Detection of the interictal epileptic discharges based on wavelet bispectrum interaction and recurrent neural network

Detection of interictal epileptic discharges (IED) events in the EEG recordings is a critical indicator for detecting and diagnosing epileptic seizures.We propo...     

抑郁症静息脑电的小波包节点功率谱熵分析

头皮脑电(EEG)信号反映了大脑皮层神经元细胞群自发性节律性的电生理活动,含有丰富的生理与病理信息,是临床脑神经与精神疾病诊断的重要依据.针对抑郁症的研究和诊断中缺少客观有效的量化参数和指标的状况,提出一种基于小波包分解节点重构信号的功率谱熵值(记为W值)的脑电信号分析方法,并利用此方法对静息态的脑电信号进行计算和分析.实验和分析结果表明:抑郁症患者脑电信号S32节点(频率24~32 Hz)的熵值(置信区间[0.0129,0.0176])在部分脑区显著大于正常健康人(置信区间[0.0246,0.0303]),显示抑郁症病人快波节律的能量分布存在弥散性,符合现在关于抑郁症患者自我调节能力减弱的发病机制.对结果进行了T检验统计分析,证明了这种辨别方法的准确性和可行性,将为抑郁症疾病检测诊断提供有效的量化物理指标.     

An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism

Alcoholism affects the structure and functioning of brain. Electroencephalogram (EEG) signals can depict the state of brain. The EEG signals are ensemble of various neuronal activity recorded from different scalp regions having different characteristics and very low magnitude in microvolts. These factors make human interpretation difficult and time consuming to analyze these signals. Moreover, these highly varying EEG signals are susceptible to inter/intra variability errors. So, a Computer-Aided Diagnosis (CAD) can be used to identify the alcoholic and normal subjects accurately. However, these EEG signals exhibit nonlinear and non-stationary properties. Therefore, it needs much effort in deciphering the diagnostic evidence from them using linear time and frequency-domain methods. The nonlinear parameters together with time-frequency/scale domain methods can help to detect tiny changes in these signals. The correntropy is nonlinear indicator which characterizes the dynamic behavior of EEG signals in time-scale domain. In this paper, we present a new way for diagnosis of alcoholism using Tunable-Q Wavelet Transform (TQWT) based features derived from EEG signals. The feature extraction is performed using TQWT based decomposition and extracted Centered Correntropy (CC) from the forth decomposed detail sub-band. The Principal Component Analysis (PCA) is used for feature reduction followed by Least Squares-Support Vector Machine (LS-SVM) for classifying normal and alcoholic EEG signals. In order to make sure reliable classification performance, 10-fold cross-validation scheme is adopted. Our proposed system is able to diagnose the alcoholic and normal EEG signals, with an average accuracy of 97.02%, sensitivity of 96.53%, specificity of 97.50% and Matthews correlation coefficient of 0.9494 for Q-factor (Q) varying between 3 and 8 using Radial Basis Function (RBF) kernel function. Also, we have established a novel Alcoholism Risk Index (ARI) using three clinically significant features to discriminate the given classes by means of a single number. This system can be used for automated diagnosis and monitoring of alcoholic subjects to evaluate the effect of treatment.     

A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings

The purpose of this study was to evaluate a signal regularity-based automated seizure prediction algorithm for scalp EEG. Signal regularity was quantified using the Pattern Match Regularity Statistic (PMRS), a statistical measure. The primary feature of the prediction algorithm is the degree of convergence in PMRS (“PMRS entrainment”) among the electrode groups determined in the algorithm training process. The hypothesis is that the PMRS entrainment increases during the transition between interictal and ictal states, and therefore may serve as an indicator for prediction of an impending seizure.     

利用有限差分法计算真实头模型脑电正问题

脑电研究领域的两个关键问题是脑电正问题和脑电逆问题,脑电正问题是脑电逆问题的基础.由于复杂、非规则真实头模型中的脑电正问题不存在解析解,因此脑电源分析依赖于正问题数值算法的精度和效率.文章首先详细推导了有限差分算法求解三维各向同性脑电正问题的数学模型,然后在三层同心球模型上通过与解析解比较验证了该算法的精度和效率,最后将该算法应用于真实头模型.仿真结果表明,有限差分法可以有效地处理任意形状几何体的电位场分布问题,是模拟计算真实头模型中脑电正问题的有力工具.     

