EEG Waves Research Articles

Effects of long-term meditation on cognition, electroencephalography and visual evoked potentials derived brain dynamics: A cross-sectional comparative study

We compared EEG and VEP wave patterns, and measures of cognition of long-term meditators (LTM) with non-meditators (NM) to assess cerebral effects of meditation.

The Implicit Aesthetic Preference for Mobile Marketing Interface Layout-An ERP Study.

Businesses and scholars have been trying to improve marketing effect by optimizing mobile marketing interfaces aesthetically as users browse freely and aimlessly through mobile marketing interfaces. Although the layout is an important design factor that affects interface aesthetics, whether it can trigger customer's aesthetic preferences in mobile marketing remains unexplored. To address this issue, we employ an empirical methodology of event-related potentials (EPR) in this study from the perspective of cognitive neuroscience and psychology. Subjects are presented with a series of mobile marketing interface images of different layouts with identical marketing content. Their EEG waves were recorded as they were required to distinguish a target stimulus from the others. After the experiment, each of the subjects chose five stimuli interfaces they like and five they dislike. By analyzing the ERP data derived from the EEG data and the behavioral data, we find significant differences between the disliked interfaces and the other interfaces in the ERP component of P2 from the frontal-central area in the 200–400 ms post-stimulus onset time window and LPP from both the frontal-central and parietal-occipital area in the 400–600 ms time window. The results support the hypothesis that humans do make rapid implicit aesthetic preferences for interface layouts and suggest that even under a free browsing context like the mobile marketing context, interface layouts that raise high emotional arousal can still attract more user attention and induce users' implicit aesthetic preference.

Dietary Euterpe oleracea Mart. attenuates seizures and damage to lipids in the brain of Colossoma macropomum.

The Amazonian açai fruit (Euterpe oleracea) has shown promising anticonvulsant properties, comparable to those of diazepam (BDZ) in in vivo models submitted to pentylenetetrazole (PTZ). PTZ is a classic convulsant agent used in studies for the purpose of screening anticonvulsants and investigating the mechanisms of epilepsy. Herein, we aimed to determine, for the first time, the effect of dietary administration of lyophilized E. oleracea (LEO) on PTZ-induced seizures, using juvenile Colossoma macropomum fish (9.1 ± 1.5g) as a model. A control diet (0.00% LEO) and two levels of LEO inclusion were established: 5.00% and 10.0% LEO (w/w). Fish were divided into five groups (n = 5): control (0.9% physiological solution; i.p.), PTZ (PTZ 150mgkg-1; i.p.), PTZ LEO 5.00%, PTZ LEO 10.0%, and BDZ-PTZ (BDZ: diazepam 10mgkg-1; i.p.). In addition to the electroencephalography (EEG), the lipid peroxidation (TBARS) was quantified in the brain, along with the characterization of behavioral responses. Fish receiving PTZ showed intense action potential bursts (APB), which overlapped with a hyperactive behavior. In PTZ LEO 5.00% and 10.0% groups, convulsive behavior was significantly reduced compared to the PTZ group. Fish fed 5.00% or 10.0% LEO and exposed to PTZ showed less excitability and lower mean amplitude in tracings. The inclusion of 10.0% LEO in the diet prevented the increase in mean amplitude of the EEG waves by 80%, without significant differences to the quantified mean amplitude of the BDZ-PTZ group. TBARS concentration was reduced by 60% in the brain of fish fed 10.0% LEO-enriched diets relative to the PTZ-administered group. The results of this study demonstrated the anticonvulsant and protective roles of LEO to the brain, and the dietary inclusion of LEO seems to be promising for the formulation of functional diets. Results of this study may boost the interest on the anti-seizurogenic properties of Euterpe oleracea, including the development of new approaches for the prevention of seizures in humans and animals with low epileptic threshold.

Effect of electroencephalography-guided anesthesia on neurocognitive disorders in elderly patients undergoing major non-cardiac surgery: A trial protocol The POEGEA trial (POncd Elderly GEneral Anesthesia)

