Comparison of human ictal, interictal and normal non-linear component analyses.

OBJECTIVES: The non-linear properties of EEG and filtered rhythms obtained from healthy subjects and epileptic patients with complex partial seizures were analyzed to investigate whether EEG in different neurological states can be generated by the mechanism that integrates several non-linear dynamic systems. METHODS: The control EEG (from 26 healthy subjects), interictal EEG and ictal EEG (from 25 patients) were digitally filtered into delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz) and gamma (30-40 Hz) components. The correlation dimension was calculated on each original signal and corresponding surrogate data. A new method was developed to accelerate the calculation of the correlation integral. Function P(m,r) was defined to visualize the meaning of the correlation dimension. The point critical to the estimation was determined by the P(m,r) function. RESULTS: The EEG in the control subjects and patients showed significantly lower correlation dimensions than the surrogate data. The delta, alpha, beta and gamma components from the control EEG exhibited similar complexity to the surrogate data, while only the alpha component from the interictal EEG presented the same dimension as the surrogate data. The correlation dimensions of the theta and alpha components remained the same when the neurological state changed from interictal EEG to ictal EEG. The complexity of the beta component was higher than the complexity of other components in both control subjects and patients. The correlation dimension of EEG was significantly correlated to the complexity of delta, theta, beta and gamma components. CONCLUSIONS: Our results suggest that EEG and filtered components in different neurological states demonstrate varied dynamic properties. The characteristics of neuronal networks can be differentiated by the dynamics of filtered components. Separating EEG into different dynamic systems may facilitate understanding of the mechanisms involved in the human EEG.     

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.     

Nonlinear EEG analysis in early Alzheimer's disease

Nonlinear EEG analysis attempts to characterize the dynamics of neural networks in the brain. Abnormalities in nonlinear EEG measures have been found repeatedly in Alzheimer's disease (AD). The present study was undertaken to investigate whether these abnormalities could already be found in the early stage of AD. In a representative sample of 49 community-dwelling elderly, Alzheimer's disease was diagnosed in 7 subjects. Correlation dimension (D2) and nonlinear prediction were measured at 16 electrodes and in two different activational states. Also, 10 surrogate data sets were generated for each EEG epoch in order to investigate the presence of nonlinear dynamics. Differences between nonlinear statistics derived from original and from surrogate data sets were expressed as Z-scores. We found lower D2 and higher predictability in the demented subjects compared to the normal subjects. The results obtained with the Z-scores pointed to changed nonlinear dynamics in frontal and temporal areas in demented subjects. However, the major differences between demented and healthy subjects are not due to nonlinearity. From this it appears that linear dynamics change first in the course of AD, followed by changes in nonlinear dynamics.     

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。     

Relationship between correlation dimension and indices of linear analysis in both respiratory movement and electroencephalogram.

OBJECTIVE: We investigate the relationships between signals from the electroencephalogram (EEG) and those from respiratory movement using the correlation dimension (D(2)). METHODS: Respiratory movement and EEG were recorded for 7.5h from 7 clinically healthy men. D(2) was calculated by applying an algorithm slightly modified from that proposed by Grassberger and Procaccia (Phys Rev Lett 50 (1983) 346). Non-linearity in respiratory movement and EEG was tested by comparing D(2) for the original data with that for surrogate data. RESULTS: A statistically significant positive correlation between D(2) of the EEG and D(2) of the respiratory movement was observed for the original data, but not for the surrogate data. CONCLUSIONS: A reduced D(2) of the EEG may be associated with an increased regularity of breathing in deep sleep (stage IV). Likewise, the increased D(2) of respiratory movement during rapid eye movement may be associated with increased complexity of the signals. Whether there is a direct coordination between brain and lungs or whether brainstem systems, including that of the cholinergic system, affect both respiration and cortex requires further investigation.     

Nonlinear dynamical analysis of the neonatal EEG time series: the relationship between neurodevelopment and complexity.