Electroencephalogram-Based Control of an Electric Wheelchair

This paper presents a study on electroencephalogram (EEG)-based control of an electric wheelchair. The objective is to control the direction of an electric wheelchair using only EEG signals. In other words, this is an attempt to use brain signals to control mechanical devices such as wheelchairs. To achieve this goal, we have developed a recursive training algorithm to generate recognition patterns from EEG signals. Our experimental results demonstrate the utility of the proposed recursive training algorithm and the viability of accomplishing direction control of an electric wheelchair by only EEG signals.     

Realization of artificial intelligence for integrative electroencephalogram interpretation

A fully automatic interpretation of an awake electroencephalogram (EEG) has been developed. Automatic integrative EEG interpretation consists of four main parts: quantitative EEG interpretation, EEG report making, preprocessing of EEG data, and adaptable EEG interpretation. Automatic integrative EEG interpretation reveals essentially the same findings as those of an electroencephalographer (EEGer), and is therefore applicable in clinical situations as an additional tool for the EEGer. The method has been developed through collaboration between the engineering field (Saga University) and the medical field (Kyoto University). This work can be understood as a realization of artificial intelligence. The procedure for this realization of artificial intelligence will also be applicable in other fields of systems control. This work was presented, in part, at the Second International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1997     

An EEG emotion recognition method based on transfer learning and echo state network for HilCPS

As a new cyber physical application, emotion recognition has been shown to make human-in-the-loop cyber-physical system (HilCPS) more efficient and sustainable. Therefore, emotion recognition is of great significance for HilCPS. Electroencephalogram (EEG) signals contain abundant and useful information, and can objectively reflect human emotional states. According to EEG signals, using machine learning to recognize emotion is the main method at present. This method depends on the quantity and quality of samples as well as the capability of classification model. However, the quantity of EEG samples is often insufficient and the quality of EEG samples is often irregular. Meanwhile, EEG samples possess strong nonlinearity. Therefore, an EEG emotion recognition method based on transfer learning (TL) and echo state network (ESN) for HilCPS is proposed in this paper. First, a selection algorithm of EEG samples based on average Frechet distance is proposed to improve the sample quality. Second, a feature transfer algorithm of EEG samples based on transfer component analysis is proposed to expand the sample quantity. Third, in order to solve the problem of the nonlinearity of EEG samples, a classification model of EEG samples based on ESN is constructed to accurately classify emotional states. Finally, experimental results show that compared with traditional methods, the proposed method can expand the quantity of the high-quality EEG samples and effectively improve the accuracy of emotion recognition.     

A novel method based on realistic head model for EEG denoising

It is necessary to remove the noise in EEG before further EEG analysis and processing. For EEG is deeply masked in the noise background, it is very difficult to denoise EEG effectively. Proposed in this paper is a novel realistic head model based sparse decomposition algorithm to denoise EEG, which is an iterative procedure combining the subject's physiology of EEG generation into the denoising procedure. In this algorithm, the lead field overcomplete dictionary is numerically calculated according to the realistic head model firstly, and then the instantaneous EEG spatial potential is decomposed into one sparse combination of atoms in the lead field matrix by matching pursuit, and the sparse combination of atoms is to be regarded as the denoised EEG signal. The realistic head based sparse decomposition was tested by the simulated noisy potential and a real EEG recording collected in an oddball stimulus experiment, the results consistently confirmed the new method removed the uncorrelated noise in EEG effectively.     

Classification of mental tasks using stockwell transform

In recent years, various physiological signal based rehabilitation systems have been developed for the physically disabled in which electroencephalographic (EEG) signal is one among them. The efficiency of such a system depends upon the signal processing and classification algorithms. In order to develop an EEG based rehabilitation or assistive system, it is necessary to develop an effective EEG signal processing algorithm. This paper proposes Stockwell transform (ST) based analysis of EEG dynamics during different mental tasks. EEG signals from Keirn and Aunon database were used in this study. Three classifiers were employed such as k-means nearest neighborhood (kNN), linear discriminant analysis (LDA) and support vector machine (SVM) to test the strength of the proposed features. Ten-fold cross validation method was used to demonstrate the consistency of the classification results. Using the proposed method, an average accuracy ranging between 84.72% and 98.95% was achieved for multi-class problems (five mental tasks).     