IntroductionThe number of elderly patients undergoing major surgery is rapidly increasing. They are particularly at risk of developing postoperative neurocognitive disorders (NCD). Earlier studies suggested that processed electroencephalographic (EEG) monitors may reduce the incidence of postoperative NCD. However, none of these studies controlled for intraoperative nociception levels or personalized blood pressure targets. Their results remain unclear if the reduction in the incidence of postoperative NCD relates to avoidance of any electroencephalographic pattern suggesting excessive anesthesia depth.ObjectiveThe objective of this trial is to investigate–in patients ≥ 70 years old undergoing major non-cardiac surgery–the effect of EEG-guided anesthesia on postoperative NCD while controlling for intraoperative nociception, personalized blood pressure targets, and using detailed information provided by the EEG monitor (including burst suppression ratio, density spectral array, and raw EEG waveform).Material and methodsThis prospective, randomized, controlled trial will be conducted in a single Canadian university hospital. Patients ≥ 70 years old undergoing elective major non-cardiac surgery will be included in the trial. The administration of sevoflurane will be adjusted to maintain a BIS index value between 40 and 60, to keep a Suppression Ratio (SR) at 0%, to keep a direct EEG display without any suppression time and a spectrogram with most of the EEG wave frequency within the alpha, theta, and delta frequencies in the EEG-guided group. In the control group, sevoflurane will be administered to achieve an age-adjusted minimum alveolar concentration of [0.8–1.2]. In both groups, a nociception monitor will guide intraoperative opioid administration, individual blood pressure targets will be used, and cerebral oximetry used to tailor intraoperative hemodynamic management. The primary endpoint will be the incidence of NCD at postoperative day 1, as evaluated by the Montreal Cognitive Assessment (MoCA). Secondary endpoints will include the incidence of postoperative NCD at different time points and the evaluation of cognitive trajectories up to 90 days after surgery among EEG-guided and control groups.Study registrationNCT04825847 on ClinicalTrials.gov.

Classification of brain electrophysiological changes in response to colour stimuli.

In this study, the classification of ongoing brain activity occurring as a response to colour stimuli was managed and reported. Until now, the classification of the seen colour from brain electrical signals has not been investigated or reported in the related literature. In this study, we aimed to classify EEG brain responses corresponding to blue, green, and red coloured shapes. In addition to the current literature, we focused on ongoing EEG responses instead of using ERP metrics, with visual stimulus-related ERP metrics also compared throughout the study. The feature extraction process was carried out using the Fourier transform to obtain the conventional band power values of the EEG for each stimulus type. Delta, theta, alpha, beta, and gamma-band power values of each one-second period constituted the feature set. In addition to scalp measurements, a second feature set was obtained based on the inverse solution of the EEG waves. Furthermore, we applied one-way ANOVA for the feature selection prior to classification procedures. Four classifiers were implemented using the reduced feature set and the raw one as well. The differences between scalp responses were localized mainly around the temporal and temporoparietal regions. Our ERP-component findings support the fact that additional brain regions among the visual cortex participate in the colour categorization process of the brain. RGB colours were identified using 1s EEG data. Ensemble-KNN and KNN achieved the highest accuracy values (93%) when used either with scalp spectral features or source space features.

Is short-term memory capacity (7±2) really predicted by theta to gamma cycle length ratio?

Several studies suggest that EEG parameters, reflecting top-down processes in the brain, may predict cognitive performance, e.g. short-term memory (STM) capacity. According to Lisman and Idiart’s model, STM capacity is predicted by theta and gamma EEG waves and their ratio. This model suggests that the more periods of gamma band waves fit into one period of theta band waves, the more information can be stored. We replicated the study by Kaminski et al. (2011), which recorded spontaneous EEG activity and measured verbal STM capacity with a modified digit span task from the Wechsler battery. Our study included more subjects and two EEG recording sessions. We discuss the possible limits of EEG correlates of STM capacity as EEG parameters were not stable across the two measurements and no correlation was found between the theta/gamma ratio and performance in the digit span task.

Comparing the effects of crocin at different doses on excitability and long-term potentiation in the CA1 area, as well as the electroencephalogram responses of rats under chronic stress.

Stress adversely affects the cellular and electrophysiological mechanisms of memory; however, crocin has beneficial effects on brain functions. Nonetheless, the electrophysiological effects of using this active saffron component at different doses are not yet studied in rats under chronic restraint stress. Therefore, this study compared the impact of crocin at different doses on the excitability and long-term potentiation(LTP) in the CA1 area of rats, as well as their electroencephalogram(EEG) responses, hippocampal and frontal cortical glucose levels under chronic restraint stress (an emotional stress model).Forty rats were allocated into five groups of control, sham, restraint stress (6h/day/21days), and two stress groups receiving intraperitoneal injections of crocin (30, 60mg/kg/day). Besides measuring the slope and amplitude of field excitatory postsynaptic potentials(fEPSPs) in the input-output and LTP curves, the EEG waves and hippocampal and frontal cortical glucose levels were assessed in all groups.Chronic restraint stress significantly decreased the fEPSP slope and amplitude in the input-output curves and after LTP induction. Both doses of crocin (60and particularly 30mg/kg) significantly improved fEPSP slope and amplitude in the stressed groups. Also, stress and crocin only at a dose of 30mg/kg altered the EEG waves. Hippocampal and frontal cortical glucose levels displayed no significant differences in the experimental groups.Crocin at doses of 60mg/kg/day and particularly 30mg/kg/day reversed the harmful effects of chronic restraint stress on LTP as a cellular memory-related mechanism. However, only the lower dose of crocin affected the electrical brain activity in EEG.

