EEG Complexity Research Articles

Complexity of resting-state EEG activity in the patients with early-stage Parkinson's disease.

To investigate the abnormal brain activities in the early stage of Parkinson's disease (PD), the electroencephalogram (EEG) signals were recorded with 20 channels from non-dementia PD patients (18 patients, 8 females) and age matched healthy controls (18 subjects, 8 females) during the resting state. Two methods based on the ordinal patterns of the recorded series, i.e., permutation entropy (PE) and order index (OI), were introduced to characterize the complexity of the cortical activities for two groups. It was observed that the resting-state EEG of PD patients showed lower PE and higher OI than healthy controls, which indicated that the early-stage PD caused the reduced complexity of EEG. We further applied two methods to determine the complexity of EEG rhythms in five sub-bands. The results showed that the gamma, beta and alpha rhythms of PD patients were characterized by lower PE and higher OI, i.e., reduced complexity, than healthy subjects. No significant differences were observed in theta or delta rhythms between two groups. The findings suggested that PE and OI were promising methods to detect the abnormal changes in the dynamics of EEG signals associated with early-stage PD. Further, such changes in EEG complexity may be the early markers of the cortical or subcortical dysfunction caused by PD.

Short-term Music Training Enhances Complex, Distributed Neural Communication during Music and Linguistic Tasks.

Musical training is frequently associated with benefits to linguistic abilities, and recent focus has been placed on possible benefits of bilingualism to lifelong executive functions; however, the neural mechanisms for such effects are unclear. The aim of this study was to gain better understanding of the whole-brain functional effects of music and second-language training that could support such previously observed cognitive transfer effects. We conducted a 28-day longitudinal study of monolingual English-speaking 4- to 6-year-old children randomly selected to receive daily music or French language training, excluding weekends. Children completed passive EEG music note and French vowel auditory oddball detection tasks before and after training. Brain signal complexity was measured on source waveforms at multiple temporal scales as an index of neural information processing and network communication load. Comparing pretraining with posttraining, musical training was associated with increased EEG complexity at coarse temporal scales during the music and French vowel tasks in widely distributed cortical regions. Conversely, very minimal decreases in complexity at fine scales and trends toward coarse-scale increases were displayed after French training during the tasks. Spectral analysis failed to distinguish between training types and found overall theta (3.5-7.5 Hz) power increases after all training forms, with spatially fewer decreases in power at higher frequencies (>10 Hz). These findings demonstrate that musical training increased diversity of brain network states to support domain-specific music skill acquisition and music-to-language transfer effects.

Characterization of EEG patterns in brain-injured subjects and controls after a Snoezelen® intervention

Background and objectiveThe aim of this study was to assess the changes induced in electroencephalographic (EEG) activity by a Snoezelen® intervention on individuals with brain-injury and control subjects. MethodsEEG activity was recorded preceding and following a Snoezelen® session in 18 people with cerebral palsy (CP), 18 subjects who have sustained traumatic brain-injury (TBI) and 18 controls. EEG data were analyzed by means of spectral and nonlinear measures: median frequency (MF), individual alpha frequency (IAF), sample entropy (SampEn) and Lempel-Ziv complexity (LZC). ResultsOur results showed decreased values for MF, IAF, SampEn and LZC as a consequence of the therapy. The main changes between pre-stimulation and post-stimulation conditions were found in occipital and parietal brain areas. Additionally, these changes are more widespread in controls than in brain-injured subjects, which can be due to cognitive deficits in TBI and CP groups. ConclusionsOur findings support the notion that Snoezelen® therapy affects central nervous system, inducing a slowing of oscillatory activity, as well as a decrease of EEG complexity and irregularity. These alterations seem to be related with higher levels of relaxation of the participants.

Variations of EEG complexity across the 24 hr-cycle in patients with disorders of consciousness

Variations of EEG complexity across the 24 hr-cycle in patients with disorders of consciousness

Developmental differences of dimension complexity of hippocampal EEG during REM sleep.