OBJECTIVE: To investigate the relationship between the complexity of sleep EEG time series and neurodevelopment for premature or full-term neonates. METHODS: Nonlinear dynamical analysis of neonatal sleep EEG time series is used to compute the correlation dimension D2 which is an index of the complexity of the dynamics of the developing brain. The dimensional complexity is estimated using Theiler's modification of the Grassberger-Procaccia algorithm for two different values of Theiler's w parameter. The hypothesis that neonatal EEG data during sleep contains nonlinear features is verified by means of surrogate data testing. RESULTS: The dimensional complexity of the neonatal EEG increases with neurodevelopment and brain maturation. There is furthermore a statistically significant difference between the dimensional complexity of the EEG for neonates born prematurely when compared to full-term neonates at the same postmenstrual age (PMA). The neonatal EEG time series data used in this study proved to contain nonlinear features where the 'null hypothesis' of surrogate data testing is rejected with p<0.0001. CONCLUSIONS: A relationship between neurodevelopment and brain maturation and the complexity of the dynamics of the brain as measured by the dimensional complexity of the sleep EEG time series has been established. In particular, the dimensional complexity tends to increase with neurodevelopment and maturation as indicated by their PMA and birth status (premature or full-term). In particular, the brain dynamics of neonates born prematurely is less complex than the brain dynamics of neonates born full-term even at the same PMA. We attribute this to differences in the neurodevelopment between these two cohorts. We propose that the dimensional complexity can be used as an index for quantifying neurodevelopment. SIGNIFICANCE: The dimensional complexity as measured by the correlation dimension of the sleep EEG time series may potentially be a useful measure for quantifying neurodevelopment in neonates. Future work is directed at the analysis of other EEG channels to understand the relationship between complexity in different regions of the brain and maturation and neurodevelopment, along with the utility of complexity to relate to neurodevelopment at older ages as measured by the Bayley score.     

Global dynamical analysis of the EEG in Alzheimer's disease: frequency-specific changes of functional interactions.

OBJECTIVE: EEG coherence is decreased in Alzheimer's disease (AD), suggesting decreased interaction between brain areas. Nonlinear EEG analysis in AD points to decreased complexity of brain dynamics, implicating increased interaction. To clarify these apparently paradoxical findings from linear and nonlinear analysis, we calculated global coherence and global correlation dimension (D2), a nonlinear measure, in the EEG of patients with probable AD and controls. Our hypothesis is that these measures are related to each other when calculated in a comparable way. METHODS: From 15 patients with probable AD (mean age 63.1 years; SD 6.3) and 21 age-matched controls with subjective memory complaints (mean age 62.8; SD 12.0), band filtered EEG data were analysed in six frequency bands. For each frequency band average coherence and multichannel D2 were determined. RESULTS: ANOVA for repeated measures showed for D2 an interaction between band and group, but not for coherence. In the beta band and upper alpha band, D2 was higher in patients with probable AD compared to controls, while global coherence tended to be lower in these frequency bands in patients with probable AD. In the frequency range from theta to beta, coherence and D2 were inversely correlated without group differences. CONCLUSIONS: When calculated in comparable ways, global correlation dimension and coherence are related measures. In AD, these measures change especially in the higher frequency ranges, both pointing to decreased functional cortical connectivity. SIGNIFICANCE: Both global coherence and global correlation dimension seem to measure global connectivity, but nonlinear measures may be more sensitive. In AD, connectivity measures are not equally impaired in all frequency ranges, possibly reflecting differentiated affection of the dynamical processes responsible for the different frequency bands.     

