COMPLEXITY ANALYSIS OF DENSE ARRAY EEG SIGNAL REVEALS SEX DIFFERENCE

This article deals with the complexity aspect of the recorded electroencephalogram (EEG) signal from male and female subjects. The analysis follows direct application of time series measures of global linear complexity and characterization of the embedded complexity in the signals using the nonlinear statistic of approximate entropy. The study reveals significant differences in complexity between the two sex groups during passive, no-task conditions, whereas no apparent variation exists during a mental task state. The detection of subtle changes as well as the ease in presenting a global picture of the complexity variation on the human cortical surface makes the nonlinear statistic a better marker of system complexity.     

Complexity analysis of dense array EEG signal reveals sex difference

This article deals with the complexity aspects of the recorded electroencephalogram (EEG) signal from male and female subjects. The analysis follows direct application of time series measures of global linear complexity and characterization of the embedded complexity in the signals using the nonlinear statistic of approximate entropy. The study reveals significant differences in complexity between the two sex groups during passive, no-task conditions, whereas no apparent variation exists during a mental task state. The detection of subtle changes as well as the ease in presenting a global picture of the complexity variation on the human cortical surface makes the nonlinear statistic a better marker of system complexity.     

Application Research of Music Therapy in Mental Health of Special Children

A healthy psychological state is the premise for children to carry out various activities. Previous surveys have shown that children with special needs are affected by their own obstacles and are more prone to psychological problems such as sensitivity, low self-esteem, and impulsiveness. Therefore, it is necessary to provide more systematic mental health education support for special children. Mental health education programs are an efficient form of maintaining children’s mental health. However, in the field of special education, the number of mental health education courses developed according to the physical and mental characteristics and developmental needs of special children is relatively small, and there are many difficulties in the implementation process. Autism disorder (ASD) is a kind of pervasive developmental dysfunction that is relatively common and representative in clinical practice. In recent years, the number of autistic children has continued to surge, and has gradually expanded from a family problem to a serious social problem. At present, the evaluation of the effect of autism intervention mainly relies on various behavioral scales, which are subjective to a certain extent. At the same time, due to the unclear pathogenesis of autism, the treatment of autism cannot be predicated on the right medicine, and can only be intervened in various ways. The purpose of this paper is to explore the difference between the EEG signals of autistic children and typically developing control (TD) children through the analysis method of EEG signals, and based on the analysis of EEG signals from an objective point of view, to study whether the music therapy method of Chinese Zither playing training can effectively Improving the brain functional status of children with autism yields positive therapeutic outcomes. The experimental results show that the complexity of brain electrical signals of ASD children is much lower than that of TD children, and there is a significant difference in the brain functional state between the two. The music therapy method based on Chinese zither playing training can improve the brain function of autistic patients, and there is a positive therapeutic effect. And with the extension of the training period, the effect may be more significant. Chinese zither playing training can provide a new direction for the intervention of autism.     

Abnormal EEG complexity in patients with schizophrenia and depression.

OBJECTIVE: Schizophrenics are usually unable to perform well on cognitive tasks due to disturbances in cortical information processing that are observable as abnormalities in electroencephalogram (EEG) signals. However, whether such cortical disturbances can be assessed by quantitative EEG analysis remains unclear. The purpose of this study was to characterize EEG disturbances, using the Lempel-Ziv complexity (LZC), in the subjects with schizophrenia at rest or while performing mental arithmetic tasks. The results were compared to those from the subjects with depression and with healthy controls. METHODS: The subjects included 62 schizophrenia patients, 48 depression patients and 26 age-matched healthy controls. EEG was recorded under two conditions: (i) resting with eyes closed, and (ii) a mentally active condition wherein the subjects were asked to subtract 7 from 100 iteratively with their eyes closed. EEG signals were analyzed by LZC and conventional spectral methods. RESULTS: In all the groups, LZC of EEG decreased during the mental arithmetic compared with those under the resting conditions. Both the schizophrenia and the depression groups had a higher LZC (p<0.05) than the controls. Also, the schizophrenia group had a lower LZC (p<0.05) than the depression group during the mental arithmetic task as well as during the resting state. Significant differences in LZC, at some symmetrically located loci (FP1/FP2, F7/F8), between the two hemispheres were found in all the patient groups only during the arithmetic task. CONCLUSIONS: Compared with conventional spectral analysis, LZC was more sensitive to both the power spectrum and the temporal amplitude distribution. LZC was associated with the ability to attend to the task and adapt the information processing system to the cognitive challenge. Thus, it would be useful in studying the disturbances in the cortical information processing patients with depression or schizophrenia. SIGNIFICANCE: LZC of EEG is associated with mental activity. Thus, LZC analysis can be an important tool in understanding the pathophysiology of schizophrenia and depression in future studies.     