This paper uses spectral analysis and correlation dimension index to examine the developmental differences between the EEG measured from two hippocampal subfields, CA1 and the dentate gyrus. The study was focused on the hippocampal EEG during the vigilance state of REM sleep in freely moving rats of 15 and 90 days of age. Power spectra, magnitude-squared coherence, and correlation dimension were estimated. The correlation dimension adds a new interpretation from the nonlinear dynamics perspective, and we found that there are no significant developmental differences between the granule cells of the dentate gyrus and the pyramidal cells of area CA1 when the theta activity was present during REM sleep.

Analysis of Epileptic Discharges from Implanted Subdural Electrodes in Patients with Sturge-Weber Syndrome.

ObjectiveAlmost two-thirds of patients with Sturge-Weber syndrome (SWS) have epilepsy, and half of them require surgery for it. However, it is well known that scalp electroencephalography (EEG) does not demonstrate unequivocal epileptic discharges in patients with SWS. Therefore, we analyzed interictal and ictal discharges from intracranial subdural EEG recordings in patients treated surgically for SWS to elucidate epileptogenicity in this disorder.MethodsFive intractable epileptic patients with SWS who were implanted with subdural electrodes for presurgical evaluation were enrolled in this study. We examined the following seizure parameters: seizure onset zone (SOZ), propagation speed of seizure discharges, and seizure duration by visual inspection. Additionally, power spectrogram analysis on some frequency bands at SOZ was performed from 60 s before the visually detected seizure onset using the EEG Complex Demodulation Method (CDM).ResultsWe obtained 21 seizures from five patients for evaluation, and all seizures initiated from the cortex under the leptomeningeal angioma. Most of the patients presented with motionless staring and respiratory distress as seizure symptoms. The average seizure propagation speed and duration were 3.1 ± 3.6 cm/min and 19.4 ± 33.6 min, respectively. Significant power spectrogram changes at the SOZ were detected at 10–30 Hz from 15 s before seizure onset, and at 30–80 Hz from 5 s before seizure onset.SignificanceIn patients with SWS, seizures initiate from the cortex under the leptomeningeal angioma, and seizure propagation is slow and persists for a longer period. CDM indicated beta to low gamma-ranged seizure discharges starting from shortly before the visually detected seizure onset. Our ECoG findings indicate that ischemia is a principal mechanism underlying ictogenesis and epileptogenesis in SWS.

Neurocomputational Model of EEG Complexity during Mind Wandering.

Mind wandering (MW) can be understood as a transient state in which attention drifts from an external task to internal self-generated thoughts. MW has been associated with the activation of the Default Mode Network (DMN). In addition, it has been shown that the activity of the DMN is anti-correlated with activation in brain networks related to the processing of external events (e.g., Salience network, SN). In this study, we present a mean field model based on weakly coupled Kuramoto oscillators. We simulated the oscillatory activity of the entire brain and explored the role of the interaction between the nodes from the DMN and SN in MW states. External stimulation was added to the network model in two opposite conditions. Stimuli could be presented when oscillators in the SN showed more internal coherence (synchrony) than in the DMN, or, on the contrary, when the coherence in the SN was lower than in the DMN. The resulting phases of the oscillators were analyzed and used to simulate EEG signals. Our results showed that the structural complexity from both simulated and real data was higher when the model was stimulated during periods in which DMN was more coherent than the SN. Overall, our results provided a plausible mechanistic explanation to MW as a state in which high coherence in the DMN partially suppresses the capacity of the system to process external stimuli.

Preliminary study of Alzheimer's Disease diagnosis based on brain electrical signals using wireless EEG

This research aims to study brain's electrical signals recorded using EEG as a basis for the diagnosis of patients with Alzheimer's Disease (AD). The subjects consisted of patients with AD, and normal subjects are used as the control. Brain signals are recorded for 3 minutes in a relaxed condition and with eyes closed. The data is processed using power spectral analysis, brain mapping and chaos test to observe the level of complexity of EEG's data. The results show a shift in the power spectral in the low frequency band (delta and theta) in AD patients. The increase of delta and theta occurs in lobus frontal area and lobus parietal respectively. However, there is a decrease of alpha activity in AD patients where in the case of normal subjects with relaxed condition, brain alpha wave dominates the posterior area. This is confirmed by the results of brain mapping. While the results of chaos analysis show that the average value of MMLE is lower in AD patients than in normal subjects. The level of chaos associated with neural complexity in AD patients with lower neural complexity is due to neuronal damage caused by the beta amyloid plaques and tau protein in neurons.