Dimensional complexity of the EEG in patients with posttraumatic stress disorder

Recent electrophysiological studies have reported evidence of information processing abnormalities in patients with posttraumatic stress disorder (PTSD). The aim of this study is to examine dynamical complexity of the EEG in PTSD patients, which is thought to reflect information processing of the brain. Resting EEG recordings (32,800 data points acquired continuously from 82 s of an EEG record) were obtained in 16 channels of 27 patients with PTSD from a mixed civilian trauma population and 14 healthy subjects. The correlation dimension (D2) of the EEG was used to quantify the complexity of the cortical dynamics underlying the EEG signal. The PTSD patients were found to have lower D2 values than those of the healthy subjects in most channels (Fp1, F8, C4, P4, T3, T4, T5, T6, and O1), indicating that PTSD patients have globally reduced complexity in their EEG waveforms. This study supports the hypotheses that PTSD patients exhibit disturbed cortical information processing, and that non-linear dynamical analysis of the EEG can be a tool for detecting changes in neurodynamics of the brain in PTSD.     

Non-linear EEG analysis in children with epilepsy and electrical status epilepticus during slow-wave sleep (ESES)

OBJECTIVE: The objective of this work was to study the non-linear aspects of electroencephalography (EEG) in children with epilepsy and electrical status epilepticus during slow-wave sleep (ESES). METHODS: In this study, we recorded the sleep EEG in 5 subjects with ESES (4 males and one female, aged 6.5-10 years) who were also mentally retarded and affected by cerebral palsy (3 subjects) and hydrocephalus (two subjects). The signals were sampled at 128Hz and stored on hard disk. All the subsequent computational steps were performed on EEG epochs (4096 data points) selected from wakefulness and non-rapid eye movement (non-REM) (with ESES) or REM sleep. The dynamic properties of the EEG were assessed by means of the non-linear cross prediction (NLCP) test which uses 3 different 'model' time series in order to predict non-linearly the original data set (Pred, Ama and Tir). Pred is a measure of the predictability of the time series and Ama and Tir are measures of asymmetry, indicating non-linear structure. Moreover, the correlation dimension (D2) was estimated by means of the algorithm by for the epochs showing non-linear nature. RESULTS: The NLCP test provided evidence of significant non-linear dynamics in all epochs of non-REM sleep, when ESES was evident. Only during this stage, the possible presence of low-dimensional chaos could also be suspected (average D2=4.02; range 3.16-6.21). EEG without ESES could not be distinguished from linearly filtered noise. CONCLUSIONS: The results of the present study seem to indicate that subjects with ESES show a profound modification of their EEG dynamics with the occurrence, during sleep, of long periods characterized by non-linear dynamics and, probably, low-dimensional chaotic structure able to modify in a substantial way their brain functioning during sleep.     

Nonlinear analysis of electroencephalogram at rest and during cognitive tasks in patients with schizophrenia

Background

In spite of the large number of studies on schizophrenia, a full understanding of its core pathology still eludes us. The application of the nonlinear theory of electroencephalography (EEG) analysis provides an interesting tool to differentiate between physiologic conditions (e.g., resting state and mathematical task) and normal and pathologic brain activities. The aim of the present study was to investigate nonlinear EEG activity in patients with schizophrenia.

Methods

We recorded 19-lead EEGs in patients with stable schizophrenia and healthy controls under 4 different conditions: eyes closed, eyes open, forward counting and backward counting. A nonlinear measure of complexity was calculated by means of correlation dimension (D2).

Results

We included 17 patients and 17 controls in our analysis. Comparing the 2 populations, we observed greater D2 values in the patient group. In controls, increased D2 values were observed during active states (eyes open and the 2 cognitive tasks) compared with baseline conditions. This increase of brain complexity, which can be interpreted as an increase of information processing and integration, was not preserved in the patient population.

Limitations

Patients with schizophrenia were taking antipsychotic medications, so the presence of medication effects cannot be excluded.

Conclusion

Our results suggest that patients with schizophrenia present changes in brain activity compared with healthy controls, and this pathologic alteration can be successfully studied with nonlinear EEG analysis.     

