Analysis Of Electroencephalographic Data Research Articles

Psychophysiological changes during workspace virtualization

Aim. To estimate psychophysiological changes during workspace virtualization.Materials and Methods. We evaluated the psychophysiological profile of 10 healthy right-handed males aged 25 to 45 years before, during and after the working in a virtual reality (VR) headset. All participants had higher education, normal or corrected to normal vision, and were experienced computer users. Psychometric testing included a neurological examination, assessment of functional and feedback-related brain activity (reaction time, errors, and missed signals) and attention span, quantification of processed symbols in the 1st and 4th minutes of Bourdon test, analysis of short-term memory (10 words, 10 numbers and 10 meaningless syllables memorization) and spatial perception, and multi-channel electroencephalography recording in rest.Results. Deterioration of psychometric indicators after a cognitive load in a VR headset was documented only in the most difficult tasks: the number of errors increased by 93% in the brain performance test and by 65% in the attention distribution test. The analysis of electroencephalography data showed that the delta rhythm and theta1 rhythm activity decreased by 28 and 13%, respectively, after working in a VR headset as compared to baseline values, while alpha1 rhythm activity increased by 96%. Probably, the observed electroencephalography changes corresponded to the patterns of brain activation associated with cognitive load and the resulting fatigue.Conclusions. We developed a suitable approach for the psychometric testing before and after working in VR headset, which demonstrated general tolerance and acceptable subjective difficulties to VR load.

Are our brains more prescriptive than our mouths? Experience with dialectal variation in syntax differentially impacts ERPs and behavior

We investigated online auditory comprehension of dialectal variation in English syntax with event-related potential (ERP) analysis of electroencephalographic data. The syntactic variant under investigation was the double modal, comprising two consecutive auxiliary verbs (e.g., might could). This construction appears across subregional dialects of Southern United States English and expresses indirectness or uncertainty. We compared processing of sentences with attested double modals and single modals in two groups of young adult participants: listeners who were either familiar (Southern) or unfamiliar (Unmarked) with double modal constructions. Both Southern and Unmarked listeners engaged rapid error detection (early anterior negativity) and sentence-level reanalysis (P600) in response to attested double modals, relative to single modals. Offline acceptability and intelligibility judgments reflected dialect familiarity, contrary to the ERP data. We interpret these findings in relation to usage-based and socially weighted theories of language processing, which together capture the effects of frequency and standard language ideology.

Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks.

Neural responses of oddball tasks can be used as a physiological biomarker to evaluate the brain potential of information processing under the assumption that the differential contribution of deviant stimuli can be assessed accurately. Nevertheless, the non-stationarity of neural activity causes the brain networks to fluctuate hugely in time, deteriorating the estimation of pairwise synergies. To deal with the time variability of neural responses, we have developed a piecewise multi-subject analysis that is applied over a set of time intervals within the stationary assumption holds. To segment the whole stimulus-locked epoch into multiple temporal windows, we experimented with two approaches for piecewise segmentation of EEG recordings: a fixed time-window, at which the estimates of FC measures fulfill a given confidence level, and variable time-window, which is segmented at the change points of the time-varying classifier performance. Employing the weighted Phase Lock Index as a functional connectivity metric, we have presented the validation in a real-world EEG data, proving the effectiveness of variable time segmentation for connectivity extraction when combined with a supervised thresholding approach. Consequently, we performed a piecewise group-level analysis of electroencephalographic data that deals with non-stationary functional connectivity measures, evaluating more carefully the contribution of a link node-set in discriminating between the labeled oddball responses.

Predicting acute postoperative pain by the Qnox score at the end of surgery: a prospective observational study

BackgroundThe QNox score (Quantium Medical S.L., distributed by Fresenius Kabi) has recently been introduced as a tool to quantify intraoperative analgesia. Being based on the analysis of electroencephalographic data, QNox is distinctly different to other methods of nociception monitoring that rely almost entirely on the assessment of sympathetic activity. However, there are currently no published data to validate use of QNox in a clinical setting. We investigated the value of pre-arousal QNox data at the end of surgery for prediction of acute postoperative pain in the PACU. MethodsA total of 150 patients scheduled for non-emergency surgery under sevoflurane-opioid general anaesthesia were included in the study. At the end of surgery but before patient arousal, QNox was measured minutely for 5 min. After admission to the recovery room, pain scores (numeric rating scale [NRS], 0–10) were obtained 5 minutely for 15 min. ResultsData from 144 patients were analysed. QNox before arousal showed no correlation (ρ=0.057) with acute postoperative pain in the PACU. Furthermore, the score was found to have no value for the prediction of acute postoperative pain (area under the receiver operating curve, 0.501; 95% confidence interval, 0.406–0.597). ConclusionQNox at the end of surgery before arousal showed no association with and allowed no prediction of acute pain in the PACU. Clinical trial registrationACTRN12618001662257.

Atypical Sound Perception in ASD Explained by Inter-Trial (In)consistency in EEG.