Subject adaptation network for EEG data analysis

Biosignals tend to display manifest intra- and cross-subject variance, which generates numerous challenges for electroencephalograph (EEG) data analysis. For instance, in the context of classification, the discrepancy between EEG data can make the trained model generalising poorly for new test subjects. In this paper, a subject adaptation network (SAN) inspired by the generative adversarial network (GAN) to mitigate different variances is proposed for analysing EEG data. First the challenges faced by traditional approaches employed for EEG signal processing are emphasised. Then the problem is formulated from mathematical perspective to highlight the key points in resolving such discrepancies. Third, the motivation behind and design principle of the SAN are described in an intuitive manner to reflect its suitability for analysing EEG data. Then after depicting the overall architecture of the SAN, several experiments are used to justify the practicality and efficiency of using the proposed model from different perspectives. For instance, an EEG dataset captured during a stereotypical neurophysiological experiment called the VEP oddball task is utilised to demonstrate the performance improvement achieved by running the SAN.     

Automatic classification of sleep stages based on the time-frequency image of EEG signals

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner–Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals.     

Automatic detection of the topographical distribution of EEG rhythms based on an iterative adjustment of the averaged reference potential

Electroencephalogram (EEG) interpretation is important for the investigation of brain diseases. In this study, an automatic technique was developed to detect the topographical distribution of EEG rhythms. In order to obtain the topographical distribution, the amplitude of the EEG rhythm was analyzed based on the referential derivation where the reference potential was adjusted iteratively. The result of the automatic detection of the topographical distribution was helpful in highlighting the EEG rhythms of interest for automatic EEG interpretation. The technique developed has application significance for real clinics.     

基于样本熵和模式识别的脑电信号识别算法研究

脑-机接口BCI是一种实现人脑和外部设备通信的新兴技术。基于时频特性进行特征提取的传统方法无法体现EEG信号的非线性特征。为了进一步提高分类的准确率,首先采用小波阈值降噪的预处理方法提高了EEG信号的信噪比。然后结合非线性动力学的样本熵参数,对3种想象运动的脑电信号进行特征提取,保留了脑电信号的非线性特征。其中,运动想象MI脑电信号的研究一直都是BCI这一高速发展领域的重点目标。还研究了支持向量机、LVQ神经网络和BP神经网络3种分类器。通过实验结果对比发现,BP神经网络具有较高的识别率,更适用于脑电信号的分类识别。     

EEG信号构建的复杂网络同步稳定性分析

人脑是自然界最复杂的系统之一,脑网络作为复杂网络理论在神经科学中的重要应用,为脑疾病的病理机制提供了新的研究方向。同步性作为影响网络性能的指标,对于复杂网络有着重要影响。为了研究同步性在脑网络中的表现,利用EEG动力学方程对120例酗酒病人的EEG信号进行复杂网络模型构造,根据所构造的模型利用李雅普诺夫稳定性理论进行证明。并通过实验对正常人和酗酒者脑网络同步状态做出统计,给出了酗酒病人和正常人的脑网络同步差异,可以揭示酗酒疾病对于人脑在功能结构上的影响,对其他疾病提供研究思路。     

Scale-free brain ensemble modulated by phase synchronization

To listen to brain activity as a piece of music, we proposed the scale-free brainwave music （SFBM） technology, which could translate the scalp electroencephalogram （EEG） into music notes according to the power law of both EEG and music. In the current study, this methodology was further extended to a musical ensemble of two channels. First, EEG data from two selected channels are translated into musical instrument digital interface （MIDI） sequences, where the EEG parameters modulate the pitch, duration, and volume of each musical note. The phase synchronization index of the two channels is computed by a Hilbert transform. Then the two MIDI sequences are integrated into a chorus according to the phase synchronization index. The EEG with a high synchronization index is represented by more consonant musical intervals, while the low index is expressed by inconsonant musical intervals. The brain ensemble derived from real EEG segments illustrates differences in harmony and pitch distribution during the eyes-closed and eyes-open states. Furthermore, the scale-free phenomena exist in the brainwave ensemble. Therefore, the scale-free brain ensemble modulated by phase synchronization is a new attempt to express the EEG through an auditory and musical way, and it can be used for EEG monitoring and bio-feedback.     