Disturbed fluctuations of resting state EEG synchronization in Alzheimer's disease.

OBJECTIVE: We examined the hypothesis that cognitive dysfunction in Alzheimer's disease is associated with abnormal spontaneous fluctuations of EEG synchronization levels during an eyes-closed resting state. METHODS: EEGs were recorded during an eyes-closed resting state in Alzheimer patients (N=24; 9 males; mean age 76.3 years; SD 7.8; range 59-86) and non-demented subjects with subjective memory complaints (N=19; 9 males; mean age 76.1 years; SD 6.7; range: 67-89). The mean level of synchronization was determined in different frequency bands with the synchronization likelihood and fluctuations of the synchronization level were analysed with detrended fluctuation analysis (DFA). RESULTS: The mean level of EEG synchronization was lower in Alzheimer patients in the upper alpha (10-13Hz) and beta (13-30Hz) band. Spontaneous fluctuations of synchronization were diminished in Alzheimer patients in the lower alpha (8-10Hz) and beta bands. In patients as well as controls the synchronization fluctuations showed a scale-free pattern. CONCLUSIONS: Alzheimer's disease is characterized both by a lower mean level of functional connectivity as well as by diminished fluctuations in the level of synchronization. The dynamics of these fluctuations in patients and controls was scale-free which might point to self-organized criticality of neural networks in the brain. SIGNIFICANCE: Impaired functional connectivity can manifest itself not only in decreased levels of synchronization but also in disturbed fluctuations of synchronization levels.     

A neural complexity measure applied to MEG data in Alzheimer's disease.

OBJECTIVE: A measure of neural complexity (C(N)) (Proc. Natl Acad. Sci. USA 91 (1994) 5033) was applied to magnetoencephalography (MEG) data to test the hypothesis that C(N) decreases when information processing in the brain is impaired, as is the case in patients with Alzheimer's Disease (AD). METHODS: One hundred and fifty-one channel MEGs were recorded in 20 AD patients and 20 healthy age-matched controls in a resting condition with eyes open (EO) and eyes closed (EC). Artifact-free epochs of 117 channels were selected for analysis. C(N) and D(2) were computed in different frequency bands, and correlated with the MMSE. RESULTS: The Group x Frequency band interaction was significant for both C(N) and D(2). C(N) was higher in AD, as compared with controls, in the 2-4 and 4-8Hz bands, and D(2) was higher in AD patients in the 14-20 and 20-30Hz bands. The C(N) was higher in the EC condition compared to the EO condition, whereas the D(2) was higher in the EO condition. CONCLUSIONS: The hypothesis of Tononi et al. (Proc. Natl Acad. Sci. USA 91 (1994) 5033) that the neural complexity decreases in AD patients has to be rejected. However, both neural complexity and the correlation dimension did show differences between controls and AD patients which depended on frequency band.     

Sleep EEG in Korsakoff's psychosis and Alzheimer's disease

All-night sleep EEGs were compared in the following age-equivalent groups: seven men with Korsakoff's psychosis (KP), six men and two women with presumptive Alzheimer's disease, and six healthy men and three healthy women. KP patients had significantly increased intermittent time awake than both controls and Alzheimer patients, and shorter REM latency minus time awake than controls. Alzheimer patients had significantly reduced delta sleep than controls. Differences in the pattern of sleep EEG abnormalities may be due to different pathogenic mechanisms, but their utility in differential diagnosis requires further study.     

