Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals.Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.

A Method for Quantifying the Emotional Intensity and Duration of a Startle Reaction with Customized Fractal Dimensions of EEG Signals

The assessment of emotions with fractal dimensions of EEG signals has been attempted before, but the quantification of the intensity and duration of sudden and short emotions remains a challenge. This paper suggests a method for this purpose, by using a new fractal dimension algorithm and by adjusting the amplitude of the EEG signal in order to obtain maximal separation of high and low fractal dimensions. The emotion was induced by embedding a scary image at 20 seconds in landscape videos of 60 seconds length. The new method did not only detect the onset of the emotion correctly, but also revealed its duration and intensity. The intensity is based on the magnitude and impulse of the fractal dimension signal. It is also shown that Higuchi’s method does not always detect emotion spikes correctly;on the contrary, the region of the expected emotional response can be represented by fractal dimensions smaller than the rest of the signal, whereas the new method directly reveals distinct spikes. The duration of these spikes was 10 - 11 seconds. The magnitude of these spikes varied across the EEG channels. The build-up and cool-down of the emotions can occur with steep and flat gradients.

Classification of Imagined Speech EEG Signals with DWT and SVM

With the development of human-computer interaction technology,brain-computer interface(BCI)has been widely used in medical,entertainment,military,and other fields.Imagined speech is the latest paradigm of BCI and represents the mental process of imagining a word without making a sound or making clear facial movements.Imagined speech allows patients with physical disabilities to communicate with the outside world and use smart devices through imagination.Imagined speech can meet the needs of more complex manipulative tasks considering its more intuitive features.This study proposes a classification method of imagined speech Electroencephalogram(EEG)signals with discrete wavelet transform(DWT)and support vector machine(SVM).An open dataset that consists of 15 subjects imagining speaking six different words,namely,up,down,left,right,backward,and forward,is used.The objective is to improve the classification accuracy of imagined speech BCI system.The features of EEG signals are first extracted by DWT,and the imagined words are clas-sified by SVM with the above features.Experimental results show that the proposed method achieves an average accuracy of 61.69%,which is better than those of existing methods for classifying imagined speech tasks.

EEGbands: A Computer Program to Statistically Analyze Parameters of Electroencephalographic Signals

The quantitative analysis of electroencephalographic activity (EEG) is a useful tool for the study of changes in brain electrical activity during cognitive and behavioral functions in several experimental conditions. Their recording and analysis are currently carried out primarily through the use of computer programs. This paper presents a computerized program (EEGbands) created for Windows operating systems using the Delphi language, and designed to analyze EEG signals and facilitate their quantitative exploration. EEGbands applies Rapid Fourier Transformation to the EEG signals of one or more groups of subjects to obtain absolute and relative power spectra. It also calculates both interhemispheric and intrahemispheric correlation and coherence spectra and, finally, applies parametrical statistical analysis to these spectral parameters calculated for wide frequency EEG bands. Unlike other programs, EEGbands is simple and inexpensive, and rapidly and precisely generates results files with the corresponding statistical significances. The efficacy and versatility of EEGbands allow it to be easily adapted to different experimental and clinical needs.

Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals

The term Epilepsy refers to a most commonly occurring brain disorder after a migraine.Early identification of incoming seizures significantly impacts the lives of people with Epilepsy.Automated detection of epileptic seizures(ES)has dramatically improved the life quality of the patients.Recent Electroencephalogram(EEG)related seizure detection mechanisms encountered several difficulties in real-time.The EEGs are the non-stationary signal,and seizure patternswould changewith patients and recording sessions.Further,EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of ESs.Artificial intelligence(AI)methods in the domain of ES analysis use traditional deep learning(DL),and machine learning(ML)approaches.This article introduces an Oppositional Aquila Optimizer-based Feature Selection with Deep Belief Network for Epileptic Seizure Detection(OAOFS-DBNECD)technique using EEG signals.The primary aim of the presented OAOFS-DBNECD system is to categorize and classify the presence of ESs.The suggested OAOFS-DBNECD technique transforms the EEG signals into.csv format at the initial stage.Next,the OAOFS technique selects an optimal subset of features using the preprocessed data.For seizure classification,the presented OAOFS-DBNECD technique applies Artificial Ecosystem Optimizer(AEO)with a deep belief network(DBN)model.An extensive range of simulations was performed on the benchmark dataset to ensure the enhanced performance of the presented OAOFS-DBNECD algorithm.The comparison study shows the significant outcomes of the OAOFS-DBNECD approach over other methodologies.In addition,the result of the suggested approach has been evaluated using the CHB-MIT database,and the findings demonstrate accuracy of 97.81%.These findings confirmed the best seizure categorization accuracy on the EEG data considered.

