EEG Bands Research Articles

Changes in Spectral Power and Functional Connectivity of Response-Conflict Task After Neurofeedback Training

Neurofeedback training (NFT) teaches self-control of brain activity to the subjects by measuring brain waves and providing a feedback signal in real time. NFT is considered to be a promising tool for healthy people to enhance cognition, however its specific effects on response-conflict are unknown. This study employed 50 healthy participants into two groups (NFT group = 25, control group = 25), in order to explore the NFT effects towards response-conflict resolution phenomenon. Changes in Absolute power and functional connectivity in different bands of response-conflict EEG, were investigated. The EEG of each participant was recorded while performing a response-conflict task before and after NFT. The results show an increase in theta and alpha band powers and functional connectivity. No training specific modulation is observed in beta band functional connectivity. A significant improvement in theta and alpha band functional connectivity is observed in the NFT group that is not present in the control group. These findings provide a strong rationale for the use of NFT in the resolution of response-conflict phenomenon in healthy human beings.

Depression Predictor Model for Farmers using Machine Learning Techniques

There are a few disorders that are the outcome of unbalanced mental state. A very basic one is depression. Depression is a very serious yet common mental ailment that damagingly distresses how a person thinks or feels or acts. Side effects of physical injuries are obvious and regularly agonizing, because of which they are recognized and paid attention to. Symptoms of mental illnesses are not very comprehendible. A lot of individuals don't know about them, including the people who are suffering. This research paper proposes a methodology with an approach to machine learning in order to categorize the subject into 4 distinguished levels of depression, namely normal, mildly depressed, moderately depressed and severely depressed. This procedure is proposed to be carried out using PHQ-9 and DASS-21 questionnaire and the electric EEG bands Alpha, Beta, Delta, Gamma and Theta variations will be obtained via the usage of head kit Neurosky’s Mindwave aid.

The relationship between resting electroencephalogram oscillatory abnormalities and schizotypal personality traits in the first-degree relatives of schizophrenia patients.

Although a large amount of studies have manifested resting state electroencephalogram oscillatory abnormalities in schizophrenia and their first-degree relatives and in schizotype, the results are far from consistent and no research found any relationship between electroencephalograph (EEG) abnormalities and schizotypal personality in first-degree relatives. The present study is to verify the modifications of EEG power spectra in eyes-open resting state of schizophrenia and first-degree relatives, and to investigate associations between EEG band power and schizotypal personality traits in first-degree relatives of schizophrenia patients. Participant samples in this study consisted of 33 healthy normal controls, 35 unaffected first-degree relatives of schizophrenia patients and 35 schizophrenia patients. Group differences in resting EEG band power were examined via repeated-measures analysis of variance, and correlation between EEG power and schizotypal personality traits via Pearson Correlation analysis. The results showed that patients with schizophrenia exhibited increased delta, theta and alpha activity over anterior and central regions in eyes-open resting state compared with that of normal control. Gamma band power was found for the first time to be negatively correlated to schizotypal personality traits in first-degree relatives of schizophrenia patients. To conclusion, these findings suggested that low-frequency EEG activity might be neural manifestations of pathophysiological changes in the brain of schizophrenia, and gamma band activity might be an approach to measure the genetic liability of the disorder.

Scoping Review of EEG Studies in Construction Safety.

Construction safety is critical in the success of a project. A considerable amount of effort has been placed on research and practice in order to reduce the potential risks on the construction site. Recent application of electroencephalogram (EEG) to construction research enables researchers to gain insight into construction workers’ physical and mental status during construction tasks. By summarizing existing studies that involve EEG and construction safety, the literature review aims to provide practical suggestions for future research and on-site safety management. The literature search and inclusion process included eleven eligible studies. Comprehensive analysis was conducted based on primary and secondary measures. The primary measures considered the frequency bands of EEG and the channels for detecting electrical activity of the brain. The secondary measures that were involved with physical and mental status with respect to EEG signal variations as a result of task, working hour, and work conditions. Although the field of study that combines EEG measures with construction tasks is still emerging, it is worth continuous attention in the future, as relevant findings would be of great value to the safety management and risk control in the construction industry.

