Local temporal common spatial patterns for robust single-trial EEG classification.

In this paper, we propose a novel optimal spatio-temporal filter, termed local temporal common spatial patterns (LTCSP), for robust single-trial elctroencephalogram (EEG) classification. Different from classical common spatial patterns (CSP) that uses only global spatial covariances to compute the optimal filter, LTCSP considers temporally local information in the variance modelling. The underlying manifold variances of EEG signals contain more discriminative information. LTCSP is an extension to CSP in the sense that CSP can be derived from LTCSP under a special case. By constructing an adjacency matrix, LTCSP is formulated as an eigenvalue problem. So, LTCSP is computationally as straightforward as CSP. However, LTCSP has better discrimination ability than CSP and is much more robust. Simulated experiment and real EEG classification demonstrate the effectiveness of the proposed LTCSP method.     

Current trends in Graz Brain-Computer Interface (BCI) research.

This paper describes a research approach to develop a brain-computer interface (BCI) based on recognition of subject-specific EEG patterns. EEG signals recorded from sensorimotor areas during mental imagination of specific movements are classified on-line and used e.g. for cursor control. In a number of on-line experiments, various methods for EEG feature extraction and classification have been evaluated.     

基于小波包变换的癫痫脑电信号特征提取

为了有效识别癫痫脑电信号,提出了一种适合于非平稳脑电信号的特征提取方法。以临床采集的包含癫痫发作期的5组500个EEG公共数据为样本,选择了具有任意多分辨分解特性的小波包变换,对信号进行多尺度分解,并提取了各级节点的小波包系数。将小波包系数能量作为特征值,构建了特征向量并输入到BP神经网络分类器中进行自动识别。实验结果表明,该算法的识别率达到了91.5%。     

基于脑电微状态的虚拟现实晕动症研究

针对脑电节律能量无法反映时间信息且对空间信息的探讨并不充分等问题,通过运用微状态分析方法,对虚拟现实 晕动症(vRMS)相关脑电信号的时空模式进行了研究,从而检测虚拟现实晕动症。使用多元变分模态分解(MVMD) 将脑电信 号划分为从低频到高频的5个频段,分析了脑电微状态的出现频率、平均持续时间、覆盖率以及转换率的变化,最后利用统计 分析和分类方法验证这些特征的有效性。研究结果表明,5个频段融合所有特征的分类准确率达到最大值83.9%。因此,微 状态方法可望为研究VRMS 提供新思路。     

离散差分模块在癫痫脑电分类中的应用

针对癫痫脑电信号多分类的精度提升问题,提出了一种基于信号转差分模块与卷积模块结合的分类算法.信号转差分模块对原始脑电信号进行多阶差分运算,得到描述其波动特征的差分表示;然后卷积模块动态学习的方式将差分脑电信号转换为图片,利用预训练的卷积神经网络来提取信号特征并实现自动分类.分类结果表明,与现有研究相比,所提出的方法的最...     

Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain-computer interface

Although brain-computer interface (BCI) techniques have been developing quickly in recent decades, there still exist a number of unsolved problems, such as improvement of motor imagery (MI) signal classification. In this paper, we propose a hybrid algorithm to improve the classification success rate of MI-based electroencephalogram (EEG) signals in BCIs. The proposed scheme develops a novel cross-correlation based feature extractor, which is aided with a least square support vector machine (LS-SVM) for two-class MI signals recognition. To verify the effectiveness of the proposed classifier, we replace the LS-SVM classifier by a logistic regression classifier and a kernel logistic regression classifier, separately, with the same features extracted from the cross-correlation technique for the classification. The proposed approach is tested on datasets, IVa and IVb of BCI Competition III. The performances of those methods are evaluated with classification accuracy through a 10-fold cross-validation procedure. We also assess the performance of the proposed method by comparing it with eight recently reported algorithms. Experimental results on the two datasets show that the proposed LS-SVM classifier provides an improvement compared to the logistic regression and kernel logistic regression classifiers. The results also indicate that the proposed approach outperforms the most recently reported eight methods and achieves a 7.40% improvement over the best results of the other eight studies.     

Application of tripolar concentric electrodes and prefeature selection algorithm for brain-computer interface.

For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals were acquired. An autoregressive (AR) model was developed for feature extraction with a Mahalanobis distance based linear classifier for classification. An exhaust selection algorithm was employed to analyze three factors before feature extraction. The factors analyzed were 1) length of data in each trial to be used, 2) start position of data, and 3) the order of the AR model. The results showed that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes.     

Application of Tripolar Concentric Electrodes and Prefeature Selection Algorithm for Brain–Computer Interface

For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals were acquired. An autoregressive (AR) model was developed for feature extraction with a Mahalanobis distance based linear classifier for classification. An exhaust selection algorithm was employed to analyze three factors before feature extraction. The factors analyzed were 1) length of data in each trial to be used, 2) start position of data, and 3) the order of the AR model. The results showed that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes.     