Analysis of the Complexity Measures in the EEG of Schizophrenia Patients.

Complexity measures have been enormously used in schizophrenia patients to estimate brain dynamics. However, the conflicting results in terms of both increased and reduced complexity values have been reported in these studies depending on the patients' clinical status or symptom severity or medication and age status. The objective of this study is to investigate the nonlinear brain dynamics of chronic and medicated schizophrenia patients using distinct complexity estimators. EEG data were collected from 22 relaxed eyes-closed patients and age-matched healthy controls. A single-trial EEG series of 2 min was partitioned into identical epochs of 20 s intervals. The EEG complexity of participants were investigated and compared using approximate entropy (ApEn), Shannon entropy (ShEn), Kolmogorov complexity (KC) and Lempel-Ziv complexity (LZC). Lower complexity values were obtained in schizophrenia patients. The most significant complexity differences between patients and controls were obtained in especially left frontal (F3) and parietal (P3) regions of the brain when all complexity measures were applied individually. Significantly, we found that KC was more sensitive for detecting EEG complexity of patients than other estimators in all investigated brain regions. Moreover, significant inter-hemispheric complexity differences were found in the frontal and parietal areas of schizophrenics' brain. Our findings demonstrate that the utilizing of sensitive complexity estimators to analyze brain dynamics of patients might be a useful discriminative tool for diagnostic purposes. Therefore, we expect that nonlinear analysis will give us deeper understanding of schizophrenics' brain.

Revisiting the Heidenhain Variant of Creutzfeldt-Jakob Disease: Evidence for Prion Type Variability Influencing Clinical Course and Laboratory Findings.

The Heidenhain variant defines a peculiar clinical presentation of sporadic Creutzfeldt-Jakob disease (sCJD) characterized by isolated visual disturbances at disease onset and reflecting the early targeting of prions to the occipital cortex. Molecular and histopathological typing, thus far performed in 23 cases, has linked the Heidenhain variant to the MM1 sCJD type. To contribute a comprehensive characterization of cases with the Heidenhain variant, we reviewed a series of 370 definite sCJD cases. Eighteen patients (4.9%) fulfilled the selection criteria. Fourteen of them belonging to sCJD types MM1 or MM1+2C had a short duration of isolated visual symptoms and overall clinical disease, a high prevalence of periodic sharp-wave complexes in EEG, and a marked increase of cerebrospinal fluid proteins t-tau and 14-3-3 levels. In contrast, three cases of the MM 2C or MM 2+1C types showed a longer duration of isolated visual symptoms and overall clinical disease, non-specific EEG findings, and cerebrospinal fluid concentration below threshold for the diagnosis of “probable” CJD of both 14-3-3 and t-tau. However, a brain DWI-MRI disclosed an occipital cortical hyperintensity in the majority of examined cases of both groups. While confirming the strong linkage with the methionine genotype at the polymorphic codon 129 of the prion protein gene, our results definitely establish that the Heidenhain variant can also be associated with the MM 2C sCJD type in addition to the more common MM1 type. Likewise, our results highlight the significant differences in clinical evolution and laboratory findings between cases according to the dominant PrPSc type (type 1 versus type 2).

Abnormal EEG Complexity and Functional Connectivity of Brain in Patients with Acute Thalamic Ischemic Stroke.