Emotional state detection based on common spatial patterns of EEG

The application of EEG-based emotional states is one of the most vital phases in the context of neural response decoding. Emotional response mostly appears in the presence of visual, auditory, tactile, and gustatory arousals. In our work, we use visual stimuli to evaluate the emotional feedback. One of the best performing methods in emotion estimation applications is the common spatial patterns (CSP). We implement CSP method in addition to the conventional Welch power spectral density-based analysis. Experimental results and topographies on the collected EEG data show that the CSP spatial filtering method implies the relationship between EEG bands, EEG channels, neural efficiency and emotional stimuli types.

Use of EEG for Predicting Treatment Response to Transcranial Magnetic Stimulation in Obsessive Compulsive Disorder.

Aim. In this study we assessed the predictive power of quantitative EEG (qEEG) for the treatment response to right frontal transcranial magnetic stimulation (TMS) in obsessive compulsive disorder (OCD) using a machine learning approach. Method. The study included 50 OCD patients (35 responsive to TMS, 15 nonresponsive) who were treated with right frontal low frequency stimulation and identified retrospectively from Uskudar Unversity, NPIstanbul Brain Hospital outpatient clinic. All patients were diagnosed with OCD according to the DSM-IV-TR and DSM-5 criteria. We first extracted pretreatment band powers for patients. To explore the prediction accuracy of pretreatment EEG, we employed machine learning methods using an artificial neural network model. Results. Among 4 EEG bands, theta power successfully discriminated responsive from nonresponsive patients. Responsive patients had more theta powers for all electrodes as compared to nonresponsive patients. Discussion. qEEG could be helpful before deciding about treatment strategy in OCD. The limitations of our study are moderate sample size and limited number of nonresponsive patients and that treatment response was defined by clinicians and not by using a formal symptom measurement scale. Future studies with larger samples and prospective design would show the role of qEEG in predicting TMS response better.

Neurophysiology of threat processing bias in combat-related post-traumatic stress disorder.

Post‐traumatic stress disorder (PTSD) is a debilitating condition that may develop after experiencing a traumatic event. Combat exposure increases an individual's chance of developing PTSD, making veterans especially susceptible to the disorder. PTSD is characterized by dysregulated emotional networks, memory deficits, and a hyperattentive response to perceived threatening stimuli. Recently, there have been a number of imaging studies that show structural and functional abnormalities associated with PTSD; however, there have been few studies utilizing electroencephalography (EEG). The goal of this study was to characterize **EEG brain dynamics in individuals with PTSD, in order to better understand the neurophysiological underpinnings of some of the salient features of PTSD, such as threat‐processing bias. Veterans of Operation Enduring Freedom/Iraqi Freedom completed an implicit visual threat semantic memory recognition task with stimuli that varied on both category (animals, items, nature, and people) and feature (threatening and nonthreatening) membership, including trauma‐related stimuli. Combat veterans with PTSD had slower reaction times for the threatening stimuli relative to the combat veterans without PTSD (VETC). There were trauma‐specific effects in frontal regions, with theta band EEG power reductions for the threatening combat scenes in the PTSD patients compared to the VETC group. Additionally, a moderate negative correlation was observed between trauma‐specific frontal theta power and hyperarousal symptoms as measured by clinically administered PTSD scale. These findings complement and extend current models of cortico‐limbic dysfunction in PTSD. The moderate negative correlation between frontal theta power and hyperarousal endorsements suggests the utility of these measures as therapeutic markers of symptomatology in PTSD patients.

Individual alpha frequency increases during a task but is unchanged by alpha-band flicker.

Visual perception fluctuates in sync with ongoing neural oscillations in the delta, theta, and alpha frequency bands of the human EEG. Supporting the relationship between alpha and perceptual sampling, recent work has demonstrated that variations in individual alpha frequency (IAF) correlate with the ability to discriminate one from two stimuli presented briefly in the same location. Other studies have found that, after being presented with a flickering stimulus at alpha frequencies, perception of near-threshold stimuli fluctuates for a short time at the same frequency. Motivated by previous work, we were interested in whether this alpha entrainment involves shifts in IAF. While recording EEG, we tested whether two-flash discrimination (a behavioral correlate of IAF) can be influenced by~1s of rhythmic visual stimulation at two different alpha frequencies (8.3 and 12.5Hz). Speaking against the bottom-up malleability of IAF, we found no change in IAF during stimulation and no change in two-flash discrimination immediately afterward. We also found synchronous activity that persisted after 12.5Hz stimulation, which suggests that a separate source of alpha was entrained. Importantly, we replicated the correlation between IAF and two-flash discrimination in a no-stimulation condition, demonstrating the sensitivity of our behavioral measure. We additionally found that IAF increased during the task compared to rest, which demonstrates that IAF is influenced by top-down factors but is not involved in entrainment.

