Comparison of ICA and WT with S-transform based method for removal of ocular artifact from EEG signals

Ocular artifacts are most unwanted disturbance in electroencephalograph (EEG) signals. These are characterized by high amplitude but have overlap-ping frequency band with the useful signal. Hence, it is difficult to remove the ocular artifacts by traditional filtering methods. This paper proposes a new approach of artifact removal using S-transform (ST). It provides an instantaneous time-frequency repre-sentation of a time-varying signal and generates high magnitude S-coefficients at the instances of abrupt changes in the signal. A threshold function has been defined in S-domain to detect the artifact zone in the signal. The artifact has been attenuated by a suitable multiplying factor. The major advantage of ST-fil- tering is that the artifacts may be removed within a narrow time-window, while preserving the frequency information at all other time points. It also preserves the absolutely referenced phase information of the signal after the removal of artifacts. Finally, a com-parative study with wavelet transform (WT) and in-dependent component analysis (ICA) demonstrates the effectiveness of the proposed approach.

THE EFFECT OF ACUPUNCTURING ACUPOINTS ON THE CHANGE OF ELECTROENCEPHALOGRAM (EEG) IN ENDOTOXIC SHOCKED RATS

In present work,EEG and BP were used as the indexes to observe the relationbetween the change of EEG and the change of BP in the endotoxic shocked rats。At maintainingshock for 1 hr,dysrhythmia of EEG appeared in 38/46 cases.Simultaneously,there was a markeddrop in Bp,P【0.05.Following the shocked time prolonged,dysrhythmia was getting severe。AfterEA”Rengzhong"(n=14)or“Zusanli”(n=12),BP was significantly increased(P【0.05),anddysrhythmia of EEG showed clear improvement in most of the rats。There was a close relation be-tween the changes of EEG and BP,the change of EEG had a direct bearing on the change of BP.

Discrimination of Motor Imagery Patterns by Electroencephalogram Phase Synchronization Combined With Frequency Band Energy

Central nerve signal evoked by thoughts can be directly used to control a robot or prosthetic devices without the involvement of the peripheral nerve and muscles.This is a new strategy of human-computer interaction.A method of electroencephalogram(EEG) phase synchronization combined with band energy was proposed to construct a feature vector for pattern recognition of brain-computer interaction based on EEG induced by motor imagery in this paper,rhythm and beta rhythm were first extracted from EEG by band pass filter and then the frequency band energy was calculated by the sliding time window;the instantaneous phase values were obtained using Hilbert transform and then the phase synchronization feature was calculated by the phase locking value(PLV) and the best time interval for extracting the phase synchronization feature was searched by the distribution of the PLV value in the time domain.Finally,discrimination of motor imagery patterns was performed by the support vector machine(SVM).The results showed that the phase synchronization feature more effective in4s-7s and the correct classification rate was 91.4%.Compared with the results achieved by a single EEG feature related to motor imagery,the correct classification rate was improved by 3.5 and4.3 percentage points by combining phase synchronization with band energy.These indicate that the proposed method is effective and it is expected that the study provides a way to improve the performance of the online real-time brain-computer interaction control system based on EEG related to motor imagery.

Inhibitory effects of jujuboside A on EEG and hippocampal glutamate in hyperactive rat

In this study, the inhibitory effect of jujuboside A (JuA) on a penicillin sodium (Na-PCN) induced hyperactivity model was investigated. Cortical EEG (electroencephalogram) and the concentration of hippocampal Glutamate (Glu) were monitored simultaneously in vivo as indicators of rat’s excitatory state. Power spectral density (PSD) and gravity frequency of PSD were calculated. JuA (0.05 g/L and 0.1 g/L) inhibited the EEG excitation effect caused by Na-PCN by increasing the power of δ1 and δ2 bands (P<0.01 vs model) and lowering the gravity frequency of PSD (P<0.01 vs model). JuA also remarkably reduced the Glu elevation induced by Na-PCN (P<0.05 vs model). Diazepam also depressed Glu concentration and lowered the gravity frequency, but it showed a different EEG pattern in increased β2-activity (P<0.01 vs model). EEG excitation caused by Na-PCN correlated with Glu elevation during the first hour. Neurophysiological inhibitory effects of JuA and diazepam were more persistent than their Glu inhibitoty effects.