Improving motor imagery classification with a new BCI design using neuro-fuzzy S-dFasArt

This paper presents an algorithm based on neural networks and fuzzy theory (S-dFasArt) to classify spontaneous mental activities from electroencephalogram (EEG) signals, in order to operate a noninvasive brain-computer interface. The focus is placed on the three-class problem, left-hand movement imagination, right movement imagination and word generation. The algorithm allows a supervised classification of temporal patterns improving the classification rates of the BCI Competition III (Data Set V: multiclass problem, continuous EEG). Using the precomputed data supplied for the competition and following the rules established there, a new method based on S-dFasArt, along with rule prune and voting strategy is proposed. The results have been compared with other published methods improving their success rates.     

基于TRCSP和L2范数的脑电通道选择方法

脑-机接口（BCI）系统常用高密度电极通道来获取较高空间分辨率的脑电（EEG）信号，但同时也会引入过多的噪声通道，影响脑电的解码性能。为了消除无关的噪声通道，提出了一种基于Tikhonov正则化共空间模式（TRCSP）和L2范数的运动想象脑电通道选择方法。首先基于TRCSP和分类器得到最优的空间滤波器，接着基于L2范数对空间滤波器得到的各通道的权重值进行排序。选择前K个通道的数据进行CSP特征提取，根据分类器的分类准确率确定最优K值，进而得到最优的通道数和通道组合。在实验中，使用6种分类器分别在BCI竞赛III（2005）数据集IVa和实验室自采集数据上验证所提出的通道选择方法的有效性。所提出的方法在两个数据集上的平均分类准确率分别达到了87.57%和74.32%，优于其它现有的方法。     

基于离散小波变换的卷积自编码运动想象脑电信号的分类

左右手运动想象脑电信号(MI-EEG)分类准确率低,制约了相关脑-机接口技术的发展。实验采集了16名健康受试者的运动想象脑电信号,提出了一种基于离散小波变换(DWT)和卷积自编码(CAE)的运动想象脑电信号分类算法。利用离散小波变换将EEG转换成时频矩阵,输入到卷积自编码网络中进行脑电信号的特征分类。该算法在实验数据集和公开数据集上测试都得到了较好的分类结果,静息-想象左手、静息-想象右手、想象左手-想象右手3组EEG在实验数据集上分类准确率分别为97.36%、97.27%、86.82%,在公开数据集上分类准确率分别为99.30%、98.23%、92.67%。离散小波变换结合卷积自编码网络模型在左右手运动想象脑电信号分类应用中比其他深度学习方法(CNN、LSTM、STFT-CNN)性能更优。     

Comparison of linear, nonlinear, and feature selection methods for EEG signal classification

The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.     

A time-frequency approach for newborn seizure detection

Techniques previously designed for seizure detection in newborns using the electroencephalogram (EEG) have been relatively inefficient due to their assumption of local stationarity of the EEG. To overcome the problem raised by the nonstationarity of the EEG signal, current methods are extended to a time-frequency approach. This allows the analysis and characterization of the different newborn EEG patterns that are intended to be the first step toward an automatic time-frequency seizure detection and classification. An in-depth analysis of both the autocorrelation and spectrum seizure detection techniques identified the detection criteria that can be extended to the time-frequency domain. The selected method uses a high-resolution reduced interference time-frequency distribution referred to as the B-distribution (BD). Here, the authors present the various patterns of observed time-frequency seizure signals and relate them to current knowledge of seizures. In particular, initial results indicate that a quasilinear instantaneous frequency (IF) can be used as a critical feature of the EEG seizure characteristics     

Single trial estimation of event-related potential components using spatiotemporal filtering and artificial bee colony optimized Gaussian kernel mixture model

Single trial estimation of event-related potential (ERP) components is an open research topic in neuroscience. In this article, we have proposed a method to improve the performance of spatiotemporal filtering by decreasing its dependency to prior estimates of ERP components. For this purpose, we have used a mixture of Gaussian kernels instead of a raw prior signal, and the parameters of the Gaussian kernel are estimated using artificial bee colony algorithm. The algorithm starts with one Gaussian kernel, and after optimizing its parameters, another Gaussian kernel is added. This procedure goes on until the stopping criterion is reached. The efficiency of the algorithm is tested for one single uncorrelated component and two correlated components for synthesized electroencephalogram (EEG) signal. Also, the efficiency of the proposed method is presented on real data for extraction of N170 component in real EEG data.     

