Qualitative EEG Research Articles

Aberrant brain-heart coupling is associated with the severity of post cardiac arrest brain injury.

To investigate autonomic nervous system activity measured by brain-heart interactions in comatose patients after cardiac arrest in relation to the severity and prognosis of hypoxic-ischemic brain injury. Strength and complexity of bidirectional interactions between EEG frequency bands (delta, theta, and alpha) and ECG heart rate variability frequency bands (low frequency, LF and high frequency, HF) were computed using a synthetic data generation model. Primary outcome was the severity of brain injury, assessed by (i) standardized qualitative EEG classification, (ii) somatosensory evoked potentials (N20), and (iii) neuron-specific enolase levels. Secondary outcome was the 3-month neurological status, assessed by the Cerebral Performance Category score [good (1-2) vs. poor outcome (3-4-5)]. Between January 2007 and July 2021, 181 patients were admitted to ICU for a resuscitated cardiac arrest. Poor neurological outcome was observed in 134 patients (74%). Qualitative EEG patterns suggesting high severity were associated with decreased LF/HF. Severity of EEG changes were proportional to higher absolute values of brain-to-heart coupling strength (p < 0.02 for all brain-to-heart frequencies) and lower values of alpha-to-HF complexity (p = 0.049). Brain-to-heart coupling strength was significantly higher in patients with bilateral absent N20 and correlated with neuron-specific enolase levels at Day 3. This aberrant brain-to-heart coupling (increased strength and decreased complexity) was also associated with 3-month poor neurological outcome. Our results suggest that autonomic dysfunctions may well represent hypoxic-ischemic brain injury post cardiac arrest pathophysiology. These results open avenues for integrative monitoring of autonomic functioning in critical care patients.

How the Spreading and Intensity of Interictal Epileptic Activity Are Associated with Visuo-Spatial Skills in Children with Self-Limited Focal Epilepsy with Centro-Temporal Spikes.

This paper investigates brain-behaviour associations between interictal epileptic discharges and cognitive performance in a population of children with self-limited focal epilepsy with centro-temporal spikes (SeLECTS). Sixteen patients with SeLECTS underwent an extensive neuropsychological assessment, including verbal short-term and episodic memory, non-verbal short-term memory, attentional abilities and executive function. Two quantitative EEG indices were analysed, i.e., the Spike Wave Index (SWI) and the Spike Wave Frequency (SWF), and one qualitative EEG index, i.e., the EEG score, was used to evaluate the spreading of focal SW to other parts of the brain. We investigated associations between EEG indices and neuropsychological performance with non-parametric Spearman correlation analyses, including correction for multiple comparisons. The results showed a significant negative correlation between (i) the awake EEG score and the Block Tapping Test, a visuo-spatial short-term memory task, and (ii) the sleep SWI and the Tower of London, a visuo-spatial planning task (pcorr < 0.05). These findings suggest that, in addition to the usual quantitative EEG indices, the EEG analysis should include the qualitative EEG score evaluating the spreading of focal SW to other parts of the brain and that neuropsychological assessment should include visuo-spatial skills.

Assessment of Classical and Non-Classical Quantitative Electroencephalographic Measures in Patients with Substance Use Disorders.

Background: People diagnosed with substance use disorders (SUDs) are at risk for impairment of brain function and structure. However, physicians still do not have any clinical biomarker of brain impairment that helps diagnose or treat these patients when needed. The most common method to study these patients is the classical electroencephalographic (EEG) analyses of absolute and relative powers, but this has limited individual clinical applicability. Other non-classical measures such as frequency band ratios and entropy show promise in these patients. Therefore, there is a need to expand the use of quantitative (q)EEG beyond classical measures in clinical populations. Our aim is to assess a group of classical and non-classical qEEG measures in a population with SUDs. Methods: We selected 56 non-medicated and drug-free adult patients (30 males) diagnosed with SUDs and admitted to Rehabilitation Clinics. According to qualitative EEG findings, patients were divided into four groups. We estimated the absolute and relative powers and calculated the entropy, and the alpha/(delta + theta) ratio. Results: Our findings showed a significant variability of absolute and relative powers among patients with SUDs. We also observed a decrease in the EEG-based entropy index and alpha/(theta + delta) ratio, mainly in posterior regions, in the patients with abnormal qualitative EEG. Conclusions: Our findings support the view that the power spectrum is not a reliable biomarker on an individual level. Thus, we suggest shifting the approach from the power spectrum toward other potential methods and designs that may offer greater clinical possibilities.

