基于PCANet和SVM的谎言测试研究

主成分分析网络（Principal Component Analysis Network,PCANet）是基于深度学习理论的一种非监督式的特征提取方法,它克服了手工提取特征的缺点,目前其有效性仅仅在图像处理领域中得到了验证。本文针对当前谎言测试方法中脑电信号特征提取困难的缺点,首次将PCANet方法应用到一维信号的特征提取领域,并对测谎实验的原始脑电信号提取特征,然后使用支持向量机（Support Vector Machine,SVM）将说谎者和诚实者的两类信号进行分类识别,将实验结果和其它分类器及未使用特征提取的分类效果进行了比较。实验结果显示相对未抽取任何特征的方法,提出的方法PCANet－SVM可以获得更高的训练和测试准确率,表明了PCANet方法对于脑电信号特征提取的有效性,也为基于脑电信号的测谎提供了一种新的途径。     

Predicting Reaching Targets from Human EEG

Extracting reach information from brain signals is of great interest to the fields of brain-computer interfaces (BCIs) and human motor control. To date, most work in this area has focused on invasive intracranial recordings; however, successful decoding of reach targets from noninvasive electroencephalogram (EEG) signals would be of great interest. In this article, we show that EEG signals contain sufficient information to decode target location during a reach (Experiment 1) and during the planning period before a reach (Experiment 2). We discuss the application of independent component analysis and dipole fitting for removing movement artifacts. With this technique we get similar classification accuracy for classifying EEG signals during a reach (Experiment 1) and during the planning period before a reach (Experiment 2). To the best of our knowledge, this is the first demonstration of decoding (planned) reach targets from EEG. These results lay the foundation for future EEG-based BCIs based on decoding of planned reaches.     

Nonlinear considerations in EEG signal classification

We investigate the effect of incorporating modeling of nonlinearity on the classification of electroencephalogram (EEG) signals using an artificial neural network (ANN). It is observed that the ANN's predictive ability is improved after preprocessing EEG signals using a particular nonlinear modeling technique, viz. a bilinear model, compared with those obtained by using a particular classical linear analysis method, viz. an autoregressive (AR) model. Until recently, linear time-invariant Gaussian modeling has dominated the development of time series modeling and feature extraction. The advantage of such classical models lies in the fact that a complete signal processing theory is available. In the case of EEG signals, where the underlying theory regarding the dynamical law governing the generation of these signals (e,g., the underlying physiological factors) is not completely understood, a case can be made for using improved signal processing models that are not subject to linear constraints. Such models should recognize important features of the observed data that may not be well modeled by a linear time-invariant model. It is known that EEG signals are nonstationary, and it is possible that they may be nonlinear as well. Thus, one way of gaining further insights on the structure of EEG signals is to introduce nonlinear models and higher order spectra. This paper compares the results of classification using a linear AR model with those obtained from a bilinear model. It is shown that in certain cases, the nonlinearity of the EEG signals is an important factor that ought to be taken into consideration during preprocessing of the signals prior to the classification task     

基于脑电样本熵的测谎分析

测谎分析在刑讯侦查和法律审判中具有重要意义.为了区分是否说谎,根据脑电信号的非线性特征,本文首次使用非线性动力学的样本熵方法分析30名受试者处于诚实和说谎两种状态时脑电信号的样本熵值.研究发现：受试者处于诚实状态时的熵值波动范围明显小于说谎状态下的波动范围,更重要的是说谎时的熵值显著高于说实话时的熵值,表明样本熵可以区分诚实和说谎两种不同状态下的脑电信号,该研究为基于脑电的测谎提供了一种新的途径.     

Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces

In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed.     

An Adaptive Radial Basis Function Neural Network Filter for Noise Reduction in Biomedical Recordings

Electroencephalogram (EEG) recordings often experience interference by different kinds of noise, including white, muscle and baseline, severely limiting its utility. Artificial neural networks (ANNs) are effective and powerful tools for removing interference from EEGs. Several methods have been developed, but ANNs appear to be the most effective for reducing muscle and baseline contamination, especially when the contamination is greater in amplitude than the brain signal. An ANN as a filter for EEG recordings is proposed in this paper, developing a novel framework for investigating and comparing the relative performance of an ANN incorporating real EEG recordings. This method is based on a higher-order statistics-based radial basis function (RBF) network. This ANN improves the results obtained with the conventional EEG filtering techniques: wavelet, singular value decomposition, principal component analysis, adaptive filtering and independent components analysis. Average results for the RBF-based method provided a noise reduction (SIR) of (mean\(\pm \) SD) \(\mathrm{SIR}=19.3\pm 0.3\) in contrast to traditional compared methods that, for the best case, yielded \(\mathrm{SIR}=15.2\pm 0.3\). The system has been evaluated within a wide range of EEG signals. The present study introduces a new method of reducing all EEG interference signals in one step with low EEG distortion and high noise reduction.     

Detection of evoked potentials with the use of the stochastic filtering theory

For detecting evoked potentials (EPs) in electroencephalogram (EEG) signals, a mathematical model of EEG without EP is constructed and equations of stochastic filtering are synthesized. From the analysis of updating sequences, an EP detection criterion is formulated. An EP detection algorithm is developed and studied, and its efficiency is confirmed by means of simulation.     

Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network

There are numerous neurological disorders such as dementia, headache, traumatic brain injuries, stroke, and epilepsy. Out of these epilepsy is the most prevalent neurological disorder in the human after stroke. Electroencephalogram (EEG) contains valuable information related to different physiological state of the brain. A scheme is presented for detecting epileptic seizures from EEG data recorded from normal subjects and epileptic patients. The scheme is based on discrete wavelet transform (DWT) analysis and approximate entropy (ApEn) of EEG signals. Seizure detection is performed in two stages. In the first stage, EEG signals are decomposed by DWT to calculate approximation and detail coefficients. In the second stage, ApEn values of the approximation and detail coefficients are calculated. Significant differences have been found between the ApEn values of the epileptic and the normal EEG allowing us to detect seizures with 100 % classification accuracy using artificial neural network. The analysis results depicted that during seizure activity, EEG had lower ApEn values compared to normal EEG. This gives that epileptic EEG is more predictable or less complex than the normal EEG. In this study, feed-forward back-propagation neural network has been used for classification and training algorithm for this network that updates the weight and bias values according to Levenberg–Marquardt optimization technique.     

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.     

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

Abstract-The development of asynchronous brain-computer interface （BCI） based on motor imagery （M1） 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.     

Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms

In this paper, we propose a method for the analysis and classification of electroencephalogram (EEG) signals using EEG rhythms. The EEG rhythms capture the nonlinear complex dynamic behavior of the brain system and the nonstationary nature of the EEG signals. This method analyzes common frequency components in multichannel EEG recordings, using the filter bank signal processing. The mean frequency (MF) and RMS bandwidth of the signal are estimated by applying Fourier-transform-based filter bank processing on the EEG rhythms, which we refer intrinsic band functions, inherently present in the EEG signals. The MF and RMS bandwidth estimates, for the different classes (e.g., ictal and seizure-free, open eyes and closed eyes, inter-ictal and ictal, healthy volunteers and epileptic patients, inter-ictal epileptogenic and opposite to epileptogenic zone) of EEG recordings, are statistically different and hence used to distinguish and classify the two classes of signals using a least-squares support vector machine classifier. Experimental results, with 100 % classification accuracy, on a real-world EEG signals database analysis illustrate the effectiveness of the proposed method for EEG signal classification.     

Computer analysis of EEG signals with parametric models

Fifty years ago Berger made the first registrations of the electrical activity of the brain with electrodes placed on the intact skull. It immediately became clear that the frequency content of recorded signals plays an important role in describing these signals and also the state of the brain. This paper briefly surveys the main properties the electroencephalogram (EEG), and points out several influential factors. A number of methods have been developed to quantify the EEG in order to complement visual screening; these are conveniently classified as being parametric or nonparametric. The paper emphasizes parametric methods, in which signal analysis is based on a mathematical model of the observed process. The scalar or multivariate model is typically linear, with parameters being either time invariant or time variable. Algorithms to fit the model to observed data are surveyed. Results from the analysis my be used to describe the spectral properties of the EEG, including the way in which characteristic variables change with time. Parametric models have successfully been applied to detect the occurrence of transients with epiliptic origin, so-called spikes and sharp waves. Interesting results have also been obtained by combining parameter estimation with classification algorithms in order to recognize significant functional states of the brain. The paper emphasizes methodology but includes also brief accounts of applications for research and clinical use. These mainly serve to illustrate the progress being made and to indicate the need for further work. The rapid advance of computer technology makes the processed EEG an increasingly viable tool in research and clinical practice.     

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).     

Diagnostic instrumentation for neurological injury

We have developed a prototype neonatal cortical injury monitor (NCIM) that fulfills the need to properly monitor 1-11 infants at a very early stage of life. NCIM provides two advanced analytical tools aimed at forecasting neurological outcome following hypoxic-ischemic injury to the brain. Our studies led us to concentrate on electroencephalogram (EEG) data from the first four hours after injury. We developed two metrics from which to prognosticate outcome: EEG normalized separation (NS) and number and duration of electrical bursts in the EEG. We developed a PC-based instrument that incorporates these metrics with a user-friendly graphical interface for acquisition, analysis, and display of the quantitative EEG results. We developed and tested these algorithms using a database of EEG signals     

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。      

基于Wigner分布的脑电信号处理

临床实践表明,脑电信号中包含有大量的生理与疾病信息。对脑电信号进行行之有效的处理,不仅可以为医生提供临床诊断信息,而且可以为某些脑疾病的治疗提供有效的治疗手段。目前,随着信号处理技术的发展,在脑电信号处理中已应用了多种信号分析方法来提取脑电信号中所包含的信息,但大多数还是停留在理论研究阶段。本文在研制虚拟式脑电图仪的过程中,考虑到Wigner分布在各种时频分布中具有最简单的形式和良好的性质,从临床应用及医学研究相结合的角度出发,应用Wigner分布对脑电信号进行时频分析以提取脑电信号中的特征信息。对实测脑电数据的分析表明,应用此方法可获得较好的分析效果。     

基于互信息的脑网络及测谎研究

互信息分析方法是基于信息论提出的一种描述两信号间信息交互情况的算法,其在脑电信号领域的有效性已得到了充分证实.针对当前测谎方法中脑电信号特征提取困难以及大脑整体认知功能分析在脑认知科学研究中越来越被重视的情况,本文首次将互信息分析方法应用到脑电测谎领域中,使用互信息量化大脑各节点之间的相关性,对计算结果进行统计分析,选取出在两类人群中具有显著性差异的电极对的互信息作为分类特征,进行模式识别,得到了99.67%的准确率.这一结果表明,互信息分析方法是一种有效的脑功能连接分析方法,为基于脑电信号连接分析的测谎研究提供了一种新的途径.另外,对说谎与诚实两类受试者的大脑功能网络的分析结果表明：处于说谎状态时,大脑的额叶、顶叶、颞叶及枕叶之间协同实现谎言功能,并在躯体行为所对应的脑区与其他脑区的连接上也表现出相对诚实组的显著性差异,以上结果均有助于进一步揭示谎言的神经活动机制.     

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.