A study on the effects of traditional and olfaction enhanced multimedia on pleasantness classification based on brain activity analysis

Human emotions are recognized in response to content engaging one (audio music) or two human senses (videos). An enhanced sensation with a more realistic feel could be achievable by engaging more than two human senses. In this study, olfaction enhanced multimedia content is generated by synchronizing traditional multimedia content with an olfaction dispenser for engaging olfactory sense in addition to vision and auditory senses. Brain activity of 20 participants (10 males and 10 females) is recorded with a commercially available EEG headband, while engaging with traditional and olfaction enhanced multimedia content. The human brain activity is used to analyze and differentiate the content engaging two (traditional multimedia content) or more than two (olfaction enhanced multimedia content) human senses. For brain activity analysis, we apply a t-test on the power spectra of five frequency sub-bands (delta, theta, alpha, beta, and gamma) of the acquired EEG data in response to traditional and olfaction enhanced multimedia. We observe that alpha, theta, and delta bands are significant in discriminating the response to traditional and olfaction enhanced multimedia content. High brain activity is observed in alpha, theta, and delta bands of frontal channels, while experiencing the olfaction enhanced multimedia content. A user-independent pleasantness classification based on human brain activity is also presented, where classification performance is measured using 10-fold cross validation. We extract features in frequency domain i.e., rational asymmetry (RASM) and differential asymmetry (DASM) from five EEG bands to classify two pleasantness states based on their valence scores using support vector machine (SVM) classifier. Features are further selected based on EEG electrode pair positions and sub-bands. We observed that RASM and DASM features selected from delta band (olfaction enhanced content), and alpha or gamma bands (traditional multimedia content) gives best classification accuracy. We achieved an accuracy of 75%, sensitivity of 77.7%, and specificity of 72.7% in response to olfaction enhanced multimedia content and an accuracy of 68.7%, sensitivity of 71.4%, and specificity of 69.2% in response to traditional multimedia content in classifying pleasant and unpleasant states using SVM. We observed that classification of pleasant state was comparatively better with olfaction enhanced multimedia content than traditional multimedia content.

A Neuroscientific Approach to Explore Consumers’ Intentions Towards Sustainability within the Luxury Fashion Industry

Little is presently known about customers’ expectations and the unspoken relevant factors which lead them to prefer or not sustainable luxury products. This study aimed to deepen the understanding of luxury consumers’ implicit intentions towards sustainability by using, for the first time, a neuroscientific approach applied to the luxury fashion domain. A greater cortical activity related to cognitive and emotional aspects was hypothesized for luxury sustainability-oriented consumers than for non-sustainability-oriented subjects when presented with sustainability-related cues. Sixteen luxury consumers were divided into two groups according to their sensitivity towards sustainability issues. They were asked to observe a set of 10 stimuli depicting sustainability issues and then to interact with a salesperson while their cortical activity was recorded by an electroencephalogram (EEG). Frequency band analysis revealed higher levels of beta, delta, and theta band EEG activity in temporoparietal than frontocentral areas when observing pictures related to sustainability and a specific right temporoparietal theta band activation for the Nonsustainable Group. An increased level of knowledge of sustainability themes was confirmed by the correct detection of stimuli valence and a significant presence of delta power when the salesperson explained the brand’s sustainable policy. The specific brain responses related to sensitivity towards sustainability and the different effect of knowledge on sustainability topics based on group differences are discussed here in light of emotional behavior.

