Period-peak analysis of the EEG with microprocessor applications

This paper describes the development and evaluation of a period-peak algorithm for background analysis of the clinical electroencephalogram (EEG). The procedure is a time-domain method which is harmonious with manual interpretation of the EEG tracing. Conceptually the algorithm functions in 2 modes. Major counts are detected by successsive baseline crossings in the period analysis mode. Presence of superimposed activity between major-counts induces a transition to the peak-detection mode. In this manner, period-peak analysis is capable of detecting the simultaneity of slow base-waves and relatively fast superimposed activity in the EEG. Preliminary studies have been conducted in which the analysis results of this procedure were compared to those of other EEG algorithms. In general, the period-peak algorithm offered less bias towards either end of the EEG spectrum. Subsequent to testing of a FORTRAN version, the period-peak algorithm has been implemented in assembly language on a dedicated microprocessor system for on-line analysis of EEG data.     

EEG power spectrum analysis for monitoring depth of anaesthesia during experimental surgery

The first attempts to introduce computerized power spectrum analysis of the electroencephalogram (EEG) as an intraoperative anaesthesia monitoring device started approximately 30 years ago. Since that time, the effects of various anaesthetic agents, sedative and analgesic drugs on the EEG pattern have been addressed in numerous studies in human patients and different animal species. These studies revealed dose-dependent changes in the EEG power spectrum for many intravenous and volatile anaesthetics. Moreover, EEG responses evoked by surgical stimuli during relative light levels of surgical anaesthesia have been classified as 'arousal' and 'paradoxical arousal' reaction, previously referred to as 'desynchronization' and 'synchronization', respectively. Contrasting reports on the correlation between quantitative EEG (QEEG) variables derived from power spectrum analysis (i.e. spectral edge frequency, median frequency) and simultaneously recorded clinical signs such as movement and haemodynamic responses, however, limited the routine use of intraoperative EEG monitoring. In addition, the appearance of EEG burst suppression pattern and isoelectricity at clinically relevant concentrations/doses of newer general anaesthetics (i.e. isoflurane, sevoflurane, propofol) may have weakened the dose-related EEG changes previously reported. Despite these findings, the EEG power spectrum analysis may still provide valuable information during intraoperative monitoring in the individual subject. The information obtained from EEG power spectrum analysis may be further supplemented by newer EEG indices such as bispectral index and approximate entropy or other neurophysiological monitors including auditory evoked potentials or somatosensory evoked potentials.     

Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques

Electroencephalogram (EEG) analysis remains problematic due to limited understanding of the signal origin, which leads to the difficulty of designing evaluation methods. In spite of these shortcomings, the EEG is a valuable tool in the evaluation of some neurological disorders as well as in the evaluation of overall cerebral activity. In most studies, which use quantitative EEG analysis, the properties of measured EEG are computed by applying power spectral density (PSD) estimation for selected representative EEG samples. The sample for which the PSD is calculated is assumed to be stationary. This work deals with a comparative study of the PSD obtained from normal, epileptic and alcoholic EEG signals. The power density spectra were calculated using fast Fourier transform (FFT) by Welch's method, auto regressive (AR) method by Yule–Walker and Burg's method. The results are tabulated for these different classes of EEG signals.     

Non-linear analysis of the electroencephalogram in Creutzfeldt-Jakob disease

Creutzfeldt-Jakob disease is a rare, neurological, dementing disorder characterised by periodic sharp waves in the electroencephalogram (EEG). Non-linear analysis of these EEG changes may provide insight into the abnormal dynamics of cortical neural networks in this disorder. Babloyantz et al. have suggested that the periodic sharp waves reflect low-dimensional chaotic dynamics in the brain. In the present study this hypothesis was re-examined using newly developed techniques for non-linear time series analysis. We analysed the EEG of a patient with autopsy-proven Creutzfeldt-Jakob disease using the method of non-linear forecasting as introduced by Sugihara and May, and we tested for non-linearity with amplitude-adjusted, phase-randomised surrogate data. Two epochs with generalised periodic sharp waves showed clear evidence for non-linearity. These epochs could be predicted better and further ahead in time than most of the irregular background activity. Testing against cycle-randomised surrogate data and close inspection of the periodograms showed that the non-linearity of the periodic sharp waves may be better explained by quasi-periodicity than by low-dimensional chaos. The EEG further displayed at least one example of a sudden, large qualitative change in the dynamics, highly suggestive of a bifurcation. The presence of quasi-periodicity and bifurcations strongly argues for the use of a non-linear model to describe the EEG in Creutzfeldt-Jakob disease. Received: 28 October 1996 / Accepted in revised form: 8 July 1997     