EEG patterns in long-term Brahmakumari meditators and their possible significance

Background: Yoga and meditation are associated with improvement in both physical and mental health. Brahmakumari meditation is concerned with awareness of the metaphysical self with reorientating thought patterns and feelings so one can live with positive thinking, greater harmony, and contentment. EEG gives meaningful insight about the state of mind of an individual. Aims and Objectives: The present study was designed to review the effects of meditation on the state of mind based on changing EEG brainwave patterns in long-term Brahmakumari meditators. Materials and Methods: Fifteen Brahmakumari meditators aged between 30 and 50 years who were involved in regular meditation for &gt;10 years and with no history of any neurological disorders were enrolled. Twenty-one channels EEG with EKG and EMG were recorded in all subjects during pre-meditative, meditative, and post-meditative state at an interval of 20 min. Qualitative EEG was analyzed for the appearance of wave patterns based on different voltages and frequencies. Results: High voltage alpha rhythm was observed in the majority of subjects during pre-meditation and theta waves in most subjects during meditation. During meditation, their minds progress further to a deep state of calmness and relaxation as indicated by the appearance of theta waves. Even delta waves were observed during meditation in some of the subjects indicating a phase similar to a deep sleep. Conclusion: Stress and mental unrest are major problem in the present era which is responsible for many non-communicable diseases. Regular meditation is a very important de-stressor and should be regularly practiced to lead a happy and peaceful life.

Cerebral cortical activity after withdrawal of life-sustaining measures in critically ill patients

Establishing when cerebral cortical activity stops relative to circulatory arrest during the dying process will enhance trust in donation after circulatory determination of death. We used continuous electroencephalography and arterial blood pressure monitoring prior to withdrawal of life sustaining measures and for 30 min following circulatory arrest to explore the temporal relationship between cessation of cerebral cortical activity and circulatory arrest. Qualitative and quantitative EEG analyses were completed. Among 140 screened patients, 52 were eligible, 15 were enrolled, 11 completed the full study, and 8 (3 female, median age 68 years) were included in the analysis. Across participants, EEG activity stopped at a median of 78 (Q1=-387, Q3=111) seconds before circulatory arrest. Following withdrawal of life sustaining measures there was a progressive reduction in electroencephalographic amplitude (p=.002), spectral power (p=.008), and coherence (p=.003). Prospective recording of cerebral cortical activity in imminently dying patients is feasible. Our results from this small cohort suggest that cerebral cortical activity does not persist after circulatory arrest. Confirmation of these findings in a larger multicenter study are needed to help promote stakeholder trust in donation after circulatory determination of death.

Classification of electrical status epilepticus in sleep based on EEG patterns and spatiotemporal mapping of spikes

We firstly aimed to describe and classify EEG patterns in electrical status epilepticus in sleep (ESES), and secondly subclassify EEG patterns based on analysis of spikes using spatio-temporal mapping and electrical source analysis. Overnight EEGs (minimum: eight hours) of 30 children, aged 2-12 years, with ESES (spike-wave index: at least 50%) were selected. Average reference montage was used for dipole analysis and mapping. The location and orientation of the dipoles were determined by mapping positive and negative poles and applying the rules of mapping. The onset and propagation of the spikes and the latency between the two hemispheres (for bisynchronous spikes) were determined (based on source analysis using BESA research 7.1). (1) ESES was classified as “generalised” (80%) and focal (20%) patterns; (2) the bisynchronous subtype in the “generalised” pattern was due to apparently synchronous bilateral activation of spikes (with lead-in of 20-60 ms from one hemisphere) with a tangential/oblique dipole (source analysis localised these spikes to around the peri-rolandic cortex); (3) the classic description of ESES spikes as “diffuse” spikes with bifrontal maxima is a misinterpretation using the 10-20 EEG system .Using voltage mapping and source analysis, cortical activation in the rolandic cortex was identified which imparts diffuse frontal negativity and parieto-occipital positivity; (4) ESES spikes showed intraspike and interspike dipole instability and the orientation of dipoles changed due to local spike propagation around the source and into the depth of the sulcus (which we refer to herein as “dancing dipoles”); and (5) focal ESES were classified as parietal, occipital and temporo-occipital patterns;a frontal ESES pattern was not seen. Based on detailed mapping and source analysis of ESES, we have successfully reinterpreted various misconceptions in the literature. We have simplified the interpretation of complicated EEG patterns by extracting the primary and propagated sources which aid the classification of ESES. As the dipole is always stable in self-limited childhood epilepsy with centrotemporal spikes, we believe that the phenomenon of an intrinsically unstable dipole is a reliable qualitative EEG marker of ESES.

