Electroencephalographic Measures Research Articles

EEG coherence as a diagnostic tool to measure the initial stages of Parkinson Disease

Although Parkinson Disease was described a long time ago by James Parkinson and several biomarkers were used to predict the symptoms of PD, there is no accepted tool to distinguish the initial stages of this pathology. The present hypothesis discusses the Coherence Function, an Electroencephalography measure which could be used as a simple, and low-cost tool to describe the onset of cardinal signals of PD. Our hypothesis is based on three factors: beta frequency related to movement, motor action over particular cortical regions, and cortical coupling between cortical areas involved in the execution of voluntary movement. We believe that these factors support our hypothesis pointing out coherence function as an interesting measure to detect initial stages of PD.

Multifractal Dynamic Functional Connectivity in the Resting-State Brain.

Assessing the functional connectivity (FC) of the brain has proven valuable in enhancing our understanding of brain function. Recent developments in the field demonstrated that FC fluctuates even in the resting state, which has not been taken into account by the widely applied static approaches introduced earlier. In a recent study using functional near-infrared spectroscopy (fNIRS) global dynamic functional connectivity (DFC) has also been found to fluctuate according to scale-free i.e., fractal dynamics evidencing the true multifractal (MF) nature of DFC in the human prefrontal cortex. Expanding on these findings, we performed electroencephalography (EEG) measurements in 14 regions over the whole cortex of 24 healthy, young adult subjects in eyes open (EO) and eyes closed (EC) states. We applied dynamic graph theoretical analysis to capture DFC by computing the pairwise time-dependent synchronization between brain regions and subsequently calculating the following dynamic graph topological measures: Density, Clustering Coefficient, and Efficiency. We characterized the dynamic nature of these global network metrics as well as local individual connections in the networks using focus-based multifractal time series analysis in all traditional EEG frequency bands. Global network topological measures were found fluctuating–albeit at different extent–according to true multifractal nature in all frequency bands. Moreover, the monofractal Hurst exponent was found higher during EC than EO in the alpha and beta bands. Individual connections showed a characteristic topology in their fractal properties, with higher autocorrelation owing to short-distance connections–especially those in the frontal and pre-frontal cortex–while long-distance connections linking the occipital to the frontal and pre-frontal areas expressed lower values. The same topology was found with connection-wise multifractality in all but delta band connections, where the very opposite pattern appeared. This resulted in a positive correlation between global autocorrelation and connection-wise multifractality in the higher frequency bands, while a strong anticorrelation in the delta band. The proposed analytical tools allow for capturing the fine details of functional connectivity dynamics that are evidently present in DFC, with the presented results implying that multifractality is indeed an inherent property of both global and local DFC.

Can we feel like being neither alert nor sleepy? The electroencephalographic signature of this subjective sub-state of wake state yields an accurate measure of objective sleepiness level.

Accurate measurement of objective level of sleepiness can have important implications for experimental and field studies of sleep deprived individuals. We proposed to accurately quantify changes in sleepiness level with single electroencephalographic (EEG) measures obtained from EEG spectra consisting of 16 spectral powers within the frequency interval from 1 to 16 Hz. The EEG signal was recorded every other hour from 19:00 of Friday to 19:00 of Sunday in 48 study participants. The differential spectra were calculated for the 1st minute with eyes closed as the differences between EEG spectra for pairs of distinct subjective sub-states (alert, neither alert nor sleepy, sleepy, and very sleepy sub-states scored on the Karolinska Sleepiness Scale as 3, 5, 7, and 9, respectively).The differential spectra were calculated for the sub-samples of those participants who succeeded and failed to succeed in completing all 25 EEG recording sessions (n = 25 and 23, respectively) and for the addition sample of 130 participants deprived from sleep for only one night. Single spectral EEG measures were then calculated by summation of 16 spectral powers weighted by a differential spectrum. The strongest correlation coefficients (0.981, 0.987, and 0.985) were attained between the time courses of subjective and objective measures when data on 130, 23 and 25 participants, respectively, were used for calculation of frequency waiting curve differentiating alert sub-sate either from sleepy sub-state or from neither alert nor sleepy sub-state. We recommended implementation of the proposed objective measure into experimental procedures requiring accurate estimation of objective sleepiness level.