Spectral analysis of the effects of nomifensine on bioelectric activity of the rat brain]

The influence of nomifensine on bioelectrical activity of sensorimotor cortex, dorsal hippocamp and lateral hypothalamus in conscious rats in free behavior was studied. The pharmacological and EEG analysis of nomifensine action on EEG power spectra by Fourier technique was measured. It was established that nomifensine evoked an increase and stabilization of the dominant peak in EEG spectra in the left and right cortex and in the left hippocamp, while in the other ranges of frequency a decrease was observed. The effects on hypothalamus was opposite--EEG power spectra decreased in all the ranges. The authors conclude that nomifensine evokes higher level of wakefulness, vigilance of the animals. This is likely to underlie neurophysiological mechanisms of optimal behavior due to stimulants of CNS, including nomifensine.     

The dynamics of spindles and EEG slow-wave activity in NREM sleep in mice

A quantitative analysis of spindles and spindle-related EEG activity was performed in C57BL/6 mice. The hypothesis that spindles are involved in sleep regulatory mechanisms was tested by investigating their occurrence during 24 h and after 6 h sleep deprivation (SD; n = 7). In the frontal derivation distinct spindle events were characterized as EEG oscillations with a dominant frequency approximately at 11 Hz. Spindles were most prominent during NREM sleep and increased before NREM-REM sleep transitions. Whereas spindles increased concomitantly with slow wave activity (SWA, EEG power between 0.5 and 4.0 Hz) at the beginning of the NREM sleep episode, these measures showed an opposite evolution prior to the transition to REM sleep. The 24-h time course of spindles showed a maximum at the end of the 12-h light period, and was a mirror image of SWA in NREM sleep. After 6 h SD the spindles in NREM sleep were initially suppressed, and showed a delayed rebound. In contrast, spindles occurring immediately before the transition to REM sleep were enhanced during the first 2 h of recovery. The data suggest that spindles in NREM sleep may be involved in sleep maintenance, while spindles heralding the transition to REM sleep may be related to mechanisms of REM sleep initiation.     

Multivariate EEG spectral analysis evidences the functional link between motor and visual cortex during integrative sensorimotor tasks

The identification of the networks connecting brain areas and the understanding of their role in executing complex tasks is a crucial issue in cognitive neuroscience. In this study, specific visuomotor tasks were devised to reveal the functional network underlying the cooperation process between visual and motor regions. Electroencephalography (EEG) data were recorded from twelve healthy subjects during a combined visuomotor task, which integrated precise grip motor commands with sensory visual feedback (VM). This condition was compared with control tasks involving pure motor action (M), pure visual perception (V) and visuomotor performance without feedback (V + M). Multivariate parametric cross-spectral analysis was applied to ten EEG derivations in each subject to assess changes in the oscillatory activity of the involved cortical regions and quantify their coupling. Spectral decomposition was applied to precisely and objectively determine the power associated with each oscillatory component of the spectrum, while surrogate data analysis was performed to assess the statistical significance of estimated coherence values. A significant decrease of the alpha and/or beta power in EEG spectra with respect to rest values was assumed as indicative of specific cortical area activation during task execution. Indeed alpha band coherence increased in proximity of task-involved areas, while it was suppressed or remained unchanged in other regions, suggesting the activation of a specific network for each task. According to our coherence analysis, a direct link between visual and motor areas was activated during V + M and VM tasks. The effect of visual feedback was evident in the beta band, where the increase of coherence was observed only during the VM task. Multivariate analysis suggested the presence of a functional link between motor and visual cortex subserving sensorimotor integration. Furthermore, network activation was related to the sum of single task (M and V) local effects in the alpha band, and to the presence of visual feedback in the beta band.     

