QEEG Method Research Articles

Advanced qEEG analyses discriminate between dementia subtypes

BackgroundDementia is caused by neurodegenerative conditions and characterized by cognitive decline. Diagnostic accuracy for dementia subtypes, such as Alzheimer’s Disease (AD), Dementia with Lewy Bodies (DLB) and Parkinson’s Disease with dementia (PDD), remains challenging. MethodsHere, different methods of quantitative electroencephalography (qEEG) analyses were employed to assess their effectiveness in distinguishing dementia subtypes from healthy controls under eyes closed (EC) and eyes open (EO) conditions. ResultsClassic Fast-Fourier Transform (FFT) and autoregressive (AR) power analyses differentiated between all conditions for the 4–8 Hz theta range. Only individuals with dementia with Lewy Bodies (DLB) differed from healthy subjects for the wider 4–15 Hz frequency range, encompassing the actual dominant frequency of all individuals. FFT results for this range yielded wider significant discriminators vs AR, also detecting differences between AD and DLB. Analyses of the inclusive dominant / peak frequency range (4–15 Hz) indicated slowing and reduced variability, also discriminating between synucleinopathies vs controls and AD. Dissociation of periodic oscillations and aperiodic components of AR spectra using Fitting-Oscillations-&-One-Over-F (FOOOF) modelling delivered a reliable and subtype-specific slowing of brain oscillatory peaks during EC and EO for all groups. Distinct and robust differences were particularly strong for aperiodic parameters, suggesting their potential diagnostic power in detecting specific changes resulting from age and cognitive status. ConclusionOur findings indicate that qEEG methods can reliably detect dementia subtypes. Spectral analyses comprising an integrated, multi-parameter EEG approach discriminating between periodic and aperiodic components under EC and EO conditions may enhance diagnostic accuracy in the future.

Quantitative Electroencephalography Objectivity and Reliability in the Diagnosis and Management of Traumatic Brain Injury: A Systematic Review.

Background. Persons with a history of traumatic brain injury (TBI) may exhibit short- and long-term cognitive deficits as well as psychiatric symptoms. These symptoms often reflect functional anomalies in the brain that are not detected by standard neuroimaging. In this context, quantitative electroencephalography (qEEG) is more suitable to evaluate non-normative activity in a wide range of clinical settings. Method. We searched the literature using the "Medline" and "Web of Science" online databases. The search was concluded on February 23, 2023, and revised on July 12, 2023. It returned 134 results from Medline and 4 from Web of Science. We then applied the PRISMA method, which led to the selection of 31 articles, the most recent one published in March 2023. Results. The qEEG method can detect functional anomalies in the brain occurring immediately after and even years after injury, revealing in most cases abnormal power variability and increases in slow (delta and theta) versus decreases in fast (alpha, beta, and gamma) frequency activity. Moreover, other findings show that reduced beta coherence between frontoparietal regions is associated with slower processing speed in patients with recent mild TBI (mTBI). More recently, machine learning (ML) research has developed highly reliable models and algorithms for the detection of TBI, some of which are already integrated into commercial qEEG equipment. Conclusion. Accumulating evidence indicates that the qEEG method may improve the diagnosis and management of TBI, in many cases revealing long-term functional anomalies in the brain or even neuroanatomical insults that are not revealed by standard neuroimaging. While FDA clearance has been obtained only for some of the commercially available equipment, the qEEG method allows for systematic, cost-effective, non-invasive, and reliable investigations at emergency departments. Importantly, the automated implementation of intelligent algorithms based on multimodally acquired, clinically relevant measures may play a key role in increasing diagnosis reliability.

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal.