EEG-based methods for recovery prognosis of patients with disorders of consciousness: A systematic review

ObjectiveDisorders of consciousness (DoC) are acquired conditions of severely altered consciousness. Electroencephalography (EEG)-derived biomarkers have been studied as clinical predictors of consciousness recovery. Therefore, this study aimed to systematically review the methods, features, and models used to derive prognostic EEG markers in patients with DoC in a rehabilitation setting. MethodsWe conducted a systematic literature search of EEG-based strategies for consciousness recovery prognosis in five electronic databases. ResultsThe search resulted in 2964 papers. After screening, 15 studies were included in the review. Our analyses revealed that simpler experimental settings and similar filtering cut-off frequencies are preferred. The results of studies were categorised by extracting qualitative and quantitative features. The quantitative features were further classified into evoked/event-related potentials, spectral measures, entropy measures, and graph-theory measures. Despite the variety of methods, features from all categories, including qualitative ones, exhibited significant correlations with DoC prognosis. Moreover, no agreement was found on the optimal set of EEG-based features for the multivariate prognosis of patients with DoC, which limits the computational methods applied for outcome prediction and correlation analysis to classical ones. Nevertheless, alpha power, reactivity, and higher complexity metrics were often found to be predictive of consciousness recovery. ConclusionsThis study’s findings confirm the essential role of qualitative EEG and suggest an important role for quantitative EEG. Their joint use could compensate for their reciprocal limitations. SignificanceThis study emphasises the need for further efforts toward guidelines on standardised EEG analysis pipeline, given the already proven role of EEG markers in the recovery prognosis of patients with DoC.

EEG Pattern With Spectral Analysis Can Prognosticate Good and Poor Neurologic Outcomes After Cardiac Arrest.

To investigate the prognostic value of a simple stratification system of electroencephalographical (EEG) patterns and spectral types for patients after cardiac arrest. In this prospectively enrolled cohort, using manually selected EEG segments, patients after cardiac arrest were stratified into five independent EEG patterns (based on background continuity and burden of highly epileptiform discharges) and four independent power spectral types (based on the presence of frequency components). The primary outcome is cerebral performance category (CPC) at discharge. Results from multimodal prognostication testing were included for comparison. Of a total of 72 patients, 6 had CPC 1-2 by discharge, all of whom had mostly continuous EEG background without highly epileptiform activity at day 3. However, for the same EEG background pattern at day 3, 19 patients were discharged at CPC 3 and 15 patients at CPC 4-5. After adding spectral analysis, overall sensitivity for predicting good outcomes (CPC 1-2) was 83.3% (95% confidence interval 35.9% to 99.6%) and specificity was 97.0% (89.5% to 99.6%). In this cohort, standard prognostication testing all yielded 100% specificity but low sensitivity, with imaging being the most sensitive at 54.1% (36.9% to 70.5%). Adding spectral analysis to qualitative EEG analysis may further improve the diagnostic accuracy of EEG and may aid developing novel measures linked to good outcomes in postcardiac arrest coma.

Quantitative electroencephalographic analysis as a potential biomarker of response to treatment with cannabidiol

PurposePharmaceutical grade cannabidiol (CBD) is one of the newest anti-seizure medications for refractory epilepsy, and the effects of CBD on EEG have not been fully described. MethodsPatients enrolled in a CBD expanded access study had EEGs prior to and 12 weeks after initiation of CBD treatment for their refractory epilepsy. In addition to evaluating the clinical EEG reports, a nonbiased quantitative EEG (qEEG) analysis of background EEG was performed to determine whether consistent changes occur in the EEG in response to administration of CBD. ResultsNo significant qualitative changes were seen, nor changes in quantitative markers of EEG amplitude (RMS amplitude, standard deviation of the amplitude, skewness, or kurtosis), frequency (relative delta, theta, or alpha power), Spearman correlation, or coherence between brain regions. However, relative beta power and 1/f slope, a measure of signal noise increased with the addition of CBD. When patients were separated into responders and nonresponders based on seizure reduction with CBD, responders also had decreased Spearman correlation between the frontopolar and occipital regions after addition of CBD, suggesting that responders may have quantitatively improved EEG background organization after CBD initiation. The differences in beta and 1/f slope were also seen more robustly in CBD responders compared with nonresponders after CBD initiation. These differences disappeared when analyzing only patients not taking benzodiazepines, suggesting that the effect of CBD on seizures was related to the ability of the brain to further increase beta in response to CBD in patients already taking benzodiazepines.We noted that even before initiation of CBD, 1/f slope was also significantly different in responders compared to nonresponders. Therefore, to explore the baseline EEG in responders and nonresponders, we utilized a variable selection procedure to identify baseline EEG features that could predict whether a patient’s seizures would improve with CBD. In the optimal multivariable logistic model, baseline coherence, Spearman correlation, and patient sex jointly predicted whether a patient in this cohort would respond to CBD (defined as a seizure reduction of 40% or greater) with 74% accuracy. This model performed less well on a data set of reduced duration and variability, highlighting the importance of real-world testing of any clinically relevant model. ConclusionThese results suggest that there are subtle changes in certain metrics detected by qEEG even at baseline that may not be perceived during qualitative EEG analysis and that could be used in the future as a biomarker to predict a patient’s clinical response to CBD administration. Development of such a predictive EEG biomarker, especially before the initiation of a medication trial, could reduce unnecessary ASM exposure and improve outcomes for patients with epilepsy facing new medication selection.