电针对实验性癫痫发作的影响：脑电的功率谱分析

以电惊厥和青霉素致痫作为实验性癲痫的动物模型。采用脑电的计算机功率谱分析技术,研究了电针作用于发作过程中脑电各频段功率百分比的变化。在安静的大鼠,脑电以δ和θ频段为主,其功率主峰在δ频段。青霉素致痫和电惊厥使δ频段功率百分比下降,α和β频段功率百分比增加,主功率频段右移,总功率亦大大增强。本实验采用的电针对背景脑电活动没有明显影响。而电针加电惊厥或青霉素致痫,δ频段功率百分比复又增加,α和β频段功率百分比则下降,主功率频段又回到δ频段,总功率也显著减少。压缩功率谱阵图直观地显示了这种变化。结果提示,电针可使大鼠脑电出现同步化趋势,可能是加强了脑的抑制过程,从而抑制了癲痫发作的。     

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.     

Spectral and coherence EEG analysis of rats differing in the level of seizure readiness

The resting EEGs of several brain structures (motor and visual cortex, caudate nucleus and intralaminar thalamic nuclei) were submitted to spectral and coherence computer analyses in two rat strains. Genetically predisposed to convulsive state KM rats were shown to differ from nonpredisposed Wistar rats in EEG spectral properties. KM rats EEG pattern was characterized by increase of low frequencies (1-2 Hz) power and decrease of faster activity (5-12 Hz) power in cortical spectrograms as well as by decrease of caudate nucleus EEG absolute power. The coherence value between cortical or subcortical structures at below 4 Hz was intensified in KM rats. Reinforcement of cortical auto-oscillating properties manifested by ECoG synchronization in cortical-thalamic resonance interaction as well as weakening of striatal inhibitory system may constitute neurophysiological mechanisms of enhanced convulsive readiness. The probable role of mediator imbalance in these mechanisms is discussed.     

New aspects of the neurophysiological mechanism of the action of nootropic preparations

The influence of nootropic drugs on EEG spectral power of the cortex and hippocamp was studied in resting rats. All these drugs had a specific action on EEG spectral power, causing an increase and stabilization of maximum basic distribution peak of the EEG spectral power. Such action may be attributed to better organization of rhythmic activity in theta-range. The drugs also decreased interhemispheric asymmetry of the cortical and hippocampal EEG. The authors suggest that the increase and improvement of the basic rhythmic activity in the brain and an increased level of distant (spatial) synchronization form the basis for the nootropic drug effect.     

EEG correlates of hypnotic susceptibility based upon fast Fourier power spectral analysis

The purpose of the present study was to determine if there were differences between high and low hypnotic susceptible subjects based upon fast Fourier power spectral analysis of the EEG recorded both before and during hypnotic tasks from frontal-temporal and occipital-parietal locations. Significant differences were obtained based upon EEG recording electrode location, EEG frequency within six different frequency domains, and hypnotic tasks. However, no main effect differences were obtained based upon hypnotic susceptibility. In contrast to some evoked potential studies in which a few differences have been obtained based on hypnotic susceptibility the lack of any EEG differences in this study even when positive and negative hallucination tasks were employed may have implications for the role of the neocortex in mediating hypnotic phenomena.We would like to acknowledge Dr. Michael K. Smith for his help in developing the statistical analysis and for his help in transforming the linear data employed in the ANOVAs in this study.     