Nonlinear dynamical analysis of the neonatal EEG time series: The relationship between sleep state and complexity

OBJECTIVE: The complexity of the EEG time series during stages of neonatal sleep states is investigated. The relationship between these sleep states, birth status (i.e. preterm and full-term), and the complexity of the EEG is assessed. METHODS: Dimensional complexity, an estimate of the correlation dimension (D(2)) of the EEG time series, is used as a novel index for quantifying the complexity of the EEG time series during different neonatal sleep states. The dimensional complexity is estimated by using both the Grassberger-Procaccia algorithm and Theiler's modified algorithm. Also, the hypothesis that the neonatal EEG time series contains nonlinear features is investigated and verified in certain sleep states using surrogate data testing. RESULTS: Dimensional complexity of the neonatal EEG time series during active (REM) sleep tends to be higher than during quiet (NREM) sleep; while dimensional complexity of the neonatal EEG time series during indeterminate sleep is virtually at the midpoint of the dimensional complexity range between the active and quiet sleep states. Consequently, there are statistically significant differences between the neonatal EEG time series during different sleep states as measured by dimensional complexity. Also, the birth status (preterm or full-term) of the neonate has an influence on dimensional complexity of the neonatal EEG time series. Further, the surrogate data testing null hypothesis for dimensional complexity cannot be rejected during active sleep. CONCLUSIONS: The neonatal EEG time series tends to have statistically different complexities corresponding to different sleep states. Given that the neonatal EEG time series during active sleep is more complex than during quiet sleep, one can suggest that there is greater activity among cortical circuit elements during active as compared to quiet sleep. Further, active sleep neuronal dynamics are best modeled by a linear stochastic process, while in quiet sleep a nonlinear deterministic model may be more appropriate. SIGNIFICANCE: There has been considerable controversy associated with measures of complexity, such as dimensional analysis, as applied to neonatal EEG data. This paper confirms that there are statistically significant differences in dimensional complexity associated with different states of sleep and that the origin of this complexity shifts from linear stochastic to nonlinear deterministic with transitions from active to quiet sleep, and is further influenced by maturation. This may provide important insight into the organization and structure of neuronal networks during sleep and with brain maturation in the neonatal period.     

Detection and description of non-linear interdependence in normal multichannel human EEG data.

OBJECTIVES: This study examines human scalp electroencephalographic (EEG) data for evidence of non-linear interdependence between posterior channels. The spectral and phase properties of those epochs of EEG exhibiting non-linear interdependence are studied. METHODS: Scalp EEG data was collected from 40 healthy subjects. A technique for the detection of non-linear interdependence was applied to 2.048 s segments of posterior bipolar electrode data. Amplitude-adjusted phase-randomized surrogate data was used to statistically determine which EEG epochs exhibited non-linear interdependence. RESULTS: Statistically significant evidence of non-linear interactions were evident in 2.9% (eyes open) to 4.8% (eyes closed) of the epochs. In the eyes-open recordings, these epochs exhibited a peak in the spectral and cross-spectral density functions at about 10 Hz. Two types of EEG epochs are evident in the eyes-closed recordings; one type exhibits a peak in the spectral density and cross-spectrum at 8 Hz. The other type has increased spectral and cross-spectral power across faster frequencies. Epochs identified as exhibiting non-linear interdependence display a tendency towards phase interdependencies across and between a broad range of frequencies. CONCLUSIONS: Non-linear interdependence is detectable in a small number of multichannel EEG epochs, and makes a contribution to the alpha rhythm. Non-linear interdependence produces spatially distributed activity that exhibits phase synchronization between oscillations present at different frequencies. The possible physiological significance of these findings are discussed with reference to the dynamical properties of neural systems and the role of synchronous activity in the neocortex.     

失眠症患者的脑电非线性动力学初步研究

目的探讨失眠症患者在不同认知作业状态下脑电非线性动力学的初步特点。方法应用日本光电1518K脑电图和地形图系统,以及关联维数（D2）、点关联维数（PD2）方法对75例失眠症和64名正常成人在安静闭眼、安静睁眼、闭眼心算作业、睁眼记忆和数字划消五种状态下的脑电数据进行了分析。结果 （1）在D2和PD2上,正常和失眠症睁闭眼实验中差异均未达到显著性。（2）在D2上,与正常对照组比较,失眠症在心算和数字划消两个认知心理测试中均见降低。（3）在PD2上,与正常对照组比较,失眠症在心算、汉字记忆和数字划消三个认知心理测试中也见降低（P〈0.05或P〈0.01）。结论非线性动力学分析为临床提供了一种处理分析脑电信号的新思路,有助于进一步了解失眠症认知过程中大脑的工作机制。     