Multi-View & Transfer Learning for Epilepsy Recognition Based on EEG Signals

Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-tic EEG signals and develop artificial intelligence(AI)-assist recognition,a multi-view transfer learning(MVTL-LSR)algorithm based on least squares regression is proposed in this study.Compared with most existing multi-view transfer learning algorithms,MVTL-LSR has two merits:(1)Since traditional transfer learning algorithms leverage knowledge from different sources,which poses a significant risk to data privacy.Therefore,we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance.(2)When utilizing multi-view data,we embed view weighting and manifold regularization into the transfer framework to measure the views’strengths and weaknesses and improve generalization ability.In the experimental studies,12 different simulated multi-view&transfer scenarios are constructed from epileptic EEG signals licensed and provided by the Uni-versity of Bonn,Germany.Extensive experimental results show that MVTL-LSR outperforms baselines.The source code will be available on https://github.com/didid5/MVTL-LSR.

Quantum Computational Techniques for Prediction of Cognitive State of Human Mind from EEG Signals

The utilization of quantum states for the representation of information and the advances in machine learning is considered as an efficient way of modeling the working of complex systems.The states of mind or judgment outcomes are highly complex phenomena that happen inside the human body.Decoding these states is significant for improving the quality of technology and providing an impetus to scientific research aimed at understanding the functioning of the human mind.One of the key advantages of quantum wave-functions over conventional classical models is the existence of configurable hidden variables,which provide more data density due to its exponential state-space growth.These hidden variables correspond to the amplitudes of each probable state of the system and allow for the modeling of various intricate aspects of measurable and observable physical quantities.This makes the quantum wave-functions powerful and felicitous to model cognitive states of the human mind,as it inherits the ability to efficiently couple the current context with past experiences temporally and spatially to approach an appropriate future cognitive state.This paper implements and compares some techniques like Variational Quantum Classifiers(VQC),quantum annealing classifiers,and hybrid quantum-classical neural networks,to harness the power of quantum computing for processing cognitive states of the mind by making use of EEG data.It also introduces a novel pipeline by logically combining some of the aforementioned techniques,to predict future cognitive responses.The preliminary results of these approaches are presented and are very encouraging with upto 61.53%validation accuracy.

Comparative Analysis of EEG Signals Based on Complexity Measure

The aim of this study is to identify the functions and states of the brains according to the values of the complexity measure of the EEG signals. The EEG signals of 30 normal samples and 30 patient samples are collected. Based on the preprocessing for the raw data, a computational program for complexity measure is compiled and the complexity measures of all samples are calculated. The mean value and standard error of complexity measure of control group is as 0.33 and 0.10, and the normal group is as 0.53 and 0.08. When the confidence degree is 0.05, the confidence interval of the normal population mean of complexity measures for the control group is (0.2871,0.3652), and (0.4944,0.5552) for the normal group. The statistic results show that the normal samples and patient samples can be clearly distinguished by the value of measures. In clinical medicine, the results can be used to be a reference to evaluate the function or state, to diagnose disease, to monitor the rehabilitation progress of the brain.

注意力残差网络结合LSTM的EEG情绪识别研究

基于脑电信号的情感识别已成为情感计算和人机交互领域的一个重要挑战。由于脑电信号中具有时间、空间、频率维度信息,采用结合注意力残差网络与长短时记忆网络的混合网络模型(ECA-ResNet-LSTM)对脑电信号进行特征提取与识别。首先,提取时域分段后脑电信号不同频带微分熵特征,将从不同通道中提取出的微分熵特征转化为四维特征矩阵;然后通过注意力残差网络(ECA-ResNet)提取脑电信号中空间与频率信息,并引入注意力机制重新分配更相关频带信息的权重,长短时记忆网络(LSTM)从ECA-ResNet的输出中提取时间相关信息。实验结果表明:在DEAP数据集唤醒维和效价维二分类准确率分别达到了97.15%和96.13%,唤醒-效价维四分类准确率达到了95.96%,SEED数据集积极-中性-消极三分类准确率达到96.64%,相比现有主流情感识别模型取得了显著提升。

