Association between individual EEG characteristics and the level of intelligence

The aim of this study was to investigate the relationship between individual electroencephalogram (EEG) characteristics in the resting state and the level of nonverbal intelligence. The study involved 77 students of Demidov Yaroslavl State University. Analysis of the relationship between IQ and spectral parameters of EEG theta, alpha, and two subbands of beta oscillations revealed that the amplitude and power of alphaband EEG oscillations and low frequency beta-band EEG oscillations were positively correlated with the performance in the nonverbal intelligence test. The variety of brain periodic regimes was assessed using the correlation dimension (CD) of EEG. The correlation dimension can be used to quantify the degree of complexity of the nonlinear dynamical system. It was found that the value of the EEG correlation dimension was positively associated with the level of intelligence. The periodicity of the EEG signal was studied using autocorrelation analysis. It was shown that the autocorrelogram duration was negatively associated with IQ and the autocorrelogram amplitude was positively associated with IQ. A regression equation for predicting the level of nonverbal intelligence based on the power of theta- and beta-band oscillations, alpha-band oscillation indexes, and the amplitude and autocorrelation characteristics of the EEG signal was obtained.     

Novel whole body plethysmography system for the continuous characterization of sleep and breathing in a mouse

Sleep is associated with marked alterations in ventilatory control that lead to perturbations in respiratory timing, breathing pattern, ventilation, pharyngeal collapsibility, and sleep-related breathing disorders (SRBD). Mouse models offer powerful insight into the pathogenesis of SRBD; however, methods for obtaining the full complement of continuous, high-fidelity respiratory, electroencephalographic (EEG), and electromyographic (EMG) signals in unrestrained mice during sleep and wake have not been developed. We adapted whole body plethysmography to record EEG, EMG, and respiratory signals continuously in unrestrained, unanesthetized mice. Whole body plethysmography tidal volume and airflow signals and a novel noninvasive surrogate for respiratory effort (respiratory movement signal) were validated against simultaneously measured gold standard signals. Compared with the gold standard, we validated 1) tidal volume (correlation, R(2) = 0.87, P < 0.001; and agreement within 1%, P < 0.001); 2) inspiratory airflow (correlation, R(2) = 0.92, P < 0.001; agreement within 4%, P < 0.001); 3) expiratory airflow (correlation, R(2) = 0.83, P < 0.001); and 4) respiratory movement signal (correlation, R(2) = 0.79-0.84, P < 0.001). The expiratory airflow signal, however, demonstrated a decrease in amplitude compared with the gold standard. Integrating respiratory and EEG/EMG signals, we fully characterized sleep and breathing patterns in conscious, unrestrained mice and demonstrated inspiratory flow limitation in a New Zealand Obese mouse. Our approach will facilitate studies of SRBD mechanisms in inbred mouse strains and offer a powerful platform to investigate the effects of environmental and pharmacological exposures on breathing disturbances during sleep and wakefulness.     

Nonlinear behavior of human respiratory movement during different sleep stages

To investigate the nonlinear properties of respiratory movement during different sleep stages, we applied an algorithm proposed by Grassberger and Procaccia to calculate the correlation dimension in rapid eye movement and non-rapid eye movement sleep. We also tested for nonlinearity in respiratory movement by comparing the correlation dimension for the original data with that for surrogate data. The study population included eight healthy volunteers. We recorded respiratory movement and the sleep electroencephalogram for 8 h. The correlation dimension for respiratory movement was 3.28+/-0.19 (mean +/- SD) during rapid eye movement sleep, 2.31+/-0.21 during light sleep (stage I) and 1.64+/-0.25 during deep slow-wave sleep (stage IV). Thus, the correlation dimension differed significantly by sleep stage (p < 0.001): it was least during stage IV sleep and greatest during REM. The correlation dimension for the original data also differed from that for surrogate data, confirming nonlinearity in original data. The results suggest that the nonlinear dynamics of respiratory movement in sleep changes with sleep stage, presumably due to the information processing by the cerebral cortex. The increased correlation dimension for respiratory movement in REM sleep may be related to increased cortical information processing associated with dreaming.     