Ischemic thalamus stroke has become a serious cardiovascular and cerebral disease in recent years. To date the existing researches mostly concentrated on the power spectral density (PSD) in several frequency bands. In this paper, we investigated the nonlinear features of EEG and brain functional connectivity in patients with acute thalamic ischemic stroke and healthy subjects. Electroencephalography (EEG) in resting condition with eyes closed was recorded for 12 stroke patients and 11 healthy subjects as control group. Lempel-Ziv complexity (LZC), Sample Entropy (SampEn), and brain network using partial directed coherence (PDC) were calculated for feature extraction. Results showed that patients had increased mean LZC and SampEn than the controls, which implied the stroke group has higher EEG complexity. For the brain network, the stroke group displayed a trend of weaker cortical connectivity, which suggests a functional impairment of information transmission in cortical connections in stroke patients. These findings suggest that nonlinear analysis and brain network could provide essential information for better understanding the brain dysfunction in the stroke and assisting monitoring or prognostication of stroke evolution.

EEG complexity and frequency in chronic residual schizophrenia

Objective: Some studies on schizophrenia showed an increased complexity in electroencephalography (EEG) whereas others detected a decreased complexity. Because this discrepancy might be due to the clinical features or complexity measures used, we employed two different complexity measures in a group of schizophrenics similar in illness duration (chronic) and symptom profile (residual). Methods: Right-handed chronic residual schizophrenic patients (10 male, 10 female) and age- and sex-matched 20 healthy controls were included in the study. Eyes-closed resting EEG series were measured through quantitative EEG band activities, the log energy entropy (LEE) values, and the Hurst exponents (HE) of EEG measurements were computed for each electrode site. Results: Significantly higher LEE values in the prefrontal, frontal, temporal and parietal locations were observed in schizophrenic patients compared with controls. HE values were significantly higher on the right frontal area in the schizophrenics. Patient group showed increased prefrontal, frontal and parietal delta activity, prefrontal, left temporal and right parietal theta activity and increased left temporal alpha activity. Discussion: In the present study, we found that chronic residual schizophrenia is associated with decreased complexity and increased smoothness in EEG. In addition, EEG of patients was characterized by obvious slowness at prefrontal and frontotemporal regions, dominantly. An integration of EEG complexity and frequency analysis can be proposed as an innovative tool in schizophrenia research.

Analysing complexity, variability and spectral measures of schizophrenic EEG signal

Symptoms, signs and disease progression are the mainstay of psychiatric disorders diagnosis but defining a biomarker would be a more accurate way for their diagnosis in future. Electroencephalogram (EEG) could be useful in identifying specific biomarkers for diagnosis severe psychiatric disorders. This study is aimed to compare three EEG analysis methods, complexity, variability and spectral measures for classification of schizophrenic and normal participants. Fifteen schizophrenic and 18 age-matched normal subjects participated in this study. For each case, 20 channels of EEG are recorded. The extracted features include two spectral measures such as spectral entropy (SpEn) and Reyni's entropy (ReEn), two complexity measures such as approximate entropy (ApEn) and Lempel-Ziv complexity (LZC) and a variability measure such as central tendency measure (CTM). Finally, k-nearest neighbour (k-NN) is used to classify two mentioned groups. Our results show the classification accuracy of 94% using leave-one (participant)-out cross validation, which improves the previous results in this manner and also simplifies the method. The result indicates the suggested features have a good sensitivity for detection of characteristic features of schizophrenia disorder.

Sharp-wave EEG abnormalities and neural complexity in depressive patients: preliminary report

According to recent findings cognitive and emotional dysregulation in depression and psychiatric disorders may be related to deficits in inhibitory functions and temporo-limbic seizure-like activity due to specific changes in neural dynamics and complexity of neural networks. In order to evaluate the clinical utility of nonlinear analysis of EEG complexity (measured by Lyapunov exponents) and to assess underlying epileptiform changes, we studied a sample of 19 depressive patients including nine depressive patients with episodic sharp-wave EEG abnormalities and ten depressive patients without any abnormalities, and compared also subgroups of these patients who used benzodiazepine medication to assess its influence on EEG complexity. The results show that the depressive patients with episodic sharp-wave EEG abnormalities had significantly lower EEG complexity than the control groups of patients. The data also indicate that benzodiazepines significantly influence neural complexity and increase it in the subgroup of patients with sharp wave abnormalities, and on the other hand decrease the level of complexity in the control subgroup of depressive patients. In agreement with other findings results of this study suggest that sharp waves or other underlying epileptiform EEG abnormalities related to abnormal neural excitability and disturbances in brain inhibitory systems may be reflected in specific changes of EEG complexity. These specific changes in EEG complexity may be related to treatment resistance to usual medications in several depressive patients and might represent clinical indicators for anticonvulsant treatment.