Dementia is a term that represents a set of symptoms that affect the ability of the brain's cognitive functions related to memory, thinking, behavior, and language. At worst, dementia is often called a major neurocognitive disorder or senile disease. One of the most common types of dementia after Alzheimer's is vascular dementia. Vascular dementia is closely related to cerebrovascular disease, one of which is stroke. Post-stroke patients with recurrent onset have the potential to develop dementia. An accurate diagnosis is needed for proper therapy management to ensure the patient's quality of life and prevent it from worsening. The gold standard diagnostic of vascular dementia is complex, includes psychological tests, complete memory tests, and is evidenced by medical imaging of brain lesions. However, brain imaging methods such as CT-Scan, PET-Scan, and MRI have high costs and cannot be routinely used in a short period. For more than two decades, electroencephalogram signal analysis has been an alternative in assisting the diagnosis of brain diseases associated with cognitive decline. Traditional EEG analysis performs visual observations of signals, including rhythm, power, and spikes. Of course, it requires a clinician expert, time consumption, and high costs. Therefore, a quantitative EEG method for identifying vascular dementia in post-stroke patients is discussed in this study. This study used 19 EEG channels recorded from normal elderly, post-stroke with mild cognitive impairment, and post-stroke with dementia. The QEEG method used for feature extraction includes relative power, coherence, and signal complexity; the evaluation performance of normal-mild cognitive impairment-dementia classification was conducted using Support Vector Machine and K-Nearest Neighbor. The results of the classification simulation showed the highest accuracy of 96% by Gaussian SVM with a sensitivity and specificity of 95.6% and 97.9%, respectively. This study is expected to be an additional criterion in the diagnosis of dementia, especially in post-stroke patients.

Didactic Significance of Modern Simulation Programs in Vocational Education – Divagations from Own Research

Simulation programs in polytechnic (vocational) education have an important didactic function. On the one hand, they are a great example of modern didactic software, on the other hand, knowledge of the simulation software environment by a future graduate of a technical university increases the qualifications of the future employee and allows him to acquire an attractive profession. In the era of high requirements set by employers and competition on the education market, their knowledge seems highly justified. However, whether simulation programs, especially deterministic ones, can also act as teaching aids is not so obvious. In this article, in addition to purely theoretical considerations, will be presented stages of own research on didactic efficiency, or more precisely the impact of this group of software on the cognitive process during learning. More importantly, the didactic effectiveness was tested based on typical pedagogical research as well as by means of electroencephalographic tests using the QEEG method.

Quantitative Electroencephalography Analyzed by Statistical Pattern Recognition as a Diagnostic and Prognostic Tool in Mild Cognitive Impairment: Results from a Nordic Multicenter Cohort Study

Aim: To examine diagnostic and prognostic potential of quantitative electroencephalography (qEEG) analyzed by the statistical pattern recognition (SPR) method in patients with cognitive impairment. We compared the differential diagnostic ability of SPR to visual EEG analysis. Correlation between SPR findings and cerebrospinal fluid (CSF) Alzheimer disease (AD) biomarkers were evaluated. Methods: It is a multicenter cohort study involving 129 patients, (mild cognitive impairment [MCI], AD, and healthy controls). Standardized EEG was performed at baseline. Patients were continuously clinically evaluated. Results: Receiver Operating Characteristic curves showed a low discriminative ability of SPR and no ability to predict clinical progression in patients with MCI. Moderate correlation between SPR analysis and CSF AD biomarkers was found. Conclusion: The diagnostic and prognostic abilities of qEEG were low. The SPR method was superior to the visual EEG analysis. The qEEG method correlates well to CSF AD biomarkers, suggesting association with pathology in AD.

Initial Study on Cancer-Related Cognitive Dysfunction With the Implementation of QEEG

Abstract Chemotherapy is one of the most common treatments used in cancer therapy despite its serious side effects, which remain a huge concern. To the most common side effects (apart from weight and hair loss) include memory and concentration problems as well as changes in taste. This paper presents a very preliminary study (with only one elderly female subject participating) focusing on cognitive dysfunction after chemotherapy in breast cancer. This phenomenon, called Cancer-Related Cognitive Dysfunction (CRCD), is a frequent occurrence. The obtained results prove the impact of chemotherapy on the participant’s ability to concentrate. The QEEG method and digital data analysis were used as the measurement methods.