Identifying biomarkers for epilepsy after cerebral malaria in Zambian children: rationale and design of a prospective observational study

IntroductionMalaria affecting the central nervous system (CM) is a major contributor to paediatric epilepsy in resource-poor settings, with 10%–16% of survivors developing epilepsy within 2 years of infection. Despite high...

Electroencephalography in delirium assessment: a scoping review

BackgroundDelirium is a common disorder affecting around 31% of patients in the intensive care unit (ICU). Delirium assessment scores such as the Confusion Assessment Method (CAM) are time-consuming, they cannot differentiate between different types of delirium and their etiologies, and they may have low sensitivities in the clinical setting. While today, electroencephalography (EEG) is increasingly being applied to delirious patients in the ICU, a lack of clear cut EEG signs, leads to inconsistent assessments.MethodsWe therefore conducted a scoping review on EEG findings in delirium. One thousand two hundred thirty-six articles identified through database search on PubMed and Embase were reviewed. Finally, 33 original articles were included in the synthesis.ResultsEEG seems to offer manifold possibilities in diagnosing delirium. All 33 studies showed a certain degree of qualitative or quantitative EEG alterations in delirium. Thus, normal routine (rEEG) and continuous EEG (cEEG) make presence of delirium very unlikely. All 33 studies used different research protocols to at least some extent. These include differences in time points, duration, conditions, and recording methods of EEG, as well as different patient populations, and diagnostic methods for delirium. Thus, a quantitative synthesis and common recommendations are so far elusive.ConclusionFuture studies should compare the different methods of EEG recording and evaluation to identify robust parameters for everyday use. Evidence for quantitative bi-electrode delirium detection based on increased relative delta power and decreased beta power is growing and should be further pursued. Additionally, EEG studies on the evolution of a delirium including patient outcomes are needed.

Frontal Electroencephalography Findings in Critically Ill COVID-19 Patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) negatively impacts the central nervous system, and studies using a full montage of electroencephalogram (EEG) electrodes have reported nonspecific EEG patterns associated with coronavirus disease 2019 (COVID-19) infection. The use of this technology is resource-intensive and limited in its implementation. In this descriptive pilot study, we report neurophysiological patterns and the potential prognostic capability of an abbreviated frontal EEG electrode montage in critically ill COVID-19 patients. Patients receiving mechanical ventilation for SARS-CoV-2 respiratory failure were monitored with Sedline Root Devices using EEG electrodes were placed over the forehead. Qualitative EEG assessments were conducted daily. The primary outcome was mortality, and secondary outcomes were duration of endotracheal intubation and lengths of intensive care and hospitalization stay. Twenty-six patients were included in the study, and EEG discontinuity was identified in 22 (84.6%) patients. The limited sample size and patient heterogeneity precluded statistical analysis, but certain patterns were suggested by trends in the data. Survival was 100% (4/4) for those patients in which a discontinuous EEG pattern was not observed. The majority of patients (87.5%, 7/8) demonstrating activity in the low-moderate frequency range (7 to 17 Hz) survived compared with 61.1% (11/18) of those without this observation. The majority of COVID-19 patients showed signs of EEG discontinuity during monitoring with an abbreviated electrode montage. The trends towards worse survival among those with EEG discontinuity support the need for additional studies to investigate these associations in COVID-19 patients.

