和肌肉,在大脑与外部设备之间建立起直接交流的通路。近年来,该技术在识别准确率和系统交互速率方面已取得巨大突破。然而,脑电(EEG)信号非平稳特性较强且用户主观状态波动较大,传统脑机接口技术对大脑活动的动态变化欠缺适应性,影响了脑机接口系统的控制稳定性,也限制了其智能化发展和应用。自适应脑机接口可根据大脑当前状态动态调整诱发范式和实时更新识别模型,从而增强脑控系统对非平稳大脑活动的适应性,提高其控制精度和鲁棒性,实现更加实用化的脑控系统,对推动脑机接口技术进一步发展极具意义。该文对自适应脑机接口的相关研究进行了回顾和总结,并对该技术未来发展的方向进行了展望。     

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。     

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.     

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.     

BCI Competition 2003--Data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram

The t-CWT, a novel method for feature extraction from biological signals, is introduced. It is based on the continuous wavelet transform (CWT) and Student's t-statistic. Applied to event-related brain potential (ERP) data in brain-computer interface (BCI) paradigms, the method provides fully automated detection and quantification of the ERP components that best discriminate between two samples of EEG signals and are, therefore, particularly suitable for classification of single-trial ERPs. A simple and fast CWT computation algorithm is proposed for the transformation of large data sets and single trials. The method was validated in the BCI Competition 2003, where it was a winner (provided best classification) on two data sets acquired in two different BCI paradigms, P300 speller and slow cortical potential (SCP) self-regulation. These results are presented here.     

Low Mutual Coupling of Microstrip Antenna Array Integrated with Dollar Shaped Resonator

This paper describes the effectiveness of feature obtained by power spectrum analysis (PSA) as well as the combined method of empirical mode decomposition (EMD) and PSA for the development of brain–computer interface (BCI) system using steady-state visual evoked potential (SSVEP). Accurate detection of SSVEP response from the recorded EEG signal is a difficult task for a new development of the BCI inference system. The EMD technique is a non-linear method of signal decomposition, which generates several intrinsic mode functions (IMFs) of different flickering frequencies. Prominent IMF signal of SSVEP plays a vital role in the accurate detection of frequency. The proposed method achieves the average detection accuracy of 81.45% over four subjects; in contrast, the conventional method of PSA achieves average detection accuracy of 80.43%. The achieved result indicates that the proposed method out performs state of the art by more than 1.02% over four subjects.

     

Brain-computer interface design for asynchronous control applications: improvements to the LF-ASD asynchronous brain switch

The low-frequency asynchronous switch design (LF-ASD) was introduced as a direct brain-computer interface (BCI) technology for asynchronous control applications. The LF-ASD operates as an asynchronous brain switch (ABS) which is activated only when a user intends control and maintains an inactive state output when the user is not meaning to control the device (i.e., they may be idle, thinking about a problem, or performing some other action). Results from LF-ASD evaluations have shown promise, although the reported error rates are too high for most practical applications. This paper presents the evaluation of four new LF-ASD designs with data collected from individuals with high-level spinal cord injuries and able-bodied subjects. These new designs incorporated electroencephalographic energy normalization and feature space dimensionality reduction. The error characteristics of the new ABS designs were significantly better than the LF-ASD design with true positive rate increases of approximately 33% for false positive rates in the range of 1%-2%. The results demonstrate that the dimensionality of the LF-ASD feature space can be reduced without performance degradation. The results also confirm previous findings that spinal cord-injured subjects can operate ABS designs to the same ability as able-bodied subjects.     

在线脑机接口中脑电信号的特征提取与分类方法

在脑机接口研究中,针对运动想象脑电信号的特征抽取,提出了一种基于离散小波变换和AR模型的方法.利用Daubechies类小波函数对脑电信号进行3层分解,抽取小波变换系数的统计特征;利用Burg算法提取脑电信号6阶AR模型系数.将这两类特征进行组合后使用神经网络、支持向量机、马氏距离线性判别进行分类并比较分析.采用BCI...     

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     

基于ReliefF-Pearson的嗅觉脑电通道选择

基于脑电(EEG)信号的气味识别研究在嗅觉功能客观评价及嗅觉障碍疾病诊断等方面具有重要的应用价值。在实际应用场景中使用过多EEG通道会带来诸多不便,因此研究如何选择EEG通道尤为重要。该文针对嗅觉EEG信号分类中的通道选择问题,提出了一种新型的基于ReliefF-Pearson的嗅觉EEG通道选择算法。该算法结合ReliefF的权值思想和Pearson系数的相关性原理对EEG通道进行选择。结果表明,与传统基于ReliefF的通道选择算法相比,该文所提算法在保证一定分类准确率的同时能够显著减少使用的通道数量,并且通道选择的结果不依赖人为经验和分类器。此外,使用该方法获取的通道,其空间分布与已有的嗅觉神经生理学位置相一致,进一步证实了该方法的科学性和有效性。该文所提算法为嗅觉EEG通道选择的研究提供了新思路。     

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.     

基于条件经验模式分解和串并行CNN的脑电信号识别

针对运动想象脑电信号(EEG)的非线性、非平稳特点,该文提出一种结合条件经验模式分解(CEMD)和串并行卷积神经网络(SPCNN)的脑电信号识别方法。在CEMD过程中,采用各阶固有模式分量(IMF)与原始信号的相关性系数作为第1个IMF筛选条件,在此基础上,提出各阶IMF之间的相对能量占有率作为第2个IMF筛选条件。此外,为了考虑脑电信号各个通道之间的特征和突出每个通道内的特征,该文提出SPCNN网络模型对进行CEMD过程后的脑电信号进行分类。实验结果表明,在自行采集的脑电数据集上平均识别率达到94.58%。在公开数据集BCI competition IV 2b上平均识别率达到82.13%,比卷积神经网络提高了3.85%。最后,在自行设计的智能轮椅脑电控制平台上进行了轮椅前进、左转和右转在线控制实验,验证了该文算法对脑电信号识别的有效性。

     

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.