An EEG-based stereoscopic research of the PSD differences in pre and post 2D&3D movies watching

Despite knowing the reality of three-dimensional (3D) technology in the form of eye fatigue, this technology continues to be retained by people (especially the young community). To check the influences of the human brain's power spectrum density (PSD) pre and post a 2D and 3D movie watching, a five-member test group was arranged. In this study, electroencephalogram (EEG) was used as a neuroimaging method. EEG recordings of five individuals were taken both before and after watching 2D and 3D movies. The main framework of this study was to analyze the effects of power spectrum density (PSD) of the human brain by testing all EEG frequency bands before and after 2D and 3D movie watching, also to determine effective brain lobes, and consequently, EEG channels. After 2D/3D EEG recording, this record was divided into three stages for analysis. These stages consisted of Relax, Watching, and Rest. This benchmarking analysis included I) before and after watching the 2D movie (R2b and R2a), II) before and after watching the 3D movie (R3b and R3a), and III) after watching the 2D/3D movie (R2a and R3a). In the Relax and Rest stages, the 2D/3D EEG power differences in all channels of brain regions for the five EEG bands, including delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ), were analyzed and compared. The PSD based on short-time Fourier transform (STFT) was used to select the dominant bands in this study. Feature extraction was performed on the preprocessed EEG signals using STFT and discrete wavelet transform (DWT). In the 2D analysis, δ, θ, α, and β acted as dominant bands, in 3D, δ, θ, and α were dominant bands, and in 2D/3D, δ and α were selected as meaningful and dominant bands. Partial least-squares regression (PLSR) and support vector machine (SVM) classification algorithms were considered in order to classify the obtained R2a and R3a EEG signals. After the dominant band selection, the correct choice of effective channel combinations in these bands resulted in the percentage of high success of classifiers for Stage III. Taking into account the optimum number of channels, the best percentage of classification accuracy for the average of PLSR and SVM classification results by considering the best channels of SVM and DWT feature extraction method is 91.83% and 80.73% respectively.

Role of EEG for Diagnosis of Alzheimer Disease

In the recent years, costliest Alzheimer disease (AD) is now primary reason for the cause of death. An early finding is essential as there is no cure for severe AD. Despite recent advances, early finding of Alzheimer disease from electroencephalography (EEG) remains a difficult job. In this paper, we focus a spectral and signal complexity measures through which such early findings can possibly be improved. Power spectral and nonlinear features, which have been utilized for classification of Alzheimer disease subjects (ADS) from the normal healthy subject (NHS). So far, the power in the various EEG bands has been intensely analyzed. The main aim of this research article is to study the power and nonlinear analysis for the finding of AD to consider as a probable biomarker to recognize AD subject and normal healthy subject. Relative power (RP) was independently calculated from various EEG bands which indicate the slowing of EEG signals acknowledge the Alzheimer disease subjects. In this study, EEGs signal had been acquired at the rest condition from 20 normal healthy subject whose age around 60 years along with same number of Alzheimer disease subjects. The result shows that relative power is increased towards lower frequencies while decreased towards higher frequencies in AD. Such analysis of power may additionally explore to differentiate Alzheimer disease’s stages.

An iterative cross-subject negative-unlabeled learning algorithm for quantifying passive fatigue

Objective. This paper proposes an iterative negative-unlabeled (NU) learning algorithm for cross-subject detection of passive fatigue from labelled alert (negative) and unlabeled driving EEG data. Approach. Unlike other studies which used manual labeling of the fatigue state, the proposed algorithm (PA) first iteratively uses 29 subjects’ alert data and unlabeled driving data to identify the most fatigued block of EEG data in each subject in a cross-subject manner. Subsequently, the PA computes subjects’ driving fatigue score. Repeated measures correlations of the score to EEG band powers are then performed. Main results. The PA yields an averaged accuracy of 93.77% ± 8.15% across subjects in detecting fatigue, which is significantly better than the various baselines. The fatigue scores obtained are also significantly positively correlated with theta band power and negatively correlated with beta band power that are known to respectively increase and decrease in presence of passive fatigue. There is a strong negative correlation with alpha band power as well. Significance. The proposed iterative NU learning algorithm is capable of labelling and quantifying the most fatigued block in a cross-subject manner despite the lack of ground truth in the fatigue levels of unlabeled driving EEG data. Together with the significant correlations with theta, alpha and beta band power, the results show promise in the application of the proposed algorithm to detect fatigue from EEG.