A probabilistic framework for a physiological representation of dynamically evolving sleep state

This work presents a probabilistic method for mapping human sleep electroencephalogram (EEG) signals onto a state space based on a biologically plausible mathematical model of the cortex. From a noninvasive EEG signal, this method produces physiologically meaningful pathways of the cortical state over a night of sleep. We propose ways in which these pathways offer insights into sleep-related conditions, functions, and complex pathologies. To address explicitly the noisiness of the EEG signal and the stochastic nature of the mathematical model, we use a probabilistic Bayesian framework to map each EEG epoch to a distribution of likelihoods over all model sleep states. We show that the mapping produced from human data robustly separates rapid eye movement sleep (REM) from slow wave sleep (SWS). A Hidden Markov Model (HMM) is incorporated to improve the path results using the prior knowledge that cortical physiology has temporal continuity.     

Dimensional analysis of nonlinear oscillations in brain, heart, and muscle

We present some numerical studies on the dimensional analysis of temporal oscillations observed in human electroencephalograms (EEG), heart rates, and muscle tremors. We show that it is insufficient to characterize the individual system by a single dimension value alone. We also present some detailed numerical analysis of the scaling structure of the attractors reconstructed from the time signal. Our methods are based on the concept of local gauge functions, which we derive from the raw signals and from transformed signals obtained through singular value decomposition. We are able to confirm and improve earlier results on the change of dimensionality of EEG signals. For heart rates we observe an increase of the dimensional complexity during sleep, and for muscle tremor data we find significant changes in the dimensionality depending on the isometrical contraction of the muscle. We attempt to indicate which factors are important in determining dimension estimates and where specific problems lie in each of the examples.     

Sleep EEG as a nonlinear dynamic process: a comparison of global correlation dimension of human EEG and measures of linear interdependence between channels

The goal of this work was to study (1) whether the estimation of correlation dimension (D2) using spatial embedding distinguishes between sleep stages and (2) whether information gained from the application of global D2 is redundant to measures of linear interdependence between channels. Twenty one-channel EEG segments of 12 healthy male subjects recorded during waking and sleep stages REM, I, II, and III-IV (according to the Rechtshaffen and Kales criteria) were analyzed with global (multichannel) D2, mean square correlation coefficients (MS) and proportion of variance accounted for by the first principal component (PC1). D2 was found to decrease progressively from stage I to stage III-IV with D2 values of waking and REM being close to those of stages I and II. MS and PC1 did not distinguish among sleep stages but yielded significant differences between waking and sleep. The results suggest that global D2 extracts information from human EEG. That sort of evidence cannot be obtained with measures of linear interdependence between channels.     

Automatic analysis of sleep using two parameters based on principal component analysis of electroencephalography spectral data

A computer program for the analysis of a sleep electroencephalogram (EEG) is presented. The method relies on two steps. First, a spectral analysis is performed for signals recorded from one or more electrode locations. Then, two EEG parameters are obtained by storing the spectral activity in a multidimensional space, whose dimension is reduced using principal component analysis (PCA) techniques. The main advantage of these parameters is in describing the process of sleep on a continuous scale as a function of time. Validation of the method was performed with the data collected from 16 subjects (8 young volunteers and 8 elderly insomniacs). Results snowed that the parameters correlate highly with the hypnograms established by conventional visual scoring. This signal parametrisation, however, offers more information regarding the time course of sleep, since small variations within individual sleep stages as well as smooth transitions between stages are assessed. Finally, the concurrent use of both parameters provides an original way of considering sleep as a dynamic process evolving cyclically in a single plane.     

Age-related changes in the circadian and homeostatic regulation of human sleep

The reduction of electroencephalographic (EEG) slow-wave activity (SWA) (EEG power density between 0.75-4.5 Hz) and spindle frequency activity, together with an increase in involuntary awakenings during sleep, represent the hallmarks of human sleep alterations with age. It has been assumed that this decrease in non-rapid eye movement (NREM) sleep consolidation reflects an age-related attenuation of the sleep homeostatic drive. To test this hypothesis, we measured sleep EEG characteristics (i.e., SWA, sleep spindles) in healthy older volunteers in response to high (sleep deprivation protocol) and low sleep pressure (nap protocol) conditions. Despite the fact that the older volunteers had impaired sleep consolidation and reduced SWA levels, their relative SWA response to both high and low sleep pressure conditions was similar to that of younger persons. Only in frontal brain regions did we find an age-related diminished SWA response to high sleep pressure. On the other hand, we have clear evidence that the circadian regulation of sleep during the 40 h nap protocol was changed such that the circadian arousal signal in the evening was weaker in the older study participants. More sleep occurred during the wake maintenance zone, and subjective sleepiness ratings in the late afternoon and evening were higher than in younger participants. In addition, we found a diminished melatonin secretion and a reduced circadian modulation of REM sleep and spindle frequency-the latter was phase-advanced relative to the circadian melatonin profile. Therefore, we favor the hypothesis that age-related changes in sleep are due to weaker circadian regulation of sleep and wakefulness. Our data suggest that manipulations of the circadian timing system, rather than the sleep homeostat, may offer a potential strategy to alleviate age-related decrements in sleep and daytime alertness levels.     