Effects of acute, sub-chronic and chronic chocolate consumption with different percent of cocoa/sugar on memory and EEG waves in rats

Effects of acute, sub-chronic and chronic chocolate consumption with different percent of cocoa/sugar on memory and EEG waves in rats

VARIATION OF COGNITION AND CEREBRAL ACTIVITY WITH SEVERITY OF AIRFLOW OBSTRUCTION IN COPD

Introduction: Cognitive dysfunction is one of the important systemic effect of Chronic Obstructive Pulmonary Disease that affects various cognitive domains in these patients. Aim: Assessment of cognitive function in specific cognitive domains in COPD with various stages of severity as observed by airflow obstruction. Methods: N=68 COPD patients and N=20 controls were matched for age and education. EEG changes, SpO2 and rSO2 were compared between COPD and control group. Cognitive scores and EEG wave patterncompared among different stages of severity in COPD group. Statistical analysis: Was done using SPSS version 18. Two independent t test was used for bivariate testing between COPD and control group. Chi square test applied to assess the frequency distribution of variables among each group. One way analysis of variance (ANOVA) was used to compare mean values of cognitive scores and Chi square test for number(%) of patients with abnormal scores in cognition and slow waves in EEG across groups with different severity of airflow obstruction (FEV1% Predicted). ResultsOncomparison of EEG slow wave, (rSO2) and (SpO2) among COPD and age matched control group, electroencephalographic(EEG) study in COPD group showed slower wave pattern than their age matched healthy counterpart which was statistically significant at p<0.001, so was true about rSO2 of right frontal region in the study group. Among the COPD groups with different severity (moderate, severe, and very severe grades), patients with severe and very severe airflow obstruction performed poorly with Folstein MMSE (statistically significant at p<0.001), more than 50 % of patients had abnormal scoring with CDT in all the three groups and with TMT-A in severe and very severe group. Conclusions:We found relative frontal hypoxemia and EEG slow wave in COPD group compared to healthy control. With severegrades of disease risk of cognitive impairment was noted. Thus, suggesting that low lung functioning may contribute to cognitive disorders by decreasing the oxygen delivery to brain neurons.

Relationship between advanced glycated end products and maternal cognition in gestational diabetes: a case control study

Objective Gestational diabetes (GDM) is a form of glucose intolerance which manifests during pregnancy. There is lack of literature regarding the study of cognitive functions in GDM. Recent evidences suggests an increase in accumulation of serum Advanced glycated end products (AGE’S) during GDM. Accumulation of AGE’s in brain can induce changes in permeability of blood brain barrier and creates oxidative stress and inflammation that can alter cognitive functions. In this study we hypothesize that diagnosis of GDM in pregnancy is related to lower cognitive scores which is correlated to increased serum AGE’s level. Method This was a cross sectional case control study which recruited 60 participants in total consisting of two groups with 30 participants in each - diagnosed cases of GDM and healthy pregnant controls. Subjects were recruited from OPD of Obstetrics & Gynecology department in a tertiary care hospital in South India at gestational age of 32–36 weeks. On the first appointment, biochemical parameters of Fasting plasma glucose (FPG) & HbA1C was measured in both groups. Serum was obtained for testing levels of N Carboxy methyl lysine (N-CML) (a form of AGE). On second appointment, pen and paper neurocognitive tests including Montreal cognitive tests (MOCA) and Trail making test (TMT A & B) was presented. Event related potentials (ERP’s) are time locked EEG wave signals produced in response to a sensory, motor or cognitive event. P300 is an “endogenous” ERP produced by cognitive processing in response to a stimuli presented to subject. P300 wave Latency and amplitude was recorded in both the groups as an objective marker of cognitive processing. Above mentioned biochemical and neurocognitive parameters were compared between both the groups and correlation analysis between serum AGE levels and neurocognitive parameters was performed using SPSS software. Results Biochemical parameters of HbAIC & N-CML(A form of AGE) levels were increased in GDM group (HbA1C 6.01±0.30 and N CML 236.25 ± 68.9) vs Control group (HbA1C 4.11 ± 0.68 and NCML 198.42 ± 44.2). Scores in MOCA were significantly lower in GDM (28 (27–29)) group as compared to controls (24 (23–25)). GDM subjects took significantly greater time to perform TMT A (24.59 ± 2.60 s) test than controls (29.7 ± 1.72 s). Significant changes were not found in P300 Latency & amplitude in GDM group. Decreased MOCA scores and increased duration of TMT A attempt were correlated with increased serum AGE concentration in GDM group. Conclusions Our study indicates the vulnerability of women suffering from GDM to cognitive impairment during pregnancy. Lower scores in cognitive tests were correlated to AGE accumulation in GDM women.