EEG Signal Analysis for Automated Epilepsy Seizure Detection Using Wavelet Transform and Artificial Neural Network

Electroencephalogram (EEG) measures electrical activity of the brain and proffers valuable insight of the brain dynamics. Accurate and careful analysis of EEG signal plays a prominent role in the diagnosis of brain diseases like epilepsy, brain tumor. EEG is the most significant method used for epilepsy monitoring, diagnosis and rehabilitation. A patient-specific seizure detection model has been developed using Haar wavelet and Artificial Neural Network. HAAR Wavelet decomposition of multi-channel EEG with five scales is made and three frequency bands of EEG selected for the consequent process. The conventional Haar wavelet transform (HWT) is replaced by a modified Haar wavelet transform whereas the number of multiplications and additions are reduced. The Haar wavelet reduces computational complexity from the existing Haar wavelet structure which consumes only 1–3 ms based on the decomposition level to detect epilepsy.

EEG power spectrum analysis for schizophrenia during mental activity.

Cognitive dysfunction is a core defect for schizophrenia subjects. This is due to structural and functional abnormalities of the brain which can be determined using Electroencephalogram (EEG). The objective of this study is to analyze EEG in patients with schizophrenia using power spectral density during mental activity. The subjects included in this study are 52 schizophrenia subjects and 29 Normal subjects. EEG is recorded under resting condition and during mental activity. Two modified odd ball paradigms are designed to stimulate mental activity and named as stimulus 1 and stimulus 2. EEG signal is filtered using FIR band pass filter to extract delta, theta, alpha, and beta band EEG. This method measures powers of each band using Welch power spectral density method called absolute power. The absolute power of alpha band is low and beta band is high for schizophrenia subjects compared to normal subjects during rest and two stimuli. Student's t-test is used to find the significant features (p < 0.05) at each recording condition. The significant features from each recording condition are used to classify Schizophrenia using both BPN and SVM classifier. SVM classifier is produced maximum sensitivity of 91% when features from allrecording conditions are combined together. Thus this work concludes that the mental activity EEG supports for classifying Schizophrenia from normal and hence absolute band powers can be used as features to identify Schizophrenia.

A New Method of Diagnosing Attention-Deficit Hyperactivity Disorder in Male Patients by Quantitative EEG Analysis.

Attention-deficit hyperactivity disorder (ADHD) is one of the most common neuropsychiatric disorders of childhood. Diagnosis of ADHD is based on core symptoms and checklists. However, these are both subjective, which can lead to the problems of overdiagnosis and underdiagnosis. Elevated theta/beta ratio (TBR) of EEG band has been approved by the US Food and Drug Administration as a tool to assist in the diagnosis of ADHD. However, several recent studies have demonstrated that there are no significant differences in TBR between people with and without ADHD. In this study, we attempted to develop a new method for differentiating between male with and without ADHD by analyzing EEG features. Thirty boys with ADHD combined type (aged 8 years 5 months ± 1 year 11 months) and 30 age-matched controls (aged 8 years 5 months ± 1 year 8 months) were enrolled in this study. A classification analysis-based approach comprising training and classification phases was developed for classifying each subject's EEG features as ADHD or non-ADHD. Eight crucial feature descriptors were selected and ranked based on the t test. Compared with TBR in our study, the developed method had a higher area under the curve (87.78%), sensitivity (80.0%), and specificity (80.0%). Our method is more precise than using TBR in the diagnosis of ADHD. This newly developed method is a useful tool in identifying patients with ADHD and might reduce the possibility of overdiagnosis and underdiagnosis.

Increased Sample Entropy in EEGs During the Functional Rehabilitation of an Injured Brain.