NONLINEAR BEHAVIOR OF HUMAN RESPIRATORY MOVEMENT DURING DIFFERENT SLEEP STAGES

To investigate the nonlinear properties of respiratory movement during different sleep stages, we applied an algorithm proposed by Grassberger and Procaccia to calculate the correlation dimension in rapid eye movement and non-rapid eye movement sleep. We also tested for nonlinearity in respiratory movement by comparing the correlation dimension for the original data with that for surrogate data. The study population included eight healthy volunteers. We recorded respiratory movement and the sleep electroencephalogram for 8 h. The correlation dimension for respiratory movement was 3.28 ± 0.19 (mean ± SD) during rapid eye movement sleep, 2.31 ± 0.21 during light sleep (stage I) and 1.64 ± 0.25 during deep slow-wave sleep (stage IV). Thus, the correlation dimension differed significantly by sleep stage (p < 0.001): it was least during stage IV sleep and greatest during REM. The correlation dimension for the original data also differed from that for surrogate data, confirming nonlinearity in original data. The results suggest that the nonlinear dynamics of respiratory movement in sleep changes with sleep stage, presumably due to the information processing by the cerebral cortex. The increased correlation dimension for respiratory movement in REM sleep may be related to increased cortical information processing associated with dreaming. (Chronobiology International, 18(1), 71–83, 2001)     

Synchronization and Propagation of Global Sleep Spindles

Sleep spindles occur thousands of times during normal sleep and can be easily detected by visual inspection of EEG signals. These characteristics make spindles one of the most studied EEG structures in mammalian sleep. In this work we considered global spindles, which are spindles that are observed simultaneously in all EEG channels. We propose a methodology that investigates both the signal envelope and phase/frequency of each global spindle. By analysing the global spindle phase we showed that 90% of spindles synchronize with an average latency time of 0.1 s. We also measured the frequency modulation (chirp) of global spindles and found that global spindle chirp and synchronization are not correlated. By investigating the signal envelopes and implementing a homogeneous and isotropic propagation model, we could estimate both the signal origin and velocity in global spindles. Our results indicate that this simple and non-invasive approach could determine with reasonable precision the spindle origin, and allowed us to estimate a signal speed of 0.12 m/s. Finally, we consider whether synchronization might be useful as a non-invasive diagnostic tool.     

基于奇异值第一主成分的睡眠脑电分期方法研究

目的:脑电信号含多种噪声和伪迹,信噪比较低,特征提取前必须进行复杂的预处理,严重影响睡眠分期的速度。鉴于此,本文提出一种基于奇异值第一主成分的睡眠脑电分期方法,该方法抗噪性能较强,可省去预处理过程,减少计算量,提高睡眠分期的效率。方法:对未经过预处理的睡眠脑电进行奇异系统分析,研究奇异谱曲线,提取奇异值第一主成分,探索其随睡眠状态变化的规律。并通过支持向量机利用奇异值第一主成分对睡眠分期。结果:奇异值第一主成分不仅能表征脑电信号主体,而且可以抑制噪声、降低维数。随着睡眠的深入,奇异值第一主成分的值逐渐增大,但在REM期处于S1期和S2期之间。经MIT-BIH睡眠数据库中5例同导联位置的脑电数据测试(仅1导脑电数据),睡眠脑电分期的准确率达到86.4%。结论:在未对脑电信号进行预处理的情况下,提取的睡眠脑电的奇异值第一主成分能有效表征睡眠状态,是一种有效的睡眠分期依据。本文运用提出的方法仅采用1导脑电数据,就能得到较为满意的睡眠分期结果。该方法有较强的分类性能,且抗噪能力强,不需要对脑电作复杂的预处理,计算量小,方法简单,很大程度上提高了睡眠分期的效率。     