不同思维状态下脑电近似熵的变化规律*

背景：近似熵是一种描述信号复杂性和规律性的非线性动力学方法,只需较少数据就能度量信号的复杂性。 目的：探讨不同思维状态下脑电近似熵的变化规律,以及近似熵在认知过程中的作用。 方法：用近似熵对20名健康成年人在安静闭眼、安静睁眼、闭眼记忆、闭眼心算和图片识别 5 种状态下的脑电数据进行分析。 结果与结论：近似熵值在闭眼计算和闭眼记忆思维状态高于安静闭眼状态,在图片识别状态下高于安静睁眼状态(P < 0.01);近似熵在安静闭眼和安静睁眼状态下各导联处于较低水平,在闭眼心算和闭眼记忆思维状态下各导联处明显增加。说明不同思维状态和不同导联部位对近似熵均有影响;近似熵在认知作业过程下较安静状态增高,并且不同思维状态下大脑功能活动的复杂性不同。因此脑电近似熵分析适用于认知过程脑功能活动变化规律研究,有助于了解大脑的工作机制。 关键词：近似熵;脑电;认知功能;思维状态;数字化医学     

Chaos or noise in EEG signals; dependence on state and brain site.

EEG signals have been considered to result either from random processes or to be generated by non-linear dynamic systems exhibiting chaotic behaviour. In the latter case, the system may behave as a deterministic chaotic attractor. The complexity of the attractor can be characterized by the correlation dimension that can be computed from one signal generated by the system. A new procedure was developed and applied in order to test whether the correlation dimension, calculated from an EEG epoch, may correspond to a chaotic attractor or to a random process. This procedure was applied to EEG signals recorded from different sites of the limbic cortex of the rat during different states: wakeful rest, locomotion and in the course of an epileptic seizure induced by kindling. The signals recorded during the first two states had high dimensions and could not be distinguished from random noise. However, during an epileptic seizure the correlation dimension became low (between 2 and 4) indicating that in this state the networks behave as chaotic systems. A low correlation dimension appeared at different times and brain sites during an epileptic seizure. These results show that the computation of the correlation dimension may be useful in order to obtain insight into the dynamics of the propagation of an epileptic seizure in the brain.     

Layer IV of the primary somatosensory cortex has the highest complexity under anesthesia and cortical complexity is modulated by specific thalamic inputs

The system complexity, as calculated from correlation dimension, embedded in each layer and its modulation by specific inputs and general excitatory state are not yet known. The aims of present study were to estimate the system complexity across the cortical layers by analyzing intracortical EEG signals using a nonlinear analytical method, and to identify how layer-related complexity varies with the alteration of thalamic input and brain state. Male Sprague-Dawley rats were anesthetized under l% halothane. Sixteen channels of evoked or spontaneous EEG signals were recorded simultaneously across the six cortical layers in the somatosensory cortex with a single Michigan probe. The system complexity was assessed by computing correlation dimension, D(2), based on the Nonlinear Time Series Analysis data analysis program. Cortical layer IV exhibited a D(2) value, 3.24, that was significantly higher than that of the other cortical layers. The D(2) values in layers IV and II/III were significantly reduced after reversible deactivation of the ventral posterior lateral thalamic nucleus. D(2) decreased with increases in administered halothane concentration from 0.75% to 2.0%, particularly in layer IV. The present findings suggest that cortical layer IV maintains a higher complexity than the other layers and that the complexity of the mid-cortical layers is subject to regulation from specific thalamic inputs and more sensitive to changes in the general state of brain excitation.     

Approximate entropy in the electroencephalogram during wake and sleep.

Entropy measurement can discriminate among complex systems, including deterministic, stochastic and composite systems. We evaluated the changes of approximate entropy (ApEn) in signals of the electroencephalogram (EEG) during sleep. EEG signals were recorded from eight healthy volunteers during nightly sleep. We estimated the values of ApEn in EEG signals in each sleep stage. The ApEn values for EEG signals (mean +/- SD) were 0.896 +/- 0.264 during eyes-closed waking state, 0.738 +/- 0.089 during Stage I, 0.615 +/- 0.107 during Stage II, 0.487 +/- 0.101 during Stage II, 0.397 +/- 0.078 during Stage IV and 0.789 +/- 0.182 during REM sleep. The ApEn values were found to differ with statistical significance among the six different stages of consciousness (ANOVA, p<0.001). ApEn of EEG was statistically significantly lower during Stage IV and higher during wake and REM sleep. We conclude that ApEn measurement can be useful to estimate sleep stages and the complexity in brain activity.     