BRAIN ACTIVITY IN ADOLESCENTS WITH DIFFERENT INTERNET ADDICTION RISKS

Aim: To study electroencephalographic (EEG) changes in adolescents with different risks of developing Internet addiction (IA). Methods: This cross-sectional study included 1б-17 years-old adolescents (n = 55) permanently living in the Arctic Russian town of Arkhangelsk. Risk of IA was assessed using the Chen Internet Addiction Scale (CIAS). By the results of the CIAS test all adolescents were divided into three groups: I - minimal risk of IA, II - increased risk to develop IA and III - stable IA pattern. EEG was evaluated by the values of spectral power (|jV2) in each frequency band. Results: Altogether, 25.5 % of adolescents had minimal risk of developing IA, б3.б% belonged to group 2 and 10.9 % had IA. The organized type of alpha activity EEG was found in 79 % of individuals in group I and in 100 % of individuals in group III. This indicates the optimal age ratio of the activity of the reticular formation, cerebral cortex and diencephalic structures. A large number of negative correlations between compulsive symptoms of IA and all types of EEG activity in group I was determined, which may indicate a resistance of these individuals to addiction. The organized EEG type was detected in 54 %, hypersynchronous type - in 14 %, desynchronized type - in 14 %, disorganized type - in 18 % of group II individuals. A disorganized EEG type with a large ratio of theta EEG waves in the frontal-central brain parts in group II may indicate a high limbic-diencephalic excitation. The correlations between the indicators of the withdrawal symptom scale and the EEG theta-activity reflect the psychological discomfort when access to Internet resources is difficult. Conclusions: Most adolescents with minimal risk of IA and symptoms IA have an organized EEG-type while adolescents with IA have this EEG-type only in a half of the cases, which is caused by the heterochronia of the EEG formation in the Arctic residents and the peculiarities of the formation and development of IA.

Acute effects of 2.856\xa0GHz and 1.5\xa0GHz microwaves on spatial memory abilities and CREB-related pathways

This study aimed to evaluate the acute effects of 2.856 GHz and 1.5 GHz microwaves on spatial memory and cAMP response element binding (CREB)-related pathways. A total of 120 male Wistar rats were divided into four groups: a control group (C); 2.856 GHz microwave exposure group (S group); 1.5 GHz microwave exposure group (L group); and 2.856 and 1.5 GHz cumulative exposure group (SL group). Decreases in spatial memory abilities, changes in EEG, structural injuries, and the downregulation of phosphorylated-Ak strain transforming (p-AKT), phosphorylated-calcium/calmodulin-dependent protein kinase II (p-CaMKII), phosphorylated extracellular signal regulated kinase (p-ERK) and p-CREB was observed 6 h after microwave exposure. Significant differences in the expression of p-CaMKII were found between the S and L groups. The power amplitudes of the EEG waves (θ, δ), levels of structural injuries and the expression of p-AKT, p-CaMK II, p-CREB, and p-ERK1/2 were significantly different in the S and L groups compared to the SL group. Interaction effects between the 2.856 and 1.5 GHz microwaves were found in the EEG and p-CREB changes. Our findings indicated that 2.856 GHz and 1.5 GHz microwave exposure induced a decline in spatial memory, which might be related to p-AKT, p-CaMK II, p-CREB and p-ERK1/2.

Epilepsy Associated With Mitochondrial Encephalomyopathy, Lactic Acidosis, and Stroke-Like Episodes.