基于多域信息融合的脑电情感识别研究

脑电信号识别方法较少将空间、时间和频率信息相融合,为了充分挖掘脑电信号包含的丰富信息,本文提出一种多域信息融合的脑电情感识别方法。该方法利用二维卷积神经网络和一维卷积神经网络相结合的并行卷积神经网络(PCNN)模型学习脑电信号的空间、时间和频率特征,来对人类情感状态进行分类。其中,2D-CNN用于挖掘相邻EEG通道间的空间和频率信息,1D-CNN用于挖掘EEG的时间和频率信息。最后,将两个并行卷积模块提取的信息融合进行情感识别。在数据集SEED上的情感三分类实验结果表明,融合空间、时间、频率特征的PCNN整体分类准确率达到了98.04%,与只提取空频信息的2D-CNN和提取时频信息的1D-CNN相比,准确率分别提高了1.97%和0.60%。并于最近的类似工作相比,本文提出的方法对于脑电情感分类具有一定的优越性。     

Digital processing of EEG signals

The digitization of electroencephalogram (EEG) signal data is the essential first step in using computers to analyse and manipulate EEG data. EEG signals are inherently complicated due to their nonGaussian, nonstationary, and often nonlinear nature as shown by most of the articles of this special issue. On top of that, the small amplitude of these signals reinforce their sensitivity to various artifacts and noise sources. The aim of this special issue is to shed light onto the recent digital techniques for processing EEG signals ranging from storage and artifact removal to event detection/classification and prediction issues     

Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis.

We have developed a novel approach using source analysis for classifying motor imagery tasks. Two-equivalent-dipoles analysis was proposed to aid classification of motor imagery tasks for brain-computer interface (BCI) applications. By solving the electroencephalography (EEG) inverse problem of single trial data, it is found that the source analysis approach can aid classification of motor imagination of left- or right-hand movement without training. In four human subjects, an averaged accuracy of classification of 80% was achieved. The present study suggests the merits and feasibility of applying EEG inverse solutions to BCI applications from noninvasive EEG recordings.     

Wireless Power Transmission via Sheet Medium Using Automatic Phase Adjustments of Multiple Inputs

Wireless power transmission via a sheet medium is a novel physical form of communication that utilizes a surface as a medium to provide both data and power transmission services. To efficiently transmit a relatively large amount of electric power (several watts), we have developed a wireless power transmission system via sheet medium that concentrates electric power on a specific spot by using phase control of multiple inputs. However, to find the optimal phases of the multiple inputs that let the microwave energy converge on a specific spot in the sheet medium, prior knowledge of the device's position and a preliminary experiment measuring the output power are needed. In the field of wireless communication, it is known that the retrodirective array scheme can efficiently transmit power in a self‐phasing manner, which uses pilot signals sent by client devices. In this paper, we apply the retrodirective array scheme to our wireless power transmission system via a sheet medium, and propose a power transmission scheme using the phase‐adjusted power transmission inputs. To confirm the effectiveness of the proposed scheme, we evaluate its performance by computer simulation and real‐world measurement. Both results show that the proposed scheme can achieve retrodirectivity over wireless power transmission via a sheet medium.     

A new brain-computer interface design using fuzzy ARTMAP

This paper proposes a new brain-computer interface (BCI) design using fuzzy ARTMAP (FA) neural network, as well as an application of the design. The objective of this BCI-FA design is to classify the best three of the five available mental tasks for each subject using power spectral density (PSD) values of electroencephalogram (EEG) signals. These PSD values are extracted using the Wiener-Khinchine and autoregressive methods. Ten experiments employing different triplets of mental tasks are studied for each subject. The findings show that the average BCI-FA out- puts for four subjects gave less than 6% of error using the best triplets of mental tasks identified from the classification performances of FA. This implies that the BCI-FA can be successfully used with a tri-state switching device. As an application, a proposed tri-state Morse code scheme could be utilized to translate the outputs of this BCI-FA design into English letters. In this scheme, the three BCI-FA outputs correspond to a dot and a dash, which are the two basic Morse code alphabets and a space to denote the end (or beginning) of a dot or a dash. The construction of English letters using this tri-state Morse code scheme is determined only by the sequence of mental tasks and is independent of the time duration of each mental task. This is especially useful for constructing letters that are represented as multiple dots or dashes. This combination of BCI-FA design and the tri-state Morse code scheme could be developed as a communication system for paralyzed patients.     

变压器局部放电多目标无源定位中去除测向交叉定位虚假点的快速算法

测向交叉定位是一种通过对放电信号进行分析获得放电源位置信息的方法,但在多放电源的情况下存在大量的虚假定位,如何消除测向线虚假定位是其关键技术.文中提出一种利用同一信号的相关性原理,对两个阵列测向结果进行信号分离来去除虚假定位的方法.该方法首先运用多重信号分类(Multiple Signal Classification,MUSIC)算法对多个局部放电源进行观测和计算,得到各个信号源的方位:然后利用信号的方向信息分别进行信号的分离,再将分离信号进行相关性运算,依据信号本身的相关性进行位置配对,从而去除了虚假定位.该方法只需要测向信息,无须其他先验知识.仿真结果表明了该方法的有效性和实用性.