Event-related alpha oscillations in task processing.

OBJECTIVES: Recent findings substantiate the view that electroencephalographic (EEG) alpha rhythm (7-13 Hz) is functionally involved in information processing. However, the association of alpha rhythms with cognitive brain processes is less well understood because both augmentation and suppression of alpha oscillations have been observed to accompany task performance. The present study evaluates the effect of task processing on event-related alpha oscillations at the level of single-sweep analysis. METHODS: EEG was recorded from Fz, Cz and Pz electrodes in 10 subjects participating in two experimental sessions, in which auditory stimuli with equal physical parameters were presented under different instructions (passive and task). Separate measurements of single-sweep amplitude and phase-locking were performed and statistically analyzed for consecutive time windows in the poststimulus epoch. RESULTS: Major results show that, during the cognitive task, the phase-locking of alpha oscillations at the frontal site is significantly increased for the time window of 500-1000 ms after stimulation. CONCLUSIONS: The involvement of enhanced and synchronized frontal alpha activity in higher brain processes is strongly emphasized.     

EEG-based motor imagery analysis using weighted wavelet transform features

In this study, an electroencephalogram (EEG) analysis system for single-trial classification of motor imagery (MI) data is proposed. Feature extraction in brain-computer interface (BCI) work is an important task that significantly affects the success of brain signal classification. The continuous wavelet transform (CWT) is applied together with Student's two-sample t-statistics for 2D time-scale feature extraction, where features are extracted from EEG signals recorded from subjects performing left and right MI. First, we utilize the CWT to construct a 2D time-scale feature, which yields a highly redundant representation of EEG signals in the time-frequency domain, from which we can obtain precise localization of event-related brain desynchronization and synchronization (ERD and ERS) components. We then weight the 2D time-scale feature with Student's two-sample t-statistics, representing a time-scale plot of discriminant information between left and right MI. These important characteristics, including precise localization and significant discriminative ability, substantially enhance the classification of mental tasks. Finally, a correlation coefficient is used to classify the MI data. Due to its simplicity, it will enable the performance of our proposed method to be clearly demonstrated. Compared to a conventional 2D time-frequency feature and three well-known time-frequency approaches, the experimental results show that the proposed method provides reliable 2D time-scale features for BCI classification.     

Detection of non-linearity in the EEG of schizophrenic patients.

OBJECTIVE: The aim of this study is to detect non-linearity in the EEG of schizophrenia with a modified method of surrogate data. We also want to identify if dimension complexity (correlation dimension using spatial embedding) could be used as a discriminating statistic to demonstrate non-linearity in the EEG. The difference between the attractor dimension of healthy subjects and schizophrenic subjects is expected to be interpreted as reflecting some mechanisms underlying brain wave by views of non-linear dynamics analysis may reflect mechanistic differences. METHODS: EEGs were recorded with 14 electrodes in 18 healthy male subjects (average age: 26.3; range: 20--35) and 18 male schizophrenic patients (average age: 30.6; range: 24--40) during a resting eye-closed state. Neither of two groups was taking medicines. All artificial epochs in the EEG records were rejected by an experienced doctor's visual inspection. RESULTS: Testing non-linearity with modified surrogate data, we showed that correlation dimension of EEG data of schizophrenia does refuse the null hypothesis that the data were resulted from a linear dynamic system. A decrease of dimension complexity was found in the EEG of schizophrenia compared with controls. We interpreted it as the result of the psychopath's dysfunction overall brain. The surrogating procedure results in a significant increase in D(s). CONCLUSIONS: Non-linearity of the EEG in schizophrenia was proven in our study. We think the correlation dimension with spatial embedding as a good discriminating statistic for testing such non-linearity. Moreover, schizophrenic patients' EEGs were compared with controls and a lower dimension complexity was found. The results of our study indicate the possibility of using the methods of non-linear time series analysis to identify the EEGs of schizophrenic patients.     

Neural network classification of autoregressive features from electroencephalogram signals for brain-computer interface design

In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.     

Effects of task difficulty on evoked gamma activity and ERPs in a visual discrimination task.