Evaluation of EEG β2/θ -ratio and channel locations in measuring anesthesia depth

In this paper, the ratio of powers in the frequency bands of β2 and θ waves in EEG signals (termed as the β2/θ -ratio) was introduced as a potential enhancement in measuring anesthesia depth. The β2/θ -ratio was compared to the relative β-ratio which had been commercially used in the BIS monitor. Sensitivity and reliability of the β2/θ -ratio and EEG measurement locations were analyzed for their effectiveness in measuring anesthesia depth during different stages of propofol induced anesthesia (awake, induction, maintenance, and emergence). The analysis indicated that 1) the relative β -ratio and β2/θ-ratio derived from the prefrontal, frontal, and the central cortex EEG signals were of substantial sensitivity for capturing anesthesia depth changes. 2) Certain channel positions in the frontal part of the cortex, such as , had the combined benefits of substantial sensitivity and noise resistance. 3) The β2/θ-ratio captured the initial excitation, while the relative β -ratio did not. 4) In the maintenance and emergence stages, the β2/θ -ratio showed improved reliability. Implications: The ratio of powers in EEG frequency bands and derived from the frontal cortex EEG channels has combined benefits of substantial sensitivity and noise resistance in measuring anesthesia depth.

基于有效注意力和GAN结合的脑卒中EEG增强算法

在基于脑电的卒中分类诊断任务中,以卷积神经网络为基础的深度模型得到广泛应用,但由于卒中类别病患样本数量少,导致数据集类别不平衡,降低了分类精度。现有的少数类数据增强方法大多采用生成对抗网络(GAN),生成效果一般,虽然可通过引入缩放点乘注意力改善样本生成质量,但存储及运算代价往往较大。针对此问题,构建一种基于线性有效注意力的渐进式数据增强算法LESA-CGAN。首先,算法采用双层自编码条件生成对抗网络架构,分别进行脑电标签特征提取及脑电样本生成,并使生成过程逐层精细化;其次,通过在编码部分引入线性有效自注意力(LESA)模块,加强脑电的标签隐层特征提取,并降低网络整体的运算复杂度。消融与对比实验结果表明,在合理的编码层数与生成数据比例下,LESA-CGAN与其他基准方法相比计算资源占用较少,且在样本生成质量指标上实现了10%的性能提升,各频段生成的脑电特征样本均更加自然,同时将病患分类的准确率和敏感度提高到了98.85%和98.79%。

15 Years of Evolution of Non-Invasive EEG-Based Methods for Restoring Hand &Arm Function with Motor Neuroprosthetics in Individuals with High Spinal Cord Injury: A Review of Graz BCI Research

Patients who suffer from a high spinal cord injury have severe motor disabilities in the lower as well as in the upper extremities. Thus they rely on the help of other people in everyday life. Restoring the function of the upper limbs, especially the grasp function can help them to gain some independence. Using EEG-based neuroprosthetics is a way to help tetraplegic people restore different grasp types as well as moving the arm and the elbow. In this work an overview of non-invasive EEG-based methods for restoring the hand and arm function with the use of neuroprosthetics in individuals with high spinal cord injury is given. Since the Graz BCI group is leading in this area of non-invasive research mainly, the work of this group is represented.

基于TOF+CCD相机组合标定的EEG电极定位

为快速精准定位脑电图的源信号,设计一套新的摄影测量系统。该系统通过CCD相机与TOF相机结合来定位源信号,将深度相机获取的三维点云数据替代深度图像进行标定,从而提高相机标定精度。利用三维扫描仪获取实际数据,将重建出的脑部电极的结果数据与实际数据进行比较。实验结果表明,该系统测量平均距离误差为3. 26 mm,与改进前的方法(平均距离误差6. 13 mm)相比,精度有较大提高。

Detrended Fluctuation Analysis of the Human EEG during Listening to Emotional Music

A nonlinear method named detrended fluctuation analysis （DFA） was utilized to investigate the scaling behavior of the human electroencephalogram （EEG） in three emotional music conditions （fear, happiness, sadness） and a rest condition （eyes-closed）. The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponents β1 and β2 which represented the complexity of higher and lower frequency activity besides α band respectively. As the emotional intensity decreased the value of β1 increased and the value of β2 decreased. The change of β1 was weakly correlated with the ＇approach-withdrawal＇ model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition. The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion.

Evaluation of Product Placement with Attention on Eye-Tracking and EEG

Advertising evaluation can not only evaluate the subconscious behavior of consumers and help consumers to avoid irrational consumption,but also improve the utilization of advertising resources.To deal with this topic,electroencephalogram(EEG)data and eye movement data from18 subjects is collected.The power spectra of beta band and theta band are used to calculate the EEG attention index,while the eye attention index is calculated by the statistics at fixation on the interesting area.Therefore the fusion attention index is obtained by fusing the EEG signal and eye movement data in the feature level.The results indicate that the memory of the advertisements is synced up with the attention of the products.And the fusion attention index has a higher significant difference than the other two indexes.