ALGORITHM FOR CHOOSING THE OPTIMAL MODE OF CEREBROPROTECTIVE THERAPY IN PATIENTS WITH HYPERTENSION WITH COGNITIVE IMPAIRMENT

The article is devoted to an urgent problem - cognitive impairment in patients with arterial hypertension. The aim of the study was to establish objective qualitative and quantitative EEG criteria for cognitive impairment in patients with hypertension in order to use them later to determine the optimal regimen of cerebroprotective therapy and evaluate its effectiveness. The study involved 192 people (patients with hypertension and potentially healthy people without hypertension and CN). The number of patients with an established diagnosis of AH and with CN was 99 people. They were divided into three groups in accordance with the regimens of cerebroprotective therapy prescribed by the neurologist. As a result of the work, an algorithm of treatment tactics was developed and an application for patent No. 2021126327, priority date 06.09.2021, was issued.

Using EEG in Resource-Limited Areas: Comparing Qualitative and Quantitative Interpretation Methods in Cerebral Malaria

BackgroundOur goal was to compare the strength of association and predictive ability of qualitative and quantitative electroencephalographic (EEG) factors with the outcomes of death and neurological disability in pediatric cerebral malaria (CM). MethodsWe enrolled children with a clinical diagnosis of CM admitted to Queen Elizabeth Central Hospital (Blantyre, Malawi) between 2012 and 2017. A routine-length EEG was performed within four hours of admission. EEG data were independently interpreted using qualitative and quantitative methods by trained pediatric neurophysiologists. EEG interpreters were unaware of patient discharge outcome. ResultsEEG tracings from 194 patients were reviewed. Multivariate modeling revealed several qualitative and quantitative EEG variables that were independently associated with outcomes. Quantitative methods modeled on mortality had better goodness of fit than qualitative ones. When modeled on neurological morbidity in survivors, goodness of fit was better for qualitative methods. When the probabilities of an adverse outcome were calculated using multivariate regression coefficients, only the model of quantitative EEG variables regressed on the neurological sequelae outcome showed clear separation between outcome groups. ConclusionsMultiple qualitative and quantitative EEG factors are associated with outcomes in pediatric CM. It may be possible to use quantitative EEG factors to create automated methods of study interpretation that have similar predictive abilities for outcomes as human-based interpreters, a rare resource in many malaria-endemic areas. Our results provide a proof-of-concept starting point for the development of quantitative EEG interpretation and prediction methodologies useful in resource-limited settings.

EEG seizure onset patterns in status epilepticus

Introduction: The seizure-onset (SOn) patterns have been studied especially with intracranial electrodes to improve the determination of the epileptogenic zone. Qualitative and quantitative EEG studies characterizing the onset pattern in status epilepticus (SE) are lacking. We aimed to classify the different SOn patterns and to compare them in order to determine any possible differences in terms of mortality, morbidity and treatment response.

Qualitative EEG Measures to Discriminate Long-Term Effects of Malnutrition in Comparison With Healthy Control Subjects

Qualitative EEG Measures to Discriminate Long-Term Effects of Malnutrition in Comparison With Healthy Control Subjects

Signal Dynamics Analysis for Epileptic Seizure Classification on EEG Signals

Epilepsy is the most common form of neurological disease. Patients with epilepsy may experience seizures of a certain duration with or without provocation. Epilepsy analysis can be done with an electroencephalogram (EEG) examination. Observation of qualitative EEG signals generates high cost and often confuses due to the nature of the non-linear EEG signal and noise. In this study, we proposed an EEG signal processing system for EEG seizure detection. The signal dynamics approach to normal and seizure signals' characterization became the main focus of this study. Spectral Entropy (SpecEn) and fractal analysis are used to estimate the EEG signal dynamics and used as feature sets. The proposed method is validated using a public EEG dataset, which included preictal, ictal, and interictal stages using the Naïve Bayes classifier. The test results showed that the proposed method is able to generate an ictal detection accuracy of up to 100%. It is hoped that the proposed method can be considered in the detection of seizure signals on the long-term EEG recording. Thus it can simplify the diagnosis of epilepsy.