QEEG correlates of emotionally negative state induced by autobiographic script in patients with affective disorder and healthy controls

The elucidation of the brain circuitry underlying neural bases of mood and emotion represents the essential aim of modern neuroscience and psychiatric research. The aims of the study was to identify QEEG correlates of autobiographically induced emotionally neutral and emotionally negative situation (sadness) using the QEEG methods sLORETA (standardized low-resolution brain electromagnetic tomography)and eLORETA connectivity in remitted patients with bipolar affective disorder (BAP) ( N = 30), depression ( N = 20) and healthy controls ( N = 30). Within-group comparisons (pairwise t -tests) using log-transformed values of sLORETA images(emotionally neutral vs. negative emotional state) revealed that autobiographical induction of sadness in healthy volunteers led to the increase of neuronal activity (current density in the gamma frequency band) in subgenual and anterior cingulate (Brodmann area 25, 32, 34). Patients with BAP (sadness) showed more pronounced activation of limbic and mediofrontal areas (BA 10, 11, 24, 25, 32, 33, 47). This “hyperactivation” can be considered as a trait marker of BAP, which can be identified even in patients in remission. In contrast, between-group comparisons (in both neutral and negative emotional state) were not associated with any significant changes in brain activity in healthy volunteers compared to patients with BAP.

35. Practical value of quantitative EEG in epilepsy surgery planning

Objective To assess whether available algorithms of quantitative EEG (qEEG) could practically help in localizing epileptogenic zone (EZ) and modify surgical planning in patients with focal intractable epilepsy. Methods We will present a case report of a 7-year-old boy with catastrophic epilepsy caused by focal cortical dysplasia located in the operculo-insular region of the right hemisphere. Due to the challenging localization of the presumed EZ, uncertain surgical borders and expected significant risks of the resection, the patient was stereotactically implanted with oblique depth electrodes. Intracranial EEG (iEEG) signal was analyzed using different qEEG methods. Our originally developed interictal epileptiform discharges (IED) detecting algorithm, which also extracts repetitive propagation patterns, was applied to localize sources of IED. We also used own network connectivity algorithm to analyze ictal (seizure) iEEG activity in detail. Employing results of qEEG analyses, 2D and 3D dynamic reconstructions of both interictal and ictal iEEG epileptiform changes were created and used to guide surgical approach. Results Both qEEG algorithms clearly proved the EZ localization in the dorso-caudal insular cortex of the right hemisphere and demonstrated sparing of initially suspected frontal opercular area. The dorso-caudal insular cortex generated 89% of IED; remaining 11% IED originated from the primary motor cortex. Surgical approach was adjusted to this qEEG-based hypothesis. Oblique depth electrodes were preserved intraoperatively to help precise targeting of the lesion. Continuous intraoperative motor-evoked potential monitoring was used to preserve motor functions. The boy has been followed for more than one year postoperatively. He is seizure-free with no motor deficit; with normal cognitive functions. Conclusions The case report demonstrates that currently available qEEG methods could help in guiding resective epilepsy surgery in complicated patients indicated for iEEG studies. We suggest our approach could increase patients’ chance to obtain seizure-free outcomes without new deficits and thus ultimately improve their quality of life. Supported by MH CZ–DRO, University Hospital Motol, Prague, Czech Republic 00064203 and IGA NT/11460-4.

QEEG and 19-Channel Neurofeedback as a Clinical Evaluation Tool for Children with Attention, Learning and Emotional Problems

Attention, learning and emotional problems can have different causes that cannot be easily and clearly distinguished by clinical testing methods. But, QEEG and, even more so, live 19-channel Z-score training under different task conditions can both give very detailed insights about the specific functioning and dysregulations of an individual’s brain. The clinical intake evaluation of the child is optimized by including a quantitative, neurometric analysis of an eyes open (EO) and eyes closed (EC) EEG acquisition combined with a real-time analysis of the child’s (in vivo) brain functioning during a specific set of conditions, as described below. This method was developed and refined with more than 300 children who were tested between June 2012 and April 2014. The goal is to get as much information as possible in only one session lasting 45 to 60 minutes. The different parts of the evaluation consist of: eyes open (EO) and eyes closed (EC) collection of data, display of the actual brain waves, listing of the Z-score values (also presented as plots or instant brain maps with different task conditions), followed by games to play with a challenge condition. In addition, current source density (CSD) sLORETA of the different wave frequencies (usually delta, theta, alpha, beta, and gamma bands), distribution and velocity are shown as they change, as well as when the brain evaluates emotions. The session ends with a brief, individual 19-channel training with video feedback. Because of the usefulness of the information obtained from using this QEEG method, the author recommends that QEEG and an interactive neurofeedback session be included as a standard component in the diagnosis of and treatment planning for children with attention, learning and emotional problems.