OBJECTIVE: The present study examined oscillatory brain activity of the EEG gamma band and event-related potentials (ERPs) with relation to the difficulty of a visual discrimination task. METHODS: Three tasks with identical stimulus material were performed by 9 healthy subjects. The tasks comprised a passive control task, and an easy and a hard visual discrimination task, requiring discrimination of the color of circles. EEG was recorded from 26 electrodes. A wavelet transform based on Morlet wavelets was employed for the analysis of gamma activity. RESULTS: Evoked EEG gamma activity was enhanced by both discrimination tasks as compared to the passive control task. Within the two discrimination tasks, the latency of the evoked gamma peak was delayed for the harder task. Higher amplitudes of the ERP components N170 and P300 were found in both discrimination tasks as compared to the passive task. The N2b, which showed a maximum activation at about 260 ms, was increased in the hard discrimination task as compared to the easy discrimination task. CONCLUSIONS: Our results indicate that early evoked gamma activity and N2b are related to the difficulty of visual discrimination processes. A delayed gamma activity in the hard task indicated a longer duration of stimulus processing, whereas the amplitude of the N2b directly indicates the level of task difficulty.     

磁刺激神门穴脑电信号的非线性动力学分析

背景：中医临床已经证明针刺或电刺神门穴可以改变神经活动或者治疗神经系统疾病。磁刺激技术是一种新型生物刺激技术,鉴于其刺激强度高且无痛无创,所以可以采用经颅磁刺激技术刺激穴位替代针刺或电刺穴位。 目的：应用磁刺激刺激人肢体神门穴,分析大脑不同的功能状态与脑电信号混沌程度的关系,初步验证应用磁刺激疗法治疗疾病的可行性。 设计、时间及地点：对照观察,于2007-01/2008-05在河北工业大学河北省电磁场与电器可靠性重点实验室完成。 对象：被试选择健康右利手男性4名,无实验经历,无既往神经、精神系统病史,实验期间被试保持闭眼静息。 方法：共进行2个实验。静息实验即测量被试安静状态下的脑电信号样本,磁刺激实验即采用经颅磁刺激刺激被试肢体神门穴,并测量脑电信号样本。用非线性动力学的方法对被试在2种状态下的脑电信号样本进行分析。 主要观察指标：安静与磁刺激状态下脑电信号非线性动力学参数。 结果：磁刺激状态下被试的非线性特征指标如关联维数、Lyapunov指数和样本熵均较安静状态低(P < 0.05)。以上3个指标越低,大脑的状态越趋于平静、有序。 结论：磁刺激肢体神门穴降低了脑电信号的混沌程度,使大脑更加镇静。提示磁刺激肢体神门穴具有改善脑功能的作用,在一定程度上证明了磁刺激疗法的可行性。     

Determination of awareness in patients with severe brain injury using EEG power spectral analysis

Objective

To determine whether EEG spectral analysis could be used to demonstrate awareness in patients with severe brain injury.

Methods

We recorded EEG from healthy controls and three patients with severe brain injury, ranging from minimally conscious state (MCS) to locked-in-state (LIS), while they were asked to imagine motor and spatial navigation tasks. We assessed EEG spectral differences from 4 to 24 Hz with univariate comparisons (individual frequencies) and multivariate comparisons (patterns across the frequency range).

Results

In controls, EEG spectral power differed at multiple frequency bands and channels during performance of both tasks compared to a resting baseline. As patterns of signal change were inconsistent between controls, we defined a positive response in patient subjects as consistent spectral changes across task performances. One patient in MCS and one in LIS showed evidence of motor imagery task performance, though with patterns of spectral change different from the controls.

Conclusions

EEG power spectral analysis demonstrates evidence for performance of mental imagery tasks in healthy controls and patients with severe brain injury.

Significance

EEG power spectral analysis can be used as a flexible bedside tool to demonstrate awareness in brain-injured patients who are otherwise unable to communicate.     

On seeing the trees and the forest: Single-signal and multisignal analysis of periictal intracranial EEG