Age‐related Changes in the Circadian and Homeostatic Regulation of Human Sleep

The reduction of electroencephalographic (EEG) slow‐wave activity (SWA) (EEG power density between 0.75–4.5 Hz) and spindle frequency activity, together with an increase in involuntary awakenings during sleep, represent the hallmarks of human sleep alterations with age. It has been assumed that this decrease in non‐rapid eye movement (NREM) sleep consolidation reflects an age‐related attenuation of the sleep homeostatic drive. To test this hypothesis, we measured sleep EEG characteristics (i.e., SWA, sleep spindles) in healthy older volunteers in response to high (sleep deprivation protocol) and low sleep pressure (nap protocol) conditions. Despite the fact that the older volunteers had impaired sleep consolidation and reduced SWA levels, their relative SWA response to both high and low sleep pressure conditions was similar to that of younger persons. Only in frontal brain regions did we find an age‐related diminished SWA response to high sleep pressure. On the other hand, we have clear evidence that the circadian regulation of sleep during the 40 h nap protocol was changed such that the circadian arousal signal in the evening was weaker in the older study participants. More sleep occurred during the wake maintenance zone, and subjective sleepiness ratings in the late afternoon and evening were higher than in younger participants. In addition, we found a diminished melatonin secretion and a reduced circadian modulation of REM sleep and spindle frequency—the latter was phase‐advanced relative to the circadian melatonin profile. Therefore, we favor the hypothesis that age‐related changes in sleep are due to weaker circadian regulation of sleep and wakefulness. Our data suggest that manipulations of the circadian timing system, rather than the sleep homeostat, may offer a potential strategy to alleviate age‐related decrements in sleep and daytime alertness levels.     

高维混沌降维的投影追踪主分量分析法

一些生理信号,例如脑电是源自于高维混沌系统,因此低维混沌理论和方法不适用于分析这类高维混沌。采用投影追踪主分量分析法（Princiopal Component Analysis based on Projection Pursuit,PP PCA）对高维Lorenz模型系统进行了降维的研究。在用上述方法成功地对线性和非线性噪声－周期模型分别进行了PP PCA分析的基础上,对Lorenz高维混沌系统进行了PPPCA降维的研究。结果表明,正确选用非线性的投影追踪主分量分析法,可以通过简化原系统达到降维的目的,并能保留研究所关心的原系统的主要动态特性。同时也阐明了方法的稳定性和将该方法应用于高维脑电降维的可行性。     

Analysis and automatic identification of sleep stages using higher order spectra

Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%.     

Nonlinear analysis of EEG signals: Surrogate data analysis

ObjectivesThe electroencephalogram (EEG) signal contains information about the state and condition of the brain. The aim of the study is to conduct a nonlinear analysis of the EEG signals and to compare the differences in the nonlinear characteristics of the EEG during normal state and the epileptic state.DataThe EEG data used for this study – which consisted of epileptic EEG and normal EEG – were obtained from the EEG database available with the Bonn University, Germany.ResultsThe attractors seen in normal and epileptic human brain dynamics were studied and compared. Surrogate data analyses were conducted on two nonlinear measures, namely the largest Lyapunov exponent and the correlation dimension, to test the hypothesis whether EEG signals were in accordance with linear stochastic models.DiscussionsThe existence of deterministic chaos in brain activity is confirmed by the existence of a chaotic attractor; also, saturation of the correlation dimension towards a definite value is the manifestation of a deterministic dynamics. Also a reduction is observed between the dimensionalities of the brain attractors from normal state to the epileptic state. The evaluation of the largest Lyapunov exponent also confirms the lowering of complexity during an episode of seizure.ConclusionIn case of Lyapunov exponent of EEG data, the change due to surrogating is small suggesting that it is not representing the system complexity properly but there is a marked change in the case of correlation dimension value due to surrogating.     

Early Adolescent Cognitive Gains Are Marked by Increased Sleep EEG Coherence

Although the increases in cognitive capacities of adolescent humans are concurrent with significant cortical restructuring, functional associations between these phenomena are unclear. We examined the association between cortical development, as measured by the sleep EEG, and cognitive performance in a sample of 9/10 year olds followed up 1 to 3 years later. Our cognitive measures included a response inhibition task (Stroop), an executive control task (Trail Making), and a verbal fluency task (FAS). We correlated sleep EEG measures of power and intra-hemispheric coherence at the initial assessment with performance at that assessment. In addition we correlated the rate of change across assessments in sleep EEG measures with the rate of change in performance. We found no correlation between sleep EEG power and performance on cognitive tasks for the initial assessment. In contrast, we found a significant correlation of the rate of change in intra-hemispheric coherence for the sigma band (11 to 16 Hz) with rate of change in performance on the Stroop (r = 0.61; p<0.02) and Trail Making (r = −0.51; p<0.02) but no association for the FAS. Thus, plastic changes in connectivity (i.e., sleep EEG coherence) were associated with improvement in complex cognitive function.     