Complex nerve remodeling occurs in the injured brain area during functional rehabilitation after a brain injury; however, its mechanism has not been thoroughly elucidated. Neural remodeling can lead to changes in the electrophysiological activity, which can be detected in an electroencephalogram (EEG). In this paper, we used EEG band energy, approximate entropy (ApEn), sample entropy (SampEn), and Lempel–Ziv complexity (LZC) features to characterize the intrinsic rehabilitation dynamics of the injured brain area, thus providing a means of detecting and exploring the mechanism of neurological remodeling during the recovery process after brain injury. The rats in the injury group (n = 12) and sham group (n = 12) were used to record the bilateral symmetrical EEG on days 1, 4, and 7 after a unilateral brain injury in awake model rats. The open field test (OFT) experiments were performed in the following three groups: an injury group, a sham group, and a control group (n = 10). An analysis of the EEG data using the energy, ApEn, SampEn, and LZC features demonstrated that the increase in SampEn was associated with the functional recovery. After the brain injury, the energy values of the delta1 bands on day 4; the delta2 bands on days 4 and 7; the theta, alpha, and beta bands and the values of ApEn, SampEn, and LZC of the cortical EEG signal on days 1, 4 and 7 were significantly lower in the injured brain area than in the non-injured area. During the process of recovery for the injured brain area, the values of the beta bands, ApEn, and SampEn of the injury group increased significantly, and gradually became equal to the value of the sham group. The improvement in the motor function of the model rats significantly correlated with the increase in SampEn. This study provides a method based on EEG nonlinear features for measuring neural remodeling in injured brain areas during brain function recovery. The results may aid in the study of neural remodeling mechanisms.

Combining ICA Clustering and Power Spectral Density for Feature Extraction of Mental Fatigue of Spinal Cord Injury Patients.

This paper presents the combination of clustering-based independent component analysis (ICASSO) and power spectral density (PSD) as a features extractor of mental fatigue from spinal cord injury (SCI) patients. Initially, the results show that SCI and abled-bodied groups have no differences in EEG for alert and mental fatigue states. Further, the coefficient determination (R2) is calculated for testing the variation of data alert vs. fatigue on the SCI group, resulting in a lower R2 for proposed combination of ICASSO and PSD method compared to the PSD method only. With the lower R2 values, this shows that the proposed method ICASSO and PSD is able to provide superior distinction for separating fatigue vs. alert data variation. The statistical significance is found across four EEG bands and EEG channels.

Comparison between Daytime and Nighttime Scenery Focusing on Restorative and Recovery Effect

The overall purpose of this study was to investigate psycho-physiological variations in human bodies by observing visual images of daytime and nighttime scenery to focus on restorative and recovery effects. Unlike previous studies that have focused on the natural versus built environments, this study aims to compare restorative and recovery potentials between daytime and nighttime. The experiment was conducted by showing a total of 12 images to 60 participants in order to measure the brain response with an electroencephalogram (EEG). As measures of the psychological impact of the images, perceived restorative and recovery scales were used. The self-reported data indicates that daytime sceneries are rated more positively than nighttime sceneries in terms of restorative and recovery effects. According to the EEG results, restorative and recovery feelings have negative relationships with the relative theta band, while positive relationships are shown with the relative alpha band. The correlation analysis between EEG bands and brain regions showed a significant correlation (p &lt; 0.05) with 46 pairs for the daytime scenery stimuli and 52 pairs for the nighttime scenery stimuli. Through the results of the study, we conclude that daytime and nighttime scenery affect restorative feelings and the human brain response through both verbal and non-verbal methods.

Movement related activity in the μ band of the human EEG during a robot-based proprioceptive task.

Innovative research in the fields of prosthetic, neurorehabilitation, motor control and human physiology has been focusing on the study of proprioception, the sense through which we perceive the position and movement of our body, and great achievements have been obtained regarding its assessment and characterization. However, how proprioceptive signals are combined with other sensory modalities and processed by the central nervous system to form a conscious body image, is still unknown. Such a crucial question was addressed in this study, which involved 23 healthy subjects, by combining a robot-based proprioceptive test with a specific analysis of electroencephalographic activity (EEG) in the $\mu$ frequency band (8-12 Hz). We observed important activation in the motor area contralateral to the moving hand, and besides, a substantial bias in brain activation and proprioceptive acuity when visual feedback was provided in addition to the proprioceptive information during movement execution. In details, brain activation and proprioceptive acuity were both higher in case of movements performed with visual feedback. Remarkably, we also found a correlation between the level of activation in the brain motor area contralateral to the moving hand and the value of proprioceptive acuity.