A qualitative awake EEG score for the diagnosis of continuous spike and waves during sleep (CSWS) syndrome in self-limited focal epilepsy (SFE): A case-control study

PurposeTo determine whether awake EEG criteria can differentiate epileptic encephalopathy with continuous spike and waves during sleep (EE-CSWS) at the time of cognitive regression from typical, self-limited focal epilepsy (SFE). MethodsThis retrospective case-control study was based on the analysis of awake EEGs and included 15 patients with EE-CSWS and 15 age-matched and sex-matched patients with typical SFE. The EEGs were anonymised and scored by four independent readers. The following qualitative and quantitative EEG indices were analysed: slow-wave index (SLWI), spike-wave index (SWI), spike-wave frequency (SWF), long spike-wave clusters (CLSW) and EEG score (between grades 0 and 4). Sensitivity and specificity were assessed using receiver operating characteristic (ROC) curves and their reproducibility with a kappa test. ResultsBased on a highly sensitive cut-off, EE-CSWS patients were 8.4 times more likely than those with SFE to have an SLWI > 6%, 15 times more likely to have an SWI > 10 % and six times more likely to have a CLSW of ≥ 1 s. There was substantial agreement between readers (with kappa values of 0.64, 0.69 and 0.67). EE-CSWS patients were 13 times more likely to have an SWF of > 11 % and 149 times more likely to have an EEG score of ≥ 3 than typical SFE patients. Agreement about these ratings was almost perfect (kappa 0.91 and 0.86). ConclusionAn EEG score of ≥ 3 on a 20-min awake EEG differentiates typical SFE from EE-CSWS at the time of cognitive regression, with good reliability across readers with different levels of expertise.

Quantitative and Qualitative EEG as a Prediction Tool for Outcome and Complications in Acute Stroke Patients.

Currently, the relevance of EEG measurements in acute stroke patients is considered low in clinical practice. However, recent studies on the predictive value of EEG measurements after stroke for various outcomes may increase the role of EEG in patients with stroke. We aimed to review the current literature on the utility of EEG measurements after stroke as a tool to predict outcome and complications, focusing on studies in which the EEG measurement was performed in the acute phase after the event and in which long-term outcome measures were reported. In our literature review, we identified 4 different outcome measures (functional outcome, mortality, development of post-stroke cognitive decline, and development of post-stroke epilepsy) where studies on the utility of acute EEG measurements exist. There is a large body of evidence for the prediction of functional outcome, in which a multitude of associated quantitative and qualitative EEG parameters are described. In contrast, only few studies focus on mortality as outcome parameter. We found studies of high methodical quality on the prediction of post-stroke cognitive decline, though the number of patients in these studies often was small. The role of EEG as a prediction tool for seizures and epilepsy after stroke could increase after a recently published study, especially if its result can be incorporated into already existing post-stroke epilepsy prediction tools. In summary, EEG is useful for the prediction of functional outcome, mortality, development of post-stroke cognitive decline and epilepsy, even though there is a discrepancy between the large amount of studies on EEG in acute stroke patients and its underuse in clinical practice.

Comparative study of electroencephalographic findings in patients with depression and schizophrenia

Introduction: Schizophrenia and depression are two major and very common mentaldisorders worldwide. Many clinical questionnaire and scales have been formulated todiagnose these cases. Standard (qualitative) electroencephalography (EEG) has alsobeen routinely employed in the diagnostic evaluation of patients of these disorders.However quantitative EEG (qEEG) has not been frequently used in evaluation of thesedisorders. Qualitative EEG (qEEG) findings differ between patients with schizophreniaand patients with depression, but results are not consistent. We performed this study todetermine the differences in EEG parameters(qualitative and quantitative) betweenpatients with schizophrenia, patients with depression, and healthy subjects.Materials and Methods: Our study included 69 patients with schizophrenia, 69patients with depression, and 138 healthy subjects. All patients and healthy subjectswere aged between 18-50 years. All clinical diagnoses were made according to DSM-IVdiagnostic criteria. Standard EEG was performed on all study participants and artefact-free 100-second epochs were selected from the recorded material and analysed withFast Fourier Transformation (FFT) analysis.Observations and Result: Of the entire sample of patients, 40.58% had abnormal EEG.Patients with schizophrenia when compared with healthy subjects showed increaseddelta, theta, and beta activity and decreased alpha activity. Patients with depression alsoshowed similar results, but in fewer regions. In patients with schizo-phrenia, deltapower over some frontal regions was increased in comparison with those in patientswith depression. Schizophrenic patients and healthy subjects showed interhemisphericasymmetry, but it was absent in patients with depression.Conclusion: Abnormal EEG findings in patients with schizophrenia and depression isnot very common. However, on qEEG, the patients with schizophrenia had in-creaseddelta power in comparison to patients with depression. Hence, qEEG may have a rolein clinical differentiation between these two mental disorders and it may be especiallyimportant in cases of negative-symptom schizophrenia.Key words: Schizophrenia, Depression, quantitative electroencephalography(qEEG).