Complexity of Multi-Dimensional Spontaneous EEG Decreases during Propofol Induced General Anaesthesia.

Emerging neural theories of consciousness suggest a correlation between a specific type of neural dynamical complexity and the level of consciousness: When awake and aware, causal interactions between brain regions are both integrated (all regions are to a certain extent connected) and differentiated (there is inhomogeneity and variety in the interactions). In support of this, recent work by Casali et al (2013) has shown that Lempel-Ziv complexity correlates strongly with conscious level, when computed on the EEG response to transcranial magnetic stimulation. Here we investigated complexity of spontaneous high-density EEG data during propofol-induced general anaesthesia. We consider three distinct measures: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability in the constitution of the set of active channels; and (iii) the novel synchrony coalition entropy (SCE), which measures the variability in the constitution of the set of synchronous channels. After some simulations on Kuramoto oscillator models which demonstrate that these measures capture distinct ‘flavours’ of complexity, we show that there is a robustly measurable decrease in the complexity of spontaneous EEG during general anaesthesia.

Complexity of Multi-Channel Electroencephalogram Signal Analysis in Childhood Absence Epilepsy.

Absence epilepsy is an important epileptic syndrome in children. Multiscale entropy (MSE), an entropy-based method to measure dynamic complexity at multiple temporal scales, is helpful to disclose the information of brain connectivity. This study investigated the complexity of electroencephalogram (EEG) signals using MSE in children with absence epilepsy. In this research, EEG signals from 19 channels of the entire brain in 21 children aged 5-12 years with absence epilepsy were analyzed. The EEG signals of pre-ictal (before seizure) and ictal states (during seizure) were analyzed by sample entropy (SamEn) and MSE methods. Variations of complexity index (CI), which was calculated from MSE, from the pre-ictal to the ictal states were also analyzed. The entropy values in the pre-ictal state were significantly higher than those in the ictal state. The MSE revealed more differences in analysis compared to the SamEn. The occurrence of absence seizures decreased the CI in all channels. Changes in CI were also significantly greater in the frontal and central parts of the brain, indicating fronto-central cortical involvement of “cortico-thalamo-cortical network” in the occurrence of generalized spike and wave discharges during absence seizures. Moreover, higher sampling frequency was more sensitive in detecting functional changes in the ictal state. There was significantly higher correlation in ictal states in the same patient in different seizures but there were great differences in CI among different patients, indicating that CI changes were consistent in different absence seizures in the same patient but not from patient to patient. This implies that the brain stays in a homogeneous activation state during the absence seizures. In conclusion, MSE analysis is better than SamEn analysis to analyze complexity of EEG, and CI can be used to investigate the functional brain changes during absence seizures.

Nonlinear analysis of EEG in major depression with fractal dimensions.

Major depressive disorder (MDD) is a psychiatric mood disorder characterized by cognitive and functional impairments in attention, concentration, learning and memory. In order to investigate and understand its underlying neural activities and pathophysiology, EEG methodologies can be used. In this study, we estimated the nonlinearity features of EEG in MDD patients to assess the dynamical properties underlying the frontal and parietal brain activity. EEG data were obtained from 16 patients and 15 matched healthy controls. A wavelet-chaos methodology was used for data analysis. First, EEGs of subjects were decomposed into 5 EEG sub-bands by discrete wavelet transform. Then, both the Katz's and Higuchi's fractal dimensions (KFD and HFD) were calculated as complexity measures for full-band and sub-bands EEGs. Last, two-way analyses of variances were used to test EEG complexity differences on each fractality measures. As a result, a significantly increased complexity was found in both parietal and frontal regions of MDD patients. This significantly increased complexity was observed not only in full-band activity but also in beta and gamma sub-bands of EEG. The findings of the present study indicate the possibility of using the wavelet-chaos methodology to discriminate the EEGs of MDD patients from healthy controls.