Adaptive Signal Enhancement Unit for EEG Analysis in Remote Patient Care Monitoring Systems

In this paper we propose an efcient process of physiological artifact elimination methodology from brain waves(BW),which are also commonly known as electroencephalogram(EEG)signal.In a clinical environment during the acquisition of BW several artifacts contaminates the actual BW component.This leads to inaccurate and ambiguous diagnosis.As the statistical nature of the EEG signal is more non-stationery,adaptive ltering is the more promising method for the process of artifact elimination.In clinical conditions,the conventional adaptive techniques require many numbers of computational operations and leads to data samples overlapping and instability of the algorithm used.This causes delay in diagnosis and decision making.To overcome this problem in our work we propose to set a threshold value to diminish the problem of round off error.The resultant adaptive algorithm based on this strategy is Non-linear Least mean square(NL2MS)algorithm.Again,to improve this algorithm in terms of ltering capability we perform data normalization,using this algorithm several hybrid versions are developed to improve ltering and reduce computational operations.Using the method,a new signal enhancement unit(SEU)is realized and performance of various hybrid versions of algorithms examined using real EEG signals recorded from the subject.The ability of the proposed schemes is measured in terms of convergence,enhancement and multiplications required.Among various SEUs,the MCN2L 2MS algorithm achieves 14.6734,12.8732,10.9257,15.7790 dB during the artifact removal of RA,EMG,CSA and EBA components with only two multiplications.Hence,this algorithm seems to be better candidate for artifact elimination.

Editorial commentary on special issue of Advances in EEG Signal Processing and Machine Learning for Epileptic Seizure Detection and Prediction

This special issue of The Journal of Biomedical Research features novel studies on epileptic seizure detection and prediction based on advanced EEG signal processing and machine learning algorithms.The articles selected present important findings including new experimental results and theoretical studies.

Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals

The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical signals.Therefore,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography(EEG)Biomedical Signals,named OSAE-SSCEEG technique.The major intention of the OSAE-SSCEEG technique is tofind the sleep stage disorders using the EEG biomedical signals.The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach.Moreover,the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization(SAE-SR)with softmax(SM)approach.Finally,the parameter optimization of the SAE-SR technique is carried out by the use of Coyote Optimization Algorithm(COA)and it leads to boosted classification efficiency.In order to ensure the enhanced performance of the OSAE-SSCEEG technique,a wide ranging simulation analysis is performed and the obtained results demonstrate the betterment of the OSAE-SSCEEG tech-nique over the recent methods.

Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements;perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm.This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease(PD).To play out this handling method,electroencepha-logram(EEG)signals are gained while the subject is performing different wrist and elbow movements.Then,the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass filter.Then,feature extraction is carried out using Fast Fourier Transform(FFT).Subse-quently,the extraction process is done by Daubechies(db4)and Haar wavelet(db1)in MATLAB and classified using the Levenberg-Marquardt Algorithm.The results of the frequency changes that occurred during various wrist move-ments in the parietal region are compared with the frequency changes that occurred in frontal EEG signals.This proposed algorithm also uses the deep learn-ing pattern analysis network to evaluate the matching sequence for each action that takes place.Maximum accuracy of 97.2%and maximum error range of 0.6684%are achieved during the analysis.Results of this research confirm that the Levenberg-Marquardt algorithm,along with the newly developed deep learn-ing hybrid PatternNet,provides a more accurate range of frequency changes than any other classifier used in previous works of literature.Based on the analysis,the peak-to-peak value is used to define the threshold for the prototype arm,which performs all the intended degrees of freedom(DOF),verifying the results.These results would aid the specialists in their decision-making by facilitating the ana-lysis and interpretation of brain signals in the field of neuroscience,specifically in tremor analysis in PD.