Support vector machine classification of brain states exposed to social stress test using EEG-based brain network measures

Stress is one of the most significant health problems in the 21st century, and should be dealt with due to the costs of primary and secondary cares of stress-associated psychological and psychiatric problems. In this study, the brain network states exposed to stress were monitored based on electroencephalography (EEG) measures extracted by complex network analysis. To this regard, 23 healthy male participants aged 18–28 were exposed to a stress test. EEG data and salivary cortisol level were recorded for three different conditions including before, right after, and 20min after exposure to stress. Then, synchronization likelihood (SL) was calculated for the set of EEG data to construct complex networks, which are scale reduced datasets acquired from multi-channel signals. These networks with weighted connectivity matrices were constructed based on original EEG data and also by using four different waves of the recorded signals including δ, θ, α, and β. In addition to these networks with weighted connectivity, networks with binary connectivity matrices were also derived using threshold T. For each constructed network, four measures including transitivity, modularity, characteristic path length, and global efficiency were calculated. To select the sensitive optimal features from the set of the calculated measures, compensation distance evaluation technique (CDET) was applied. Finally, multi-class support vector machine (SVM) was trained in order to classify the brain network states. The results of testing the SVM models showed that the features based on the original EEG, α and β waves have got better performances in monitoring the brain network states.

Psychological and electrophysiological indices of inattention in hoarding

Individuals with elevated hoarding symptoms report elevated symptoms of ADHD and these symptoms are related to impaired daily functioning. Neuropsychological studies have found specific deficits in attention, and a recent review of attentional data from numerous hoarding studies concluded that inattention likely represents an etiological factor in hoarding, rather than a comorbidity. Our study aimed to examine which symptoms of ADHD, inattention or hyperactivity, are related to hoarding symptom severity, and whether individuals with hoarding symptoms display a neurophysiological marker of poor attention (Theta/Beta Ratio; THBR) that might explain these associations. The THBR indexes theta power relative to beta power in the frontal cortex and is often atypical in individuals with ADHD. We hypothesised that individuals would report more severe problems with inattention and would exhibit an elevated theta/beta ratio relative to a healthy control group. We also predicted that any relationship between hoarding and inattention would be independent of anxiety and depression symptoms. 17 hoarding-symptomatic participants and 16 healthy control participants completed self-report measures relating to ADHD, hoarding and general psychopathology, and then underwent resting measures of electroencephalography (EEG). Individuals with hoarding symptoms reported greater difficulties with inattention and hyperactivity, however they did not exhibit an elevated theta/beta ratio. When taking into account recent anxiety and depression, only inattention predicted hoarding symptom severity. Further investigations may help clarify this association and help inform attention-based treatments for hoarding.

Performance monitoring and post-error adjustments in adults with attention-deficit/hyperactivity disorder: an EEG analysis

Recently, research into attention-deficit/hyperactivity disorder (ADHD) has focused increasingly on its neurobiological underpinnings, revealing (among other things) frontal lobe alterations. Specifically, action-monitoring deficits have been described, including impaired behavioural adjustments following errors. Our aim was to examine the neurophysiological background of post-error behavioural alterations in an adult ADHD sample for the first time, hypothesizing that people with ADHD would differ from controls in neurophysiological markers of cognitive preparation and stimulus processing, specifically following errors. In total, 34 people with ADHD and 34 controls participated in an electroencephalography measurement while performing a flanker task. The final number of electroencephalography samples included in the analyses ranged from 23 to 28. We recorded event-related potentials for the erroneous response itself (error-related negativity) and for events following errors (intertrial interval: contingent negative variation; next flanker stimulus: P300). Over frontal electrode sites, error-related negativity amplitudes were significantly reduced in people with ADHD across response conditions. Both groups showed reduced P300 amplitudes on flanker stimuli following errors. Moreover, during the intertrial interval, patients exhibited significantly reduced contingent negative variation, specifically following errors. At the behavioural level, we observed no significant group differences in post-error data. Only adults were examined (no longitudinal data). Based on previous reports of post-error behavioural alterations in childhood samples, we conclude that people with ADHD develop compensatory strategies across the lifespan that lead to inconspicuous post-error behaviour in adulthood. Neurophysiologically, however, subtle alterations remain, indicating a perseverance of at least some frontal lobe deficits in people with ADHD who are partially medicated, particularly with respect to action-monitoring and post-error adaptation.