Purpose: Epileptic seizures are associated with a dysregulation of electrical brain activity on many different spatial scales. To better understand the dynamics of epileptic seizures, that is, how the seizures initiate, propagate, and terminate, it is important to consider changes of electrical brain activity on different spatial scales. Herein we set out to analyze periictal electrical brain activity on comparatively small and large spatial scales by assessing changes in single intracranial electroencephalography (EEG) signals and of averaged interdependences of pairs of EEG signals. Methods: Single and multiple EEG signals are analyzed by combining methods from symbolic dynamics and information theory. This computationally efficient approach is chosen because at its core it consists of analyzing the occurrence of patterns and bears analogy to classical visual EEG reading. Symbolization is achieved by first mapping the EEG signals into bit strings, that is, long sequences of zeros and ones, depending solely on whether their amplitudes increase or decrease. Bit strings reflect relational aspects between consecutive values of the original EEG signals, but not the values themselves. For each bit string the relative frequencies of the different constituent short bit patterns are then determined and used to compute two information theoretical measures: (1) redundancy (R) of single bit strings characterizes electrical brain activity on a comparatively small spatial scale represented by a single EEG signal and (2) averaged pair-wise mutual information with all other bit strings (M), which allows tracking of larger-scale EEG dynamics. Key Findings: We analyzed 20 periictal intracranial EEG recordings from five patients with pharmacoresistant temporal lobe epilepsy. At seizure onset, R first strongly increased and then decreased toward seizure termination, whereas M gradually increased throughout the seizure. Bit strings with maximal R were always derived from EEG signals recorded from the visually identified seizure-onset zone. When compared to the bit strings derived from other EEG signals, their M was relatively smaller. These findings are consistent with a strong but transient occurrence of information-poor, that is, redundant electrical brain activity on a smaller spatial scale, which is particularly pronounced in the seizure-onset zone. On a larger spatial scale, a progressively more collective state emerges, as revealed by increasing amounts of mutual information. Significance: Information theoretical analysis of bit patterns derived from EEG signals helps to characterize periictal brain activity on different spatial scales in a quantitative and efficient way and may provide clinically relevant results.     

A brain-computer interface using electrocorticographic signals in humans

Brain-computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain.     

脑电非线性分析在认知功能研究中的应用

目的　探讨在不同认知作业状态下脑电非线性动力学特性的变化规律、脑电非线性动态分析在认知过程研究中的作用。方法　我们用关联维数 (D2 )、点关联维数 (PD2 )对 30名健康成年人四种状态下的脑电数据进行了分析 :安静闭眼、安静睁眼、闭眼心算作业和睁眼图形推理作业。结果　认知作业过程相对于安静状态 ,D2 和PD2 有明显的升高。闭眼和心算 ,睁眼和图形推理状态之间差异有显著意义 (D2 分别为 3 93和 4 33,P <0 0 1;4 4 7和 4 98,P <0 0 1)。D2 和PD2 随时间存在时高时低的现象。结论　动态的、短时程的非线性动力学分析方法 ,更适合研究认知过程中大脑功能活动的变化规律。以D2 和PD2 地形图为基础的动态分析 ,可以清晰地展示认知过程中D2 和PD2的分布情况及与认知作业相关的大脑部位活跃顺序和活跃程度的变化 ,有助于我们了解认知过程中大脑的工作机制。     

Operational Architectonics of the Human Brain Biopotential Field: Towards Solving the Mind-Brain Problem

The understanding of the interrelationshipbetween brain and mind remains far from clear.It is well established that the brain'scapacity to integrate information from numeroussources forms the basis for cognitiveabilities. However, the core unresolvedquestion is how information about the``objective' physical entities of the externalworld can be integrated, and how unifiedand coherent mental states (or Gestalts) can beestablished in the internal entities ofdistributed neuronal systems. The present paperoffers a unified methodological and conceptualbasis for a possible mechanism of how thetransient synchronization of brain operationsmay construct the unified and relatively stableneural states, which underlie mental states.It was shown that the sequence of metastablespatial EEG mosaics does exist and probablyreflects the rapid stabilization periods of theinterrelation of large neuron systems. At theEEG level this is reflected in thestabilization of quasi-stationary segments oncorresponding channels. Within the introducedframework, physical brain processes andpsychological processes are considered as twobasic aspects of a single whole informationalbrain state.The relations between operational process ofthe brain, mental states and consciousness arediscussed.     

Assessment of EEG dynamical complexity in Alzheimer’s disease using multiscale entropy

ObjectiveMultiscale entropy (MSE) is a recently proposed entropy-based index of physiological complexity, evaluating signals at multiple temporal scales. To test this method as an aid to elucidating the pathophysiology of Alzheimer’s disease (AD), we examined MSE in resting state EEG activity in comparison with traditional EEG analysis.MethodsWe recorded EEG in medication-free 15 presenile AD patients and 18 age- and sex-matched healthy control (HC) subjects. MSE was calculated for continuous 60-s epochs for each group, concurrently with power analysis.ResultsThe MSE results from smaller and larger scales were associated with higher and lower frequencies of relative power, respectively. Group analysis demonstrated that the AD group had less complexity at smaller scales in more frontal areas, consistent with previous findings. In contrast, higher complexity at larger scales was observed across brain areas in AD group and this higher complexity was significantly correlated with cognitive decline.ConclusionsMSE measures identified an abnormal complexity profile across different temporal scales and their relation to the severity of AD.SignificanceThese findings indicate that entropy-based analytic methods with applied at temporal scales may serve as a complementary approach for characterizing and understanding abnormal cortical dynamics in AD.