Er EEG en nyttig undersøkelse i voksenpsykiatrien?

We present a brief overview of the use of EEG in psychiatry, with particular emphasis on differential diagnosis examination in case of acute psychiatric conditions. The article is based on a literature search in PubMed and the authors' own collections of articles and personal experience. Onset of epilepsy, encephalitis or other cerebral diseases may be accompanied by psychiatric or cognitive symptoms. Slow EEG activity may be an unspecific sign of cerebral disease. Psychiatric patients are also at an increased risk of epilepsy. In case of seizure symptoms such as convulsions or conditions with rapid changes in affective states, epileptiform activity during EEG is a specific sign of epileptic aetiology or comorbidity. Quantitative frequency analysis (QEEG) is useful for research purposes and in exceptional cases also in a clinical context. No QEEG method has as yet become accepted as providing reliable, independent markers for psychiatric disease or treatment response. EEG should be undertaken in case of newly occurring psychoses and for conditions with a fluctuating or progradiating loss of cognitive function. Adult psychiatric patients with seizure symptoms or rapid changes in affective states should also be referred to EEG.

AS21-04 - Reduction of prefrontal theta cordance in the prediction of response to antidepressants

Cordance is a QEEG method that incorporates both absolute and relative power and has been shown to have a strong association with cerebral perfusion or metabolism. Cordance accurately characterizes brain function on the cortical convexities and has demonstrated usefulness for characterizing antidepressant response. The aim of this study was to evaluate the efficacy of QEEG cordance in the prediction of response to various antidepressants in patients with resistant depression. A total of 81 inpatients with depressive disorder (MADRS≥20) who previously did not respond to at least one antidepressant treatment were treated with various antidepressants for 4 weeks. QEEG cordance was computed at 3 frontal electrodes in theta frequency band (4–8 Hz). Depressive symptoms were assessed using Montgomery-Åsberg Depression Rating Scale. 29 from 33 responders and 14 from 48 non-responders decreased prefrontal QEEG cordance value after the first week of treatment (p = 0,0001). There was a difference between responders and nonresponders in the change of cordance value after first week of treatment (p = 0.002). Positive and negative predictive value of cordance reduction for response to treatment was 0.67 (0.58–0.73) and 0.90 (0.79–0.96), respectively. The overall accuracy of the test was 0.78 and the effect size (w = 0.59) was in large range. Early change in prefrontal theta band cordance probably reflects a common underlying mechanism of antidepressant effect, regardless of the type of treatment. Prefrontal cordance may provide a useful biomarker for the early detection of response to antidepressant therapy.

P02-100 - QEEG cordance in the prediction of response to ketamine in depressive patients - interim analysis of randomized controlled trial

ObjectiveRapid and robust antidepressant-like effect of ketamine in subanesthetic doses was already manifested in depressive patients. Maximum of mood improvement was shown within the period from 2 hours to 3 days. Previous studies proved predictive value of prefrontal QEEG theta cordance reduction after 1 week on a new antidepressant.Congruently with previous findings we hypothesised in our compressed model decrease of QEEG cordance in 10 minutes of ketamine hydrochloride infusion as the prediction of antidepressant response.Methods14 MDD patients (6F/8M) diagnosed with a moderate to severe depressive episode without psychotic symptoms were included. All of the participants received the 30 minutes lasting only infusion with subanesthetic dose of ketamine hydrochloride solution (0.54 mg/kg). EEG measurements on the baseline, after 10 and 30 minutes of infusion were taken into account in computation of QEEG cordance.Results9 (64.3%) of subjects responded to single ketamine infusion following day and 8 (88.9%) of them decreased QEEG cordance. T-test pair comparison found significant difference between baseline and after 10 minutes of ketamine infusion in responders (F = 4.12; p < 0.003). ConclusionsPreliminary results have shown the tendency of prefrontal QEEG cordance to decrease as a ketamine response prediction. Larger sample size is needed to increased precision in estimates of cordance sensitivity and specificity. Combination of latest QEEG method and fastest-acting antidepressant-like effect of ketamine in this trail is unexampled. Supported by IGA (MH CR) No.NS10379-3, 1M0517 (MEYS CR), MZ0PCP2005 (MH CR). Local Ethics Committee approval has been granted.