Extracranial Estimation of Neural Mass Model Parameters Using the Unscented Kalman Filter

Data assimilation, defined as the fusion of data with preexisting knowledge, is particularly suited to elucidating underlying phenomena from noisy/insufficient observations. Although this approach has been widely used in diverse fields, only recently have efforts been directed to problems in neuroscience, using mainly intracranial data and thus limiting its applicability to invasive measurements involving electrode implants. Here we intend to apply data assimilation to non-invasive electroencephalography (EEG) measurements to infer brain states and their characteristics. For this purpose, we use Kalman filtering to combine synthetic EEG data with a coupled neural-mass model together with Ary’s model of the head, which projects intracranial signals onto the scalp. Our results show that using several extracranial electrodes allows to successfully estimate the state and pa rameters of the neural masses and their interactions, whereas one single electrode provides only a very partial and insufficient view of the system. The superiority of using multiple extracranial electrodes over using only one, be it intra- or extracranial, is shown in different dynamical behaviours. Our results show potential towards future clinical applications of the method.

Extraction of SSVEPs-Based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients

Inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the robustness of brain systems. In this study, we present a novel application of multi-scale relative inherent fuzzy entropy using repetitive steady-state visual evoked potentials (SSVEPs) to investigate EEG complexity change between two migraine phases, i.e. inter-ictal (baseline) and pre-ictal (before migraine attacks) phases. We used a wearable headband EEG device with O1, Oz, O2 and Fpz electrodes to collect EEG signals from 80 participants (40 migraine patients and 40 healthy controls [HCs]) under the following two conditions: during resting state and SSVEPs with five 15-Hz photic stimuli. We found a significant enhancement in occipital EEG entropy with increasing stimulus times in both HCs and patients in the inter-ictal phase but a reverse trend in patients in the pre-ictal phase. In the 1st SSVEP , occipital EEG entropy of the HCs was significantly higher than that of patents in the pre-ictal phase (FDR-adjusted p < 0.05). Regarding the transitional variance of EEG entropy between the 1st and 5th SSVEPs, patients in the pre-ictal phase exhibited significantly lower values than patients in the inter-ictal phase (FDR-adjusted p < 0.05). Furthermore, in the classification model, the AdaBoost ensemble learning showed an accuracy of 81% and AUC of 0.87 for classifying inter-ictal and pre-ictal phases. In contrast, there were no differences in EEG entropy among groups or sessions by using other competing entropy models, including approximate entropy, sample entropy and fuzzy entropy on the same dataset. In conclusion, inherent fuzzy entropy offers novel applications in visual stimulus environments and may have the potential to provide a pre-ictal alert to migraine patients.

How to evaluate emotional experiences in television drama series: improving viewer evaluations using a combination of psychophysiological measurements and self-reports

ABSTRACTThis paper describes experiences with new methodological approaches utilised in early viewer evaluations of a public service television drama series. We would like to challenge self-reports as the only source of data in viewer evaluations of television drama series. Based on two viewer evaluation studies of one-hour drama series, both conducted in an applied research setting, we outline pros and cons of electroencephalography (EEG) and skin conductance (SC) measurements tested in combination with in-depth interviews and written self-reports. In conclusion, we suggest a research design consisting of skin conductance measurement with written self-reports as the preferred method for viewer evaluations of television drama series conducted in an applied research setting where time and money is an important constraint.

Studying appraisal-driven emotion processes: taking stock and moving to the future

ABSTRACTAppraisal theories of emotion, and particularly the Component Process Model, have claimed over the past three decades that the different components of the emotion process (action tendencies, physiological reactions, expressions, and feeling experiences) are essentially driven by the results of multi-level cognitive appraisals and that the feeling component constitutes a central integration and representation of these processes. Given the complexity of the proposed architecture of emotion generation, comprehensive experimental tests of these predictions are difficult to perform and thus evidence has been slow to appear. Complementing earlier work on self-reported appraisal, a massive amount of empirical results from studies with experimental designs based on appraisal manipulation, using electroencephalographic and electromyographic measures, now confirms many of the theoretical predictions with respect to the effect of different appraisal checks, their interactions, and their exact timing. A major issue for future research is the nature of the coherence or synchronisation of the appraisal-driven components in the unfolding emotion process. It is suggested that interdisciplinary multi-team research will be needed to face the theoretical and methodological challenges of experimentally investigating the dynamics of the emotion process.