Electroencephalograms of patients with initial signs of depression: Independent component analysis

Independent component analysis (ICA) of 19-channel background EEG was performed in 111 patients with the early signs of depressive disorders and in 526 healthy subjects. The power spectra of the independent components were compared in the depressive patients and in healthy subjects at the eyes closed and eyes opened states. Statistically significant differences between the groups were detected in three frequency bands: θ (4–7.5 Hz), α (7.5–14 Hz), and β (14–20 Hz). Increased θ and α activities in parietal and occipital derivations of depressive patients may have been caused by a reduced cortical activity in the projection of these derivation. Diffuse enhancement of the β activity may be correlated with anxiety symptoms that are pronounced in the clinical picture of depressive disorders at early stages of the disease. ICA used to compare quantitative EEG parameters in different groups of patients and in healthy persons makes it possible to localize the differences more accurately than the traditional analysis of EEG spectra.     

Principal component analysis of electroencephalographic activity during sleep and wakefulness in the spider monkey (Ateles geoffroyi)

The study of electroencephalographic (EEG) activity during sleep in the spider monkey has provided new insights into primitive arboreal sleep physiology and behavior in anthropoids. Nevertheless, studies conducted to date have maintained the frequency ranges of the EEG bands commonly used with humans. The aim of the present work was to determine the EEG broad bands that characterize sleep and wakefulness in the spider monkey using principal component analysis (PCA). The EEG activity was recorded from the occipital, central, and frontal EEG derivations of six young-adult male spider monkeys housed in a laboratory setting. To determine which frequencies covaried and which were orthogonally independent during sleep and wakefulness, the power EEG spectra and interhemispheric and intrahemispheric EEG correlations from 1 to 30 Hz were subjected to PCA. Findings show that the EEG bands detection differed from those reported previously in both spider monkeys and humans, and that the 1–3 and 2–13 Hz frequency ranges concur with the oscillatory activity elucidated by cellular recordings of subcortical regions. Results show that applying PCA to the EEG spectrum during sleep and wakefulness in the spider monkey led to the identification of frequencies that covaried with, and were orthogonally independent of, other frequencies in each behavioral vigilance state. The new EEG bands differ from those used previously with both spider monkeys and humans. The 1–3 and 2–13 Hz frequency ranges are in accordance with the oscillatory activity elucidated by cellular recordings of subcortical regions in other mammals.     

Electroencephalogram bands modulated by vigilance states in an anuran species: a factor analytic approach

Dramatic changes in neocortical electroencephalogram (EEG) rhythms are associated with the sleep–waking cycle in mammals. Although amphibians are thought to lack a neocortical homologue, changes in rest–activity states occur in these species. In the present study, EEG signals were recorded from the surface of the cerebral hemispheres and midbrain on both sides of the brain in an anuran species, Babina daunchina, using electrodes contacting the meninges in order to measure changes in mean EEG power across behavioral states. Functionally relevant frequency bands were identified using factor analysis. The results indicate that: (1) EEG power was concentrated in four frequency bands during the awake or active state and in three frequency bands during rest; (2) EEG bands in frogs differed substantially from humans, especially in the fast frequency band; (3) bursts similar to mammalian sleep spindles, which occur in non-rapid eye movement mammalian sleep, were observed when frogs were at rest suggesting sleep spindle-like EEG activity appeared prior to the evolution of mammals.     

Spectral EEG characteristics in students with different anxiety profile during tests

EEG spectral power was calculated in 39 students at the age of 19-21 years in two experimental conditions: during the common educational process and immediately before an examination (stress condition). During the education process, in subjects with high anxiety (tested by Spielberger) the relative spectral power of the delta activity was higher than in the other group in the occipital, parietal, central, and right frontal brain areas, whereas the power of the EEG alpha in these areas was lower. Before examination tests, in subjects with high activity the delta power bilaterally increased in the temporal areas, whereas in subjects with low anxiety there was a decrease in the alpha rhythm power, especially in the right frontal area. In stress condition, the relative power of the delta activity in both occipital and temporal, right parietal and central areas was higher in subjects with low anxiety, whereas their alpha power was lower in both frontal and in occipital, parietal, and temporal areas of the left hemisphere.     