Application of the correlation integral to respiratory data of infants during REM sleep

 Non-linear time sequence analysis has been performed on infant sleep measurement data in order to obtain more information about the respiratory processes. As a first step, respiration data during REM sleep were analysed with methods from non-linear dynamics, especially, the correlation integral and the slope of its log-log plot, representing the correlation dimension. Before calculation of the correlation integral, a special kind of filtering has to be applied to the data. This filtering algorithm is a state space and singular value decomposition-based noise reduction method, and it is used to separate the noise and signal subspaces. The dynamics of a signal (in our case data from the respiratory process) and its degrees of freedom can be characterised by the correlation integral and by the correlation dimension, respectively. The main result of this study is that the highly irregular-looking breathing patterns during REM sleep could be described by a deterministic system, and finally the physiological significance of this finding is discussed. Received: 17 June 1994/Accepted in revised form: 18 November 1994     

Surrogate data analysis of sleep electroencephalograms reveals evidence for nonlinearity

 We tested the hypothesis of whether sleep electroencephalographic (EEG) signals of different time windows (164 s, 82 s, 41 s and 20.5 s) are in accordance with linear stochastic models. For this purpose we analyzed the all-night sleep electroencephalogram of a healthy subject and corresponding Gaussian-rescaled phase randomized surrogates with a battery of five nonlinear measures. The following nonlinear measures were implemented: largest Lyapunov exponent L1, correlation dimension D2, and the Green-Savit measures δ2, δ4 and δ6. The hypothesis of linear stochastic data was rejected with high statistical significance. L1 and D2 yielded the most pronounced effects, while the Green-Savit measures were only partially successful in differentiating EEG epochs from the phase randomized surrogates. For L1 and D2 the efficiency of distinguishing EEG signals from linear stochastic data decreased with shortening of the time window. Altogether, our results indicate that EEG signals exhibit nonlinear elements and cannot completely be described by linear stochastic models. Received: 21 December 1995/Accepted in revised form: 19 March 1996     

Neural net classification of REM sleep based on spectral measures as compared to nonlinear measures

 In various studies the implementation of nonlinear and nonconventional measures has significantly improved EEG (electroencephalogram) analyses as compared to using conventional parameters alone. A neural network algorithm well approved in our laboratory for the automatic recognition of rapid eye movement (REM) sleep was investigated in this regard. Originally based on a broad range of spectral power inputs, we additionally supplied the nonlinear measures of the largest Lyapunov exponent and correlation dimension as well as the nonconventional stochastic measures of spectral entropy and entropy of amplitudes. No improvement in the detection of REM sleep could be achieved by the inclusion of the new measures. The accuracy of the classification was significantly worse, however, when supplied with these variables alone. In view of results demonstrating the efficiency of nonconventional measures in EEG analysis, the benefit appears to depend on the nature of the problem. Received: 10 October 2000 / Accepted in revised form: 26 April 2001     

Is breathing in infants chaotic? Dimension estimates for respiratory patterns during quiet sleep

Wedescribe an analysis of dynamic behavior apparent in times-seriesrecordings of infant breathing during sleep. Three principal techniqueswere used: estimation of correlation dimension, surrogate dataanalysis, and reduced linear (autoregressive) modeling (RARM). Correlation dimension can be used to quantify the complexity of timeseries and has been applied to a variety of physiological andbiological measurements. However, the methods most commonly used toestimate correlation dimension suffer from some technical problems thatcan produce misleading results if not correctly applied. We used a newtechnique of estimating correlation dimension that has fewer problems.We tested the significance of dimension estimates by comparingestimates with artificial data sets (surrogate data). On the basis ofthe analysis, we conclude that the dynamics of infant breathing duringquiet sleep can best be described as a nonlinear dynamic system withlarge-scale, low-dimensional and small-scale, high-dimensionalbehavior; more specifically, a noise-driven nonlinear system with atwo-dimensional periodic orbit. Using our RARM technique, we identifiedthe second period as cyclic amplitude modulation of the same period asperiodic breathing. We conclude that our data are consistent withrespiration being chaotic.