EEG coherence reflects regional corpus callosum area in Alzheimer's disease

OBJECTIVE: Correlations between corpus callosum size and interhemispheric EEG coherence were investigated as measures of interhemispheric connectivity in patients with Alzheimer's disease. METHODS: 11 patients underwent both magnetic resonance imaging and quantitative electroencephalography to assess corpus callosum size and interhemispheric coherence. For comparison, corpus callosum size was measured in 24 healthy elderly control subjects. RESULTS: Corpus callosum cross sectional area was significantly reduced in Alzheimer patients relative to controls. Posterior interhemispheric coherence (alpha and beta frequencies) correlated significantly with the size of posterior corpus callosum area, and anterior coherence (delta, theta, and alpha frequencies) with the size of anterior corpus callosum area in the Alzheimer patients. CONCLUSION: Region specific correlations between corpus callosum size and EEG coherence suggest that the decline in interhemispheric connectivity in Alzheimer's disease results from a specific loss of cortical association neurones projecting through the corpus callosum.     

Quantitative EEG analysis in Alzheimer's disease: spectral, coherence and complexity parameters

In our study we examined the linear and non-linear characteristics of EEG signals derived from patients with Alzheimer's disease (AD) with the future aim of developing a widely available method for monitoring therapy and the progression of the disease or to be used even for the purposes of differential diagnosis. EEG was recorded with eyes closed and eyes open conditions ("resting") in a group of patients with early-stage AD and in healthy control subjects matched by age. In addition to the conventional methods of analysis (frequency spectrum, coherence), the so-called complexity measures developed in recent years (Omega-complexity, synchronised probability) have also been determined. By means of frequency spectrum analysis, we managed to detect the slowdown of EEG in the early stage of dementia, a feature that so far has been associated with the later stages of AD. Coherence was reduced in the majority of frequency bands in the patient group; however, this difference could be observed only in some of the leads. Thus, resting EEG coherence is less suitable for separating various stages than the other methods. Complexity features have shown the most robust changes in Alzheimer's disease in our investigation. Besides the reduction in synchronised probability, significantly higher values of Omega-complexity were obtained in the patient group. This may be associated with the impairment of cortical afferentation (cholinergic and monoaminergic) and with the reduction in the number of neurons and synapses. Our methods have proved to be very sensitive to quantify these changes.     

EEG correlates in the spectrum of cognitive decline.

OBJECTIVE: To investigate relations between EEG measures and performance on tests of global cognition, memory, language and executive functioning. METHODS: Twenty-two controls, 18 patients with mild cognitive impairment (MCI) and 16 with probable Alzheimer's disease (AD) underwent neuropsychological and EEG investigations. We used the following EEG measures: theta relative power during eyes closed, alpha reactivity during memory activation (i.e. the percentual decrease in alpha power as compared to eyes closed) and alpha coherence during eyes closed and memory activation. RESULTS: Theta relative power was increased in AD patients as compared with controls (p<0.001) and MCI patients (p<0.01) and related to decreased performance in all cognitive domains. Alpha reactivity was decreased in AD patients as compared with controls (p<0.005) and related to decreased performance on tests of global cognition, memory and executive functioning. Alpha coherence did not differ between groups and was unrelated to cognition. CONCLUSIONS: EEG power measures were associated with decreased performance on tests of global cognition, memory, language and executive functioning, while coherence measures were not. SIGNIFICANCE: The EEG yielded several power measures related to cognitive functions. These EEG power measures might prove useful in prospective studies aimed at predicting longitudinal cognitive decline and dementia.