On-Going Frontal Alpha Rhythms Are Dominant in Passive State and Desynchronize in Active State in Adult Gray Mouse Lemurs

The gray mouse lemur (Microcebus murinus) is considered a useful primate model for translational research. In the framework of IMI PharmaCog project (Grant Agreement n°115009, www.pharmacog.org), we tested the hypothesis that spectral electroencephalographic (EEG) markers of motor and locomotor activity in gray mouse lemurs reflect typical movement-related desynchronization of alpha rhythms (about 8–12 Hz) in humans. To this aim, EEG (bipolar electrodes in frontal cortex) and electromyographic (EMG; bipolar electrodes sutured in neck muscles) data were recorded in 13 male adult (about 3 years) lemurs. Artifact-free EEG segments during active state (gross movements, exploratory movements or locomotor activity) and awake passive state (no sleep) were selected on the basis of instrumental measures of animal behavior, and were used as an input for EEG power density analysis. Results showed a clear peak of EEG power density at alpha range (7–9 Hz) during passive state. During active state, there was a reduction in alpha power density (8–12 Hz) and an increase of power density at slow frequencies (1–4 Hz). Relative EMG activity was related to EEG power density at 2–4 Hz (positive correlation) and at 8–12 Hz (negative correlation). These results suggest for the first time that the primate gray mouse lemurs and humans may share basic neurophysiologic mechanisms of synchronization of frontal alpha rhythms in awake passive state and their desynchronization during motor and locomotor activity. These EEG markers may be an ideal experimental model for translational basic (motor science) and applied (pharmacological and non-pharmacological interventions) research in Neurophysiology.     

Brain signal analysis based on recurrences

The EEG is one of the most commonly used tools in brain research. Though of high relevance in research, the data obtained is very noisy and nonstationary. In the present article we investigate the applicability of a nonlinear data analysis method, the recurrence quantification analysis (RQA), to such data. The method solely rests on the natural property of recurrence which is a phenomenon inherent to complex systems, such as the brain. We show that this method is indeed suitable for the analysis of EEG data and that it might improve contemporary EEG analysis.     

Spectral analysis of the human EEG while listening to music

EEG was recorded from 16 electrodes (International 10-20 System) in 14 healthy volunteers aged from 18 to 45 in the state of rest and during listening to music (popular classical symphonic pieces). The EEG spectral analysis was carried out. Presentation of musical pieces increased the EEG spectral power in the alpha-range in the parietal and occipital areas of both hemispheres. During repeated listening of the same musical fragment there was a tendency for attenuation of these changes. A significant decrease in the peak frequency of the alpha-band was found in the process of listening. The spectral values did not significantly differ between the left and right hemispheres. The findings suggest a decrease in the level of CNS activation under the influence of music.     

Bispectral analysis does not differentiate between anaesthesia EEG and a linear random process]

Bispectral analysis of the electroencephalogram (EEG) has been used to monitor depth of anaesthesia. In the majority of publications this has involved the use of the so called BIS-Index TM (Aspect Medical Systems, Inc.). The exact relationship of this index to such bispectral parameters as the bispectrum and bicoherence has not yet been reported. If the EEG is considered as a linear random process, bicoherence is trivial, i.e. it is independent of the EEG frequency. The aim of this study was to determine the proportion of EEG epochs with non-trivial bicoherence during isoflurane/N20 anaesthesia. We reanalyzed 25.5 hours of digitalised EEG signal from 9 patients undergoing gynaecological surgery. The test developed by Hinich for Gaussian distribution and linearity was then applied. The test was validated using various synthetic surrogate data: Gaussian random data, the z-component of the Lorenz attractor, the phase randomized EEG and the phase randomized z-component of the Lorenz attractor. The percentage of epochs (8.192 s, 1024 data points) with non-trivial bicoherence was: Lorenz data 95.4%, phase randomized Lorenz data 9.4%, synthetic Gaussian data 14.8%, original EEG 9.1%, phase randomized EEG 5.1%. The original EEG data were not found to contain a higher percentage of epochs with non-trivial bicoherence than the phase randomized data and the synthetic Gaussian data. We conclude that bispectral analysis does not substantially add to the information obtained with other methods of quantitative EEG analysis.