IMAGING OF EEG BY SPHERICAL HARMONIC ANALYSIS

A new three dimensional imaging method for electroencephalogram(EEG) is suggested. It consists of the solution of an inward spherical harmonic continuation. The principle of the new method is introduced in Section II, then a few numerical simulation tests are shown and the feasibility of the new method is confirmed by the simulation results.

The Role of EEG in the Diagnosis and Management of Patients with Sleep Disorders

Sleep disorders affect an individual’s ability to sleep well on a regular and natural basis. Inadequate sleep can have adverse outcomes for health and safety. Electroencephalogram (EEG) has been presented as an authentic indicator to monitor brain activities. In this review paper, different procedures of EEG tests for recording and monitoring brain activity during sleep such as the EEG electrodes system and the Dreem headband (DH) have been introduced. Also, the processes of recording and analyzing the data have been discussed and compared with each other. The results of various stages of sleep from EEG tests help sleep specialists diagnose or evaluate sleep disorders accurately and choose appropriate strategies. Sleep disorder management is integral to provide patients with a safe sleeping environment.

Emotion Judgment System by EEG Based on Concept Base of EEG Features

This paper proposes an emotion judgment system by using an electroencephalogram(EEG)feature concept base with premise of noises included.This method references the word concept association system,which associates one word with other plural words and decides the relationship between several words.In this proposed emotion judgment system,the source EEG is input and 42 EEG features are constructed by EEG data;the data are then calculated by spectrum analysis and normalization.All 2945 EEG data of 4 emotions in the EEG data emotion knowledge base are calculated by the degree of association for getting the nearest EEG data from the EEG feature concept base constructed by 2844 concepts.From the experiment,the accuracy of the proposed system was 55.9%,which was higher than the support vector machine(SVM)method.As this result,the chain structured feature of the EEG feature concept base and the efficiency by the calculation of degree of association for EEG data help reduce the influence of the noise.

Revealing True and False Brain States Based on Wavelet Analysis of Electroencephalogram

Statement of the Problem: As you know, there exist two different states in the brain’s mental activity: true and false. In recent years, a progressive method of wavelet transformation of the electroencephalogram (EEG) has been developed, which enabled us to establish the fundamental possibility of direct objective registration of the human brain’s mental activity. Earlier, we created an experimental model and software for recognizing true and false mental responses of a person based on the EEG wavelet transformation and described it in the article. The developed experimental model and information software made it possible to compare the two mental states of brain activity by electroencephalographic indicators, one of which is false and the other is true. The goal is to develop a fundamentally new information technology for recognizing true and false states in the brain’s mental activity based on the wavelet transformation of the electroencephalogram. Results: It was revealed that the true and false states of the brain can be distinguished using the method of continuous wavelet transformation and calculation of the EEG wavelet energy. It is shown that the main differences between true and false mental responses are observed in the delta and alpha ranges of the EEG. In the EEG delta rhythm, the wavelet energy is reliably higher in case of a false answer compared to a true one. In the EEG alpha rhythm, the wavelet energy is significantly higher with a true answer than a false one. Practical significance of the research: The data obtained open up the fundamental possibility of identifying true and false mental states of the brain on the basis of continuous wavelet transformation and calculation of the EEG wavelet energy.

抑郁症EEG诊断的类脑学习模型

抑郁症是一种全球性精神疾病,传统诊断方法主要依靠量表与医生的主观评估,无法有效识别症状,甚至存在误诊的风险。基于生理信号的深度学习辅助诊断有望改善传统缺乏生理学依据的方法。然而,传统深度学习方法依赖巨大算力,且大多是端到端的网络学习。这些学习方法也缺乏生理可解释性,限制了辅助诊断临床应用。提出一种用于抑郁症脑电图(electroencephalogram,EEG)诊断的类脑学习模型,在功能层面,构建脉冲神经网络对抑郁症与健康个体进行分类,精度超过97.5%,相比深度卷积方法,脉冲方法降低了能耗;在结构层面,利用复杂网络建立脑连接的空间拓扑并分析其图特征,找出了抑郁症个体潜在的脑功能连接异常机制。

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.