Epileptic seizure endorsement technique using DWT power spectrum

EEG signals play significant role in the study of mental disorders. Epilepsy is one of the major mental disorders and need significant technological support in the treatment. A method proposed here is an endorsement technique for epileptic seizures using electroencephalogram (EEG) signals captured using non-invasive method. The method uses power spectrum density and discrete wavelet transformation (DWT). The impact of power spectral analysis along with the usage of EEG characteristics in endorsement of epilepsy is addressed here. A publicly available EEG epileptic dataset is processed using FIR filters along with DWT. The power spectrum density and its average were compared with specific spectrum to get the results and were compared against the standard EEG signal frequency range. It is found that the usage of DWT is more accurate and reliable to process and classify the EEG data for epilepsy endorsement.

     

Clustering technique-based least square support vector machine for EEG signal classification

This paper presents a new approach called clustering technique-based least square support vector machine (CT-LS-SVM) for the classification of EEG signals. Decision making is performed in two stages. In the first stage, clustering technique (CT) has been used to extract representative features of EEG data. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted features to classify two-class EEG signals. To demonstrate the effectiveness of the proposed method, several experiments have been conducted on three publicly available benchmark databases, one for epileptic EEG data, one for mental imagery tasks EEG data and another one for motor imagery EEG data. Our proposed approach achieves an average sensitivity, specificity and classification accuracy of 94.92%, 93.44% and 94.18%, respectively, for the epileptic EEG data; 83.98%, 84.37% and 84.17% respectively, for the motor imagery EEG data; and 64.61%, 58.77% and 61.69%, respectively, for the mental imagery tasks EEG data. The performance of the CT-LS-SVM algorithm is compared in terms of classification accuracy and execution (running) time with our previous study where simple random sampling with a least square support vector machine (SRS-LS-SVM) was employed for EEG signal classification. We also compare the proposed method with other existing methods in the literature for the three databases. The experimental results show that the proposed algorithm can produce a better classification rate than the previous reported methods and takes much less execution time compared to the SRS-LS-SVM technique. The research findings in this paper indicate that the proposed approach is very efficient for classification of two-class EEG signals.     

面向运动想象脑电图识别的镜卷积神经网络

目的 脑电图（electroencephalogram,EEG）是一种灵活、无创、非侵入式的大脑监测方法,广泛应用于运动想象脑机接口系统中,运动想象脑电图识别精度是决定系统性能的关键因素。然而由于脑电图采集时间长、个体差异大等原因,导致单个受试者可用于模型训练的样本数量少,严重影响了卷积神经网络在脑电图识别任务中的表现。为此,本文提出一种镜卷积神经网络（mirror convolutional neural network,MCNN）模型,使用集成学习与数据扩增方法提高运动想象脑电图识别精度。方法 在训练阶段,基于源脑电通过互换左右侧脑电通道构造镜像脑电,并与源脑电一起用于源卷积网络训练,有效扩增了训练样本;在预测阶段,复制已训练源卷积网络作为镜像卷积网络,将测试集中的源脑电输入源卷积网络,构造的镜像脑电输入镜像卷积网络,集成源卷积网络与镜像卷积网络输出的类别预测概率,形成最终类别预测。结果 为了验证模型的有效性和通用性,基于3种不同运动想象脑电图识别卷积网络模型分别构造镜卷积网络,并在第4届脑机接口大赛2a与2b数据集上进行实验验证。实验结果与原始模型相比,运动想象四分类和二分类准确率分别平均提高了4.83%和4.61%,显著提高了识别精度。结论 本文面向运动想象脑电图识别,提出了镜卷积神经网络模型,通过集成学习与数据扩增方法提高运动想象识别精度,有效改善了运动想象脑机接口性能。     

基于运动想象脑电的上肢康复机器人

针对脑中风偏瘫患者的康复训练,设计了一种基于运动想象脑电的上肢康复机器人系统.首先,利用肢体运动3维动画刺激患者进行运动想象并通过USB脑电放大器采集运动想象脑电信号:然后,采用小波包算法进行特征向量的提取,并通过基于马氏距离的线性判别分类器分类;最后,PC利用虚拟现实技术进行视觉反馈,同时控制康复机器人.该系统使用患...