Objectives: The present study explored the clinical characteristics and prognostic factors of epilepsy in patients with mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS).Methods: Thirty-four MELAS patients were included in the present study. They were diagnosed by clinical characteristics, genetic testing, muscle biopsy, and retrospective analysis of other clinical data. The patients were divided into three groups according to the effects of treatment after at least 2 years of follow-up.Results: Epilepsy was more common in male MELAS patients than in females (20/14). The age of onset ranged from 0.5 to 57 years, with an average of 22.6 years. Patients with epilepsy and MELAS had various forms of seizures. Focal seizures were the most common type affecting 58.82% of patients, and some patients had multiple types of seizures. The abnormal EEG waves were mainly concentrated in the occipital (69.57%), frontal (65.22%) and temporal lobes (47.83%). Overall, the prognosis of patients with epilepsy and MELAS was poor. Poor prognosis was associated with brain atrophy (P = 0.026), status epilepticus (P < 0.001), and use of anti-seizure medications with high mitochondrial toxicity (P = 0.015).Interpretation: Avoiding the application of anti-seizure medications with high mitochondrial toxicity, controlling seizures more actively and effectively, and delaying the occurrence and progression of brain atrophy as much as possible are particularly important to improve the prognosis of patients with MELAS and epilepsy.

Which Factors Affect the Stress of Intraoperative Orthopedic Surgeons by Using Electroencephalography Signals and Heart Rate Variability?

Can we recognize intraoperative real-time stress of orthopedic surgeons and which factors affect the stress of intraoperative orthopedic surgeons with EEG and HRV? From June 2018 to November 2018, 265 consecutive records of intraoperative stress measures for orthopedic surgeons were compared. Intraoperative EEG waves and HRV, comprising beats per minute (BPM) and low frequency (LF)/high frequency (HF) ratio were gathered for stress-associated parameters. Differences in stress parameters according to the experience of surgeons, intraoperative blood loss, and operation time depending on whether or not a tourniquet were investigated. Stress-associated EEG signals including beta 3 waves were significantly higher compared to EEG at rest for novice surgeons as the procedure progressed. Among senior surgeons, the LF/HF ratio reflecting the physical demands of stress was higher than that of novice surgeons at all stages. In surgeries including tourniquets, operation time was positively correlated with stress parameters including beta 1, beta 2, beta 3 waves and BPM. In non-tourniquet orthopedic surgeries, intraoperative blood loss was positively correlated with beta 1, beta 2, and beta 3 waves. Among orthopedic surgeons, those with less experience demonstrated relatively higher levels of stress during surgery. Prolonged operation time or excessive intraoperative blood loss appear to be contributing factors that increase stress.

Brain-Computer Interfacing for Wheelchair Control by Detecting Voluntary Eye Blinks

The human brain is considered as one of the most powerful quantum computers and combining the human brain with technology can even outperform artificial intelligence. Using a Brain-Computer Interface (BCI) system, the brain signals can be analyzed and programmed for specific tasks. This research work employs BCI technology for a medical application that gives the unfortunate paralyzed individuals the capability to interact with their surroundings solely using voluntary eye blinks. This research contributes to the existing technology to be more feasible by introducing a modular design with three physically separated components: a headwear, a computer, and a wheelchair. As the signal-to-noise ratio (SNR) of the existing systems is too high to separate the eye blink artifacts from the regular EEG signal, a precise ThinkGear module is used which acquired the raw EEG signal through a single dry electrode. This chip offers an advanced filtering technology that has a high noise immunity along with an embedded Bluetooth module using which the acquired signal is transferred wirelessly to a computer. A MATLAB program captures voluntary eye blink artifacts from the brain waves and commands the movement of a miniature wheelchair via Bluetooth. To distinguish voluntary eye blinks from involuntary eye blinks, blink strength thresholds are determined. A Graphical User Interface (GUI) designed in MATLAB displays the EEG waves in real-time and enables the user to determine the movements of the wheelchair which is specially designed to take commands from the GUI. The findings from the testing phase unveil the advantages of a modular design and the efficacy of using eye blink artifacts as the control element for brain-controlled wheelchairs. The work presented here gives a basic understanding of the functionality of a BCI system, and provides eye blink-controlled navigation of a wheelchair for patients suffering from severe paralysis.