Comparison of Parametric and Non-parametric EEG Feature Extraction Methods in Detection of Pediatric Migraine without Aura

Migraine headache without aura is the most common type of migraine especially among pediatric patients. It has always been a great challenge of migraine diagnosis using quantitative electroencephalography measurements through feature classification. It has been proven that different feature extraction and classification methods vary in terms of performance regarding detection and diagnostic accuracy. Previous work on the subject was controversial, hence a comparison of these methods seems necessary. The aim of this research is to compare two parametric and non-parametric feature extraction methods and also two classification methods in order to obtain optimal combinations of diagnostic accuracy. Having recorded background EEG from 24 pediatric migraineurs and 19 control subjects, data was processed by Welch's and Yule-Walker's methods. Features were selected using genetic algorithm, and then given to a support vector machine and the linear discriminant analysis for the classification. Accuracy was calculated for all combinations having the dominant frequency and the correlated absolute power of each EEG wave band (theta, alpha, and beta) and for all wave bands combined. The highest migraine detection accuracy of 93% was obtained utilizing Welch's method for EEG feature extraction alongside support vector machine for a classifier. Besides, Yule-Walker autoregressive method showed better performance than Welch's, when only power bands (and not the dominant frequency) were used as classification input. The superiority of Welch's method over Yule-Walker's and the support vector machine over linear discriminant analysis can be great help for further researches on computer aided EEG-based diagnosis of migraine.

Dual-electrode motion artifact cancellation for mobile electroencephalography

Objective. Our purpose was to evaluate the ability of a dual electrode approach to remove motion artifact from electroencephalography (EEG) measurements. Approach. We used a phantom human head model and robotic motion platform to induce motion while collecting scalp EEG. We assembled a dual electrode array capturing (a) artificial neural signals plus noise from scalp EEG electrodes, and (b) electrically isolated motion artifact noise. We recorded artificial neural signals broadcast from antennae in the phantom head during continuous vertical sinusoidal movements (stationary, 1.00, 1.25, 1.50, 1.75, 2.00 Hz movement frequencies). We evaluated signal quality using signal-to-noise ratio (SNR), cross-correlation, and root mean square error (RMSE) between the ground truth broadcast signals and the recovered EEG signals. Main results. Signal quality was restored following noise cancellation when compared to single electrode EEG measurements collected with no phantom head motion. Significance. We achieved substantial motion artifact attenuation using secondary electrodes for noise cancellation. These methods can be applied to studying electrocortical signals during human locomotion to improve real-world neuroimaging using EEG.

Bring them aboard: Rewarding participation in technology-mediated citizen science projects

Citizen science involves the general public in research activities that are conducted in collaboration with professional scientists. In these projects, citizens voluntarily contribute to the research aims set forward by the scientists through the collection and analysis of large datasets, without a preliminary technical background required. While advancements in information technology have facilitated the involvement of the general public in citizen science through online platforms, several projects still fail due to limited participation. This paper investigates the feasibility of using selected reward mechanisms to positively influence participation and motivations to contribute in a technology-mediated citizen science project. More specifically, we report the results of an empirical study on the effects of monetary and public online acknowledgement rewards. Survey indices and electroencephalographic measurements are synergistically integrated to offer a comprehensive basis for the analysis of citizens' motivations. Our results suggest that both reward mechanisms could crowd-in participants in technology-mediated citizen science projects. With this study, we seek to lay the foundations for a private-collective research model, where the focus is the intensification of participation in technology-mediated citizen science projects.

Altered glutamate clearance in ascorbate deficient mice increases seizure susceptibility and contributes to cognitive impairment in APP/PSEN1 mice.

Ascorbate (vitamin C) is critical as a first line of defense antioxidant within the brain, and specifically within the synapse. Ascorbate is released by astrocytes during glutamate clearance and disruption of this exchange mechanism may be critical in mediating glutamate toxicity within the synapse. This is likely even more critical in neurodegenerative disorders with associated excitotoxicity and seizures, in particular Alzheimer's disease, in which ascorbate levels are often low. Using Gulo-/- mice that are dependent on dietary ascorbate, we established that low brain ascorbate increased sensitivity to kainic acid as measured via behavioral observations, electroencephalography (EEG) measurements, and altered regulation of several glutamatergic system genes. Kainic acid-induced immobility was improved in wild-type mice following treatment with ceftriaxone, which upregulates glutamate transporter GLT-1. The same effect was not observed in ascorbate-deficient mice in which sufficient ascorbate is not available for release. A single, mild seizure event was sufficient to disrupt performance in the water maze in low-ascorbate mice and in APPSWE/PSEN1dE9 mice. Together, the data support the critical role of brain ascorbate in maintaining protection during glutamatergic hyperexcitation events, including seizures. The study further supports a role for mild, subclinical seizures in cognitive decline in Alzheimer's disease.

Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service

In this article, we evaluate the Quality of Service (QoS) through media impairment levels and device operators’ subjective Quality of Experience (QoE). The human-centered QoE determination commonly requires human subject experimentation, which we combine with Electroencephalography (EEG) measurements to move towards automatized and generalized possibilities of determining the QoE. We evaluate the prediction performance for spherical/immersive images displayed with a mobile device VR viewer (Spherical Virtual Reality (SVR)) with the help of only four-position EEG data gathered at the forehead, which correlates well with practical applicability. We find that QoS levels can be predicted more reliably (directly with R2=0.68 or based on profiles with R2=0.9) than the QoE, which exhibits significant error levels. Additional comparison with previous approaches for the Spherical Augmented Reality (SAR) QoE indicates better predictability in AR scenarios over VR.

Brain-Computer Interface with Corrupted EEG Data: a Tensor Completion Approach

One of the current issues in Brain-Computer Interface is how to deal with noisy Electroencephalography measurements organized as multidimensional datasets. On the other hand, recently, significant advances have been made in multidimensional signal completion algorithms that exploit tensor decomposition models to capture the intricate relationship among entries in a multidimensional signal. We propose to use tensor completion applied to EEG data for improving the classification performance in a motor imagery BCI system with corrupted measurements. Noisy measurements are considered as unknowns that are inferred from a tensor decomposition model. We evaluate the performance of four recently proposed tensor completion algorithms plus a simple interpolation strategy, first with random missing entries and then with missing samples constrained to have a specific structure (random missing channels), which is a more realistic assumption in BCI Applications. We measured the ability of these algorithms to reconstruct the tensor from observed data. Then, we tested the classification accuracy of imagined movement in a BCI experiment with missing samples. We show that for random missing entries, all tensor completion algorithms can recover missing samples increasing the classification performance compared to a simple interpolation approach. For the random missing channels case, we show that tensor completion algorithms help to reconstruct missing channels, significantly improving the accuracy in the classification of motor imagery, however, not at the same level as clean data. Tensor completion algorithms are useful in real BCI applications. The proposed strategy could allow using motor imagery BCI systems even when EEG data is highly affected by missing channels and/or samples, avoiding the need of new acquisitions in the calibration stage.

Commentary: Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

Commentary: Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

Towards EEG-based BCI driven by emotions for addressing BCI-Illiteracy: a meta-analytic review

ABSTRACTMany critical aspects affect the correct operation of a Brain Computer Interface. The term ‘BCI-illiteracy’ describes the impossibility of using a BCI paradigm. At present, a universal solution does not exist and seeking innovative protocols to drive a BCI is mandatory. This work presents a meta-analytic review on recent advances in emotions recognition with the perspective of using emotions as voluntary, stimulus-independent, commands for BCIs. 60 papers, based on electroencephalography measurements, were selected to evaluate what emotions have been most recognised and what brain regions were activated by them. It was found that happiness, sadness, anger and calm were the most recognised emotions. Relevant discriminant locations for emotions recognition and for the particular case of discrete emotions recognition were identified in the temporal, frontal and parietal areas. The meta-analysis was mainly performed on stimulus-elicited emotions, due to the limited amount of literature about self-induced emotions. The obtained results represent a good starting point for the development of BCI driven by emotions and allow to: (1) ascertain that emotions are measurable and recognisable one from another (2) select a subset of most recognisable emotions and the corresponding active brain regions.

Sleep in Infants and Children with Prenatal Alcohol Exposure.

Prenatal exposure to alcohol can result in a range of neurobehavioral impairments and physical abnormalities. The term "fetal alcohol spectrum disorders (FASD)" encompasses the outcomes of prenatal alcohol exposure (PAE), the most severe of which is fetal alcohol syndrome. These effects have lifelong consequences, placing a significant burden on affected individuals, caregivers, and communities. Caregivers of affected children often report that their child has sleep problems, and many symptoms of sleep deprivation overlap with the cognitive and behavioral deficits characteristic of FASD. Alcohol-exposed infants and children demonstrate poor sleep quality based on measures of electroencephalography, actigraphy, and questionnaires. These sleep studies indicate a common theme of disrupted sleep pattern, more frequent awakenings, and reduced total sleep time. However, relatively little is known about circadian rhythm disruption and the neurobehavioral correlates of sleep disturbance in individuals with PAE. Furthermore, there is limited information available to healthcare providers about identification and treatment of sleep disorders in patients with FASD. This review consolidates the findings from studies of infant and pediatric sleep in this population, providing an overview of typical sleep characteristics, neurobehavioral correlates of sleep disruption, and potential avenues for intervention in the context of PAE.

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.