Correlation between level of serum prolactin and epileptic discharges of electroencephalogram from 24 to 36 hours after epileptic onset

BACKGROUND： Researchers discovered that serum prolactin could rise following an epileptic seizure. The prolactin level might reach three times more than basic level within 30 minutes and decrease to the normal value 2 hours after the seizure occurred. The mechanism might result in an increase of serum prolactin concentrations with the activation of the hypothalamic-pituitary axis. OBJECTIVE：To probe into the correlation between changes of serum prolactin and incidence of epileptic discharges of electroencephalogram （EEG） at 24-36 hours after epileptic onset of patients with secondary epilepsy. DESIGN ： Clinical observational study SEI-FING： Department of Neurology, First Hospital affiliated to Soochow University PARTICIPANTS： A total of 21 patients with secondary epilepsy were selected from the Department of Neurological Emergency or Hospital Room of the First Hospital affiliated to Soochow University from November 2005 to April 2006. There were 14 males and 7 females aged from 25 to 72 years. All patients met International League Anti-epileptic （ILAE） criteria in 1981 for secondary generalized tonic clonic seizure through CT or MRI and previous EEG. All patients were consent. Primary diseases included cerebral trauma （3 cases）, tumor （2 cases）, stroke （7 cases） and intracranial infeion （9 cases）. METHODS ： Venous blood of all patients was collected at 24-36 hours after epileptic onset. Serum prolactin kit （Beckman Coulter, Inc in USA） was used to measure value of serum prolactin according to kit instruction. Then, value of serum prolactin was compared with the normal value （male： 2.64-13.13 mg/L; female： 3.34- 26.72 mg/L）; meanwhile, EEG equipment （American Nicolet Incorporation） was used in this study. MAIN OUTCOME MEASURES ： ① Abnormal rate of serum prolactin of patients with secondary epilepsy; ②Comparison between normal and abnormal level of serum prolactin and incidence of EEG epileptic discharge of patients with secondary epilepsy. RESULTS：All 21 patients with secondary epilepsy were involved in the final analysis. ① Results of serum prolactin level： Among 21 patients with of secondary epilepsy, 10 of them had normal serum prolactin and 11 had abnormal one, and the abnormal rate was 52% （11/21）. ② Detecting results of EEG： EEG results showed that 6 cases were normal and 15 were abnormal, and the abnormal rate was 71% （15/21）. The symptoms were sharp wave, spike wave or sharp slow wave, spike slow wave of epileptic discharges in 8 cases, which was accounted for 38%. ③ Correlation between abnormality of serum prolactin and EEG epileptic wave： Eleven cases had abnormal serum prolactin, and the incidence was 64% （7/11）, which was higher of epileptic wave than that of non-epileptic wave [36% （4/11）, P 〈 0.05]; however, 10 cases had normal serum prolactin, and the incidence was 10% （1/10）. Epileptic wave was lower than non-epileptic wave [90% （9/10）, P 〈 0.01]. CONCLUSION ： The level of serum prolactin of patients with secondary epilepsy is abnormally increased at 24- 36 hours after epileptic onset; in addition, incidence of epileptic discharge is also increased remarkably.

Design of an EEG Preamplifier for Brain-Computer Interface

As a non-invasive neurophysiologieal index for brain-computer interface （BCI）, electroencephalogram （EEG） attracts much attention at present. In order to have a portable BCI, a simple and efficient pre-amplifier is crucial in practice. In this work, a preamplifier based on the characteristics of EEG signals is designed, which consists of a highly symmetrical input stage, low-pass filter, 50 Hz notch filter and a post amplifier. A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment. The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.

The Spatial Equivalence Between Wavelet Decomposition and Phase Space Embedding of EEG

Using both the wavelet decomposition and the phase space embedding, the phase trajectories of electroencephalogram (EEG) is described. It is illustrated based on the present work,that is,the wavelet decomposition of EEG is essentially a projection of EEG chaotic attractor onto the wavelet space opened by wavelet filter vectors, which is in correspondence with the phase space embedding of the same EEG. In other words, wavelet decomposition and phase space embedding are equivalent in methodology. Our experimental results show that in both the wavelet space and the embedded space the structure of phase trajectory of EEG is similar to each other. These results demonstrate that wavelet decomposition is effective on characterizing EEG time series.

The Detection of Hidden Periodicities in EEG

A novel method for detecting the hidden periodicities in EEG is proposed. By using a width-varying window in the time domain, the structure function of EEG time series is defined. It is found that the minima of the structure function, within a finite window width, can be found regularly, which indicate that there are some certain periodicities associated with EEG time series. Based on the structure function, a further quadratic structure function of EEG time series is defined. By quadratic structure function, it can be seen that the periodicities of EEG become more obvious, moreover, the period of EEG can be determined accurately. These results will be meaningful for studying the neuron activity inside the human brain.