QEEG Studies in the Assessment and Treatment of Childhood Disorders

Quantifying EEG measures across age allows the ability to establish parameters of normalcy at any age which can be used as a reference when children exhibit developmental delays in their abilities and/or other atypical and maladaptive behaviors. A review of the current literature on the utilization of QEEG methods to serve as an aid for identifying these children as distinctively different from normal, and in some cases as distinctive from other clinical considerations has been shown to provide a sufficient sensitivity and specificity worthy of consideration as a diagnostic aid in evaluating clinical deviations in development. Furthermore, these same QEEG measures can provide a means of establishing treatment efficacy for the evident brain dysfunctions underlying these childhood disorders.

QEEG Monitoring of Alzheimer's Disease Treatment: A Preliminary Report of Three Case Studies

Previous research has shown that quantitative electroencephalography (qEEG) can monitor treatment of Alzheimer's Disease (AD). This study investigated the ability of a qEEG measure based on EEG variance, combined with a delayed recognition memory task, to measure treatment effects on patients with AD. Three AD patients with very mild AD (CDR=0.5, FAST stage 3) were monitored with task specific EEG at multiple time points before and after medication treatment. Patients had their EEG recorded while performing a recognition memory task. A measure of (normalized) variance was applied to the EEG data. To the extent possible, the subjects received this treatment monitoring multiple times. These patients were monitored a total of 14 times, which yielded 11 measurements of qEEG change during the course of treatment. The direction of change in patients' qEEG values agreed with patients' medication treatment on 10 out of 11 occasions, p < 0.006 (binomial test) and was more accurate than monitoring with the relative theta power, p < 0.05. The results of this monitoring also showed that the qEEG measure accurately reflected treatment in a dose dependent manner. These results were independent of the specific medication monitored; Galantamine, Memantine, Nicotine, and Rivastigmine. In conclusion, this qEEG method may be useful for measuring AD treatment responses.

Advances in quantitative electroencephalogram analysis methods.

Quantitative electroencephalogram (qEEG) plays a significant role in EEG-based clinical diagnosis and studies of brain function. In past decades, various qEEG methods have been extensively studied. This article provides a detailed review of the advances in this field. qEEG methods are generally classified into linear and nonlinear approaches. The traditional qEEG approach is based on spectrum analysis, which hypothesizes that the EEG is a stationary process. EEG signals are nonstationary and nonlinear, especially in some pathological conditions. Various time-frequency representations and time-dependent measures have been proposed to address those transient and irregular events in EEG. With regard to the nonlinearity of EEG, higher order statistics and chaotic measures have been put forward. In characterizing the interactions across the cerebral cortex, an information theory-based measure such as mutual information is applied. To improve the spatial resolution, qEEG analysis has also been combined with medical imaging technology (e.g., CT, MR, and PET). With these advances, qEEG plays a very important role in basic research and clinical studies of brain injury, neurological disorders, epilepsy, sleep studies and consciousness, and brain function.

Quantitative electroencephalography in Parkinson's disease, dementia, depression and normal aging.

The relationship between depression and dementia in Parkinson's disease (PD) has rarely been explored. Using a quantitative EEG (qEEG) parameter, we studied four groups of subjects: PD, demented Alzheimer's type and major depressed patients and normal controls. The qEEG data were compared with those of the Mini-Mental State and the Hamilton Depression Scale. The qEEG pattern was different in the four groups of subjects. Moreover, there was a significant correlation between the qEEG data and the other variables, and, particularly, with the cognitive performances. Our findings demonstrate that the qEEG method of assessment may give valuable data for a better classification of dementia syndromes and for a distinction between dementia and pseudodementia.