EEG Signals Analysis for Epileptic Seizure Detection Using DWT Method with SVM and KNN Classifiers

Epilepsy is a critical neurological disorder with critical influences on the way of living of its victims and prominent features such as persistent convulsion periods followed by unconsciousness. Electroencephalogram (EEG) is one of the commonly used devices for seizure recognition and epilepsy detection. Recognition of convulsions using EEG waves takes a relatively long time because it is conducted physically by epileptologists. The EEG signals are analyzed and categorized, after being captured, into two types, which are normal or abnormal (indicating an epileptic seizure). This study relies on EEG signals which are provided by Arrhythmia Database. Thus, this work is a step beyond the traditional database mission of delivering users’ inquiries; instead, this work is to extract insight and knowledge of such data. The features are extracted from the signals by applying the Discrete Wavelet transform (DWT) method on the input EEG signals. Two different algorithms Support vector machine (SVM) and k-nearest neighbours (KNN) are applied to the extracted features. After using the above method, two different types of EEG are expected by using classification, either to be normal (refers to the normal activeness of the brain) or abnormal (refers to the non-normal activeness of the brain, which may involve epilepsy). The evaluation is based on three parameters (Precision, Recall, and Accuracy), and also on the implementation time. In this research, two different methods are used, the first is the DWT with SVM, and the second is the DWT with KNN. With regard to the three-parameter values and implementation time, it turned out that the second method was more efficient than the first because of its higher accuracy.

Recent, Bioelectricity-Related Articles Selected by Ann M. Rajnicek, Media Editor of Bioelectricity.

Recent, Bioelectricity-Related Articles Selected by Ann M. Rajnicek, Media Editor of Bioelectricity.

Stability Analysis of Multiscale Bubble Entropy and Power Metric based Seizure Detection Technique with MLA

In this paper, we explore the use of multiscale bubble entropy and power metric for feature extraction procedure and extend it with MLA and stability analysis to design a reliable multichannel seizure detection technique. First, we represent the multichannel EEG signal in 2D matrix form and then apply AM FM model to exploit the decomposed form of EEG. Thereafter, we construct the complexity coefficient using multiscale bubble entropy analysis from decomposed EEG wave. Then, second feature set is formed by using simple and efficient power procedure to obtain absolute power index and relative power index. Using two machine learning approaches, classification performance of proposed approach is explored to correctly identify the epileptic seizures. To show the robustness of multiscale bubble entropy, the stability analysis is performed with normal EEG dataset. Experimental results demonstrate that our proposed technique can effectively detect the epileptic seizures and achieve a superior classification performance with the ANN classifier compared to KNN classifier. This method provides higher discriminating capability with greater stability, so that they could detect wider range of seizure and thus help advance the current diagnosis system.

Efficacy of Smart EEG Monitoring Amidst the COVID-19 Pandemic

Ever since the COVID-19 pandemic has majorly altered diagnosis and prognosis practices, the need for telemedicine and mobile/electronic health has never been more appreciated. Drastic complications of the pandemic such as burdens on the social and employment status resulting from extended quarantine and physical distancing, has also negatively impacted mental health. Doctors and healthcare workers have seen more than just the lungs affected by COVID-19. Neurological complications including stroke, headache, and seizures have been reported for populations of patients. Most mental conditions can be detected using the Electroencephalogram (EEG) signal. Brain disorders, neurodegenerative diseases, seizure/epilepsy, sleep/fatigue, stress, and depression have certain characteristics in the EEG wave, which clearly differentiate them from normal conditions. Smartphone apps analyzing the EEG signal have been introduced in the market. However, the efficacy of such apps has not been thoroughly investigated. Factors and their inter-relationships impacting efficacy can be studied through a causal model. This short communications/perspective paper outlines the initial premises of a system dynamics approach to assess the efficacy of smart EEG monitoring apps amid the pandemic, that could be revolutionary for patient well-being and care policies.

A Robust Emotion Extraction System from EEG Signal Dataset using Machine Learning

The project entitled “A Robust Emotion Extraction System from EEG signal Dataset using Machine Learning” has been developed using MATLAB. The brain activity produces the different kinds of signals like electrical and magnetic signals. This activity can be recorded using different kind of approaches, which are normally classified as invasive and non-invasive. In invasive methods surgical intervention are made to implant certain device in the brain whereas in non-invasive methods no such intervention is made. Among the different non-invasive methods, Electroencephalography is one of the most commonly used methods to record the brain signals. EEG is regarded as direct and simple non-invasive method to record the brain electrical activity. Current flow in the neurons of the brain is represented as voltage fluctuation (EEG). EEG waves which can be represented as the signal over time are recorded by the electrodes places on scalp over the brain. EEG Asymmetry and Spectral Centroids techniques in extracting unique features for human stress. In our proposed work we have to classify the EEG signal whether that is stress or not. In our proposed work we will extract the features and optimizing Using Genetic Algorithm then we finally classify the EEG signal.