Memory load effect in auditory-verbal short-term memory task: EEG fractal and spectral analysis.

The objective of this preliminary study was to quantify changes in complexity of EEG using fractal dimension (FD) alongside linear methods of spectral power, event-related spectral perturbations, coherence, and source localization of EEG generators for theta (4-7 Hz), alpha (8-12 Hz), and beta (13-23 Hz) frequency bands due to a memory load effect in an auditory-verbal short-term memory (AVSTM) task for words. We examined 20 healthy individuals using the Sternberg's paradigm with increasing memory load (three, five, and seven words). The stimuli were four-letter words. Artifact-free 5-s EEG segments during retention period were analyzed. The most significant finding was the increase in FD with the increase in memory load in temporal regions T3 and T4, and in parietal region Pz, while decrease in FD with increase in memory load was registered in frontal midline region Fz. Results point to increase in frontal midline (Fz) theta spectral power, decrease in alpha spectral power in parietal region-Pz, and increase in beta spectral power in T3 and T4 region with increase in memory load. Decrease in theta coherence within right hemisphere due to memory load was obtained. Alpha coherence increased in posterior regions with anterior decrease. Beta coherence increased in fronto-temporal regions. Source localization delineated theta activity increase in frontal midline region, alpha decrease in superior parietal region, and beta increase in superior temporal gyrus with increase in memory load. In conclusion, FD as a nonlinear measure may serve as a sensitive index for quantifying dynamical changes in EEG signals during AVSTM tasks.

Concurrent brain responses to separate auditory and visual targets.

In the attentional blink, a target event (T1) strongly interferes with perception of a second target (T2) presented within a few hundred milliseconds. Concurrently, the brain's electromagnetic response to the second target is suppressed, especially a late negative-positive EEG complex including the traditional P3 wave. An influential theory proposes that conscious perception requires access to a distributed, frontoparietal global workspace, explaining the attentional blink by strong mutual inhibition between concurrent workspace representations. Often, however, the attentional blink is reduced or eliminated for targets in different sensory modalities, suggesting a limit to such global inhibition. Using functional magnetic resonance imaging, we confirm that visual and auditory targets produce similar, distributed patterns of frontoparietal activity. In an attentional blink EEG/MEG design, however, an auditory T1 and visual T2 are identified without mutual interference, with largely preserved electromagnetic responses to T2. The results suggest parallel brain responses to target events in different sensory modalities.

Interplay between impaired brain structure and function in term newborns with congenital heart disease

Background: Term neonates with congenital heart disease (CHD) demonstrate a high incidence of white matter injury (WMI), together with increased average diffusivity and decreased fractional anisotropy (FA) on MR diffusion tensor imaging. EEG background activity is a robust measure of functional brain maturation that becomes less discontinuous, contains more fast activity and shows higher complexity of EEG patterns with increasing age. We sought to determine the association between structural brain abnormalities and functional brain maturation in term neonates with CHD. Methods: Thirteen term newborns with CHD underwent pre-operative MR imaging and continuous EEG recordings (cEEG). The proportion of cEEG with discontinuous vs. continuous background activity was quantified by visual analysis. During continuous epochs, we differentiated between two states: wakefulness/active sleep vs. quiet/transitional sleep, and applied algorithms to measure spectral power and EEG complexity. Results: Three patients had multifocal WMI which was associated with greater EEG background discontinuity (P<0.05). Moreover, lower white matter diffusivity was associated with higher power of fast activity (P<0.05 for wakefulness/active sleep EEG pattern), while higher white matter FA showed a trend toward being associated with increased EEG complexity (P<0.1 for quiet/transitional sleep pattern). Conclusions: In this series of term neonates with CHD, structural and microstructural white matter abnormalities are associated with impaired maturation of brain function.