Research on time-frequency cross mutual of motor imagination data based on multichannel EEG signal

At present,multi-channel electroencephalogram(EEG)signal acquisition equipment is used to collect motor imagery EEG data,and there is a problem with selecting multiple acquisition channels.Choosing too many channels will result in a large amount of calculation.Components irrelevant to the task will interfere with the required features,which is not conducive to the real-time processing of EEG data.Using too few channels will result in the loss of useful information and low robustness.A method of selecting data channels for motion imagination is proposed based on the time-frequency cross mutual information(TFCMI).This method determines the required data channels in a targeted manner,uses the common spatial pattern mode for feature extraction,and uses support vector ma-chine(SVM)for feature classification.An experiment is designed to collect motor imagery EEG da-ta with four experimenters and adds brain-computer interface(BCI)Competition IV public motor imagery experimental data to verify the method.The data demonstrates that compared with the meth-od of selecting too many or too few data channels,the time-frequency cross mutual information meth-od using motor imagery can improve the recognition accuracy and reduce the amount of calculation.

Prediction of Epileptic EEG Signal Based on SECNN-LSTM

Brain-Computer Interface(BCI)technology is a way for humans to explore the mysteries of the brain and has applications in many areas of real life.People use this technology to capture brain waves and analyze the electroencephalograph(EEG)signal for feature extraction.Take the medical field as an example,epilepsy disease is threatening human health every moment.We propose a convolutional neural network SECNN-LSTM framework based on the attention mechanism can automatically perform feature extraction and analysis on the collected EEG signals of patients to complete the prediction of epilepsy diseases,overcoming the problem that the disease requires long time EEG monitoring and analysis by manual,which is a large workload and relatively subjective,and improving the prediction accuracy of epilepsy diseases by adding the attention mechanism module.Through experimental tests,the algorithm of SECNN-LSTM can effectively predict the EEG signal of epilepsy disease,and the correct recognition rate is improved.The experiment has some reference value for the subsequent research of EEG signals in other fields in deep learning.

基于3D特征融合与轻量化CNN的情绪EEG识别

情绪变化可引起头皮脑电信号的改变,基于脑电信号的情绪识别是近年来情绪研究的一个重要方向。为此,提出一种基于3D特征融合与轻量化卷积神经网络的情绪EEG识别方法,使用2 s窗口的3D特征图作为输入,并根据效价和唤醒提供情绪状态作为输出。在DEAP公开数据集上对所提方法进行受试者依赖实验,结果表明情绪识别性能评估效价和唤醒识别准确率分别为(97.08±0.32)%和(96.78±0.34)%。所提方法具有较高的情绪识别准确度和较低的计算复杂度,适用于实际场景中的情绪识别。

基于LabVIEW的EEG信号采集与处理系统设计

针对Emotiv Epoc采集的脑电信号,提出了一种基于LabVIEW的EEG信号的处理方法。应用LabVIEW软件平台,对采集的信号进行解析获取EEG信号,捕捉受试者的当前状态,同时对解析出的EEG数据进行保存与读取。首先利用傅里叶变换进行分析,得出在某段时间范围的频域信息,然后在此基础上进行小波分析,捕捉某一通道的某一信号出现的时间,结合这2种方法,更好地分析EEG信号。通过大量实验测试,提出的基于LabVIEW的EEG信号处理方法,能筛选出识别率较高的信号,从而对基于脑电控制的研究提供了一种有效途径。

一种新的基于小波包分解的EEG特征抽取与识别方法研究

为了提高脑思维任务分类精度,提出一种新的脑电特征抽取与识别方法.首先进行小波包分解,然后结合能反映脑电信号在时域与频域上的能量分布特征的小波包熵概念,从小波包库中选择最优小波包基,对各个最优基所对应的小波系数求取统计特性,然后根据不同脑思维任务下左右半脑各导联间的差异性对各个导联对求取不对称率构成分类特征向量,最后利用SVM分类器对其进行分类.实验结果表明：相对于一般的小波包分解,最优小波包基和自回归特征抽取方法,该方法对5类不同脑思维任务的所有10种不同组合任务对的平均分类预测精度可以达到95.41%~99.65%.

EEG相干分析在语言理解研究中的应用

EEG相干反映了EEG信号在各个脑区之间的信息传递,可以揭示各种认知加工过程中不同功能网络的协同工作方式。首先介绍了这一方法的基本原理,然后从词语和句子两个水平上阐述了EEG相干分析在语言理解研究中的应用。在词语水平上涉及一般认知加工过程、语法加工和语义加工三个方面;在句子水平上,从句子的语义整合角度进行了介绍。最后指出了这一方法的优缺点,并对今后的研究方向进行了展望。