A relative indifference to the human voice is a characteristic of Autism Spectrum Disorder (ASD). Yet, studies of voice perception in ASD provided contradictory results: one study described an absence of preferential response to voices in ASD while another reported a larger activation to vocal sounds than environmental sounds, as seen in typically developed (TD) adults. In children with ASD, an absence of preferential response to vocal sounds was attributed to an atypical response to environmental sounds. To have a better understanding of these contradictions, we re-analyzed the data from sixteen children with ASD and sixteen age-matched TD children to evaluate both inter- and intra-subject variability. Intra-subject variability was estimated with a single-trial analysis of electroencephalographic data, through a measure of inter-trial consistency, which is the proportion of trials showing a positive activity in response to vocal and non-vocal sounds. Results demonstrate a larger inter-subject variability in response to non-vocal sounds, driven by a subset of children with ASD (7/16) who do not show the expected negative Tb peak in response to non-vocal sounds around 200 ms after the start of the stimulation due to a reduced inter-trial consistency. A logistic regression model with age and clinical parameters allowed demonstrating that not a single parameter discriminated the subgroups of ASD participants. Yet, the electrophysiologically-based groups differed on a linear combination of parameters. Children with ASD showing a reduced inter-trial consistency were younger and characterized by lower verbal developmental quotient and less attempt to communicate by voice. This data suggests that a lack of specialization for processing social signal may stem from an atypical processing of environmental sounds, linked to the development of general communication abilities. Discrepancy reported in the literature may arise from that heterogeneity and it may be inadequate to divide children with ASD based only on intellectual quotient or language abilities. This analysis could be a useful tool in providing complementary information for the functional diagnostic of ASD and evaluating verbal communication impairment.

ANALYSIS OF APPLIED REFERENCE LEADS INFLUENCE ON AN EEG SPECTRUM

The article presents the analysis of various kinds of montage applied in the analysis of electroencephalographic data. The present study covers analysis of data obtained as a result of EEG examination of several persons with the age of 23–24 years. Every person has undergone an examination with appliance of 21 electrodes in accordance with classic EEG record method applying the 10–20 system. The examined persons were asked to perform the following activities: sitting steadily with opened eyes, sitting steadily with closed eyes as well as typical cognitive activity i.e. silent reading of given text. Every fragment has been subjected to initial data analysis (filtering, artifact correction) and used for spectral analysis afterwards. The whole analysis has been realized on the basis of four EEG montage examples (two bipolar and two monopolar). For each of them comparative analysis concerning the following points has been carried out: EEG spectra as well as other measures, such as activity maps, diffraction histograms for separate waves and spectrum charts for the selected electrodes.

Autism Spectrum Disorder (ASD) and Fragile X Syndrome (FXS): Two Overlapping Disorders Reviewed through Electroencephalography-What Can be Interpreted from the Available Information?

Autism Spectrum Disorder (ASD) and Fragile X syndrome (FXS) are neurodevelopmental disorders with different but potentially related neurobiological underpinnings, which exhibit significant overlap in their behavioural symptoms. FXS is a neurogenetic disorder of known cause whereas ASD is a complex genetic disorder, with both rare and common genetic risk factors and likely genetic and environmental interaction effects. A comparison of the phenotypic presentation of the two disorders may highlight those symptoms that are more likely to be under direct genetic control, for example in FXS as opposed to shared symptoms that are likely to be under the control of multiple mechanisms. This review is focused on the application and analysis of electroencephalography data (EEG) in ASD and FXS. Specifically, Event Related Potentials (ERP) and resting state studies (rEEG) studies investigating ASD and FXS cohorts are compared. This review explores the electrophysiological similarities and differences between the two disorders in addition to the potentially associated neurobiological mechanisms at play. A series of pertinent research questions which are suggested in the literature are also posed within the review.

Delay differential analysis of electroencephalographic data.

We propose a time-domain approach to detect frequencies, frequency couplings, and phases using nonlinear correlation functions. For frequency analysis, this approach is a multivariate extension of discrete Fourier transform, and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short and sparse time series and can be extended to cross-trial and cross-channel spectra (CTS) for electroencephalography data where multiple short data segments from multiple trials of the same experiment are available. There are two versions of CTS. The first one assumes some phase coherency across the trials, while the second one is independent of phase coherency. We demonstrate that the phase-dependent version is more consistent with event-related spectral perturbation analysis and traditional Morlet wavelet analysis. We show that CTS can be applied to short data windows and yields higher temporal resolution than traditional Morlet wavelet analysis. Furthermore, the CTS can be used to reconstruct the event-related potential using all linear components of the CTS.

Electroencephalographic Data Analysis With Visibility Graph Technique for Quantitative Assessment of Brain Dysfunction

Usual techniques for electroencephalographic (EEG) data analysis lack some of the important properties essential for quantitative assessment of the progress of the dysfunction of the human brain. EEG data are essentially nonlinear and this nonlinear time series has been identified as multi-fractal in nature. We need rigorous techniques for such analysis. In this article, we present the visibility graph as the latest, rigorous technique that can assess the degree of multifractality accurately and reliably. Moreover, it has also been found that this technique can give reliable results with test data of comparatively short length. In this work, the visibility graph algorithm has been used for mapping a time series-EEG signals-to a graph to study complexity and fractality of the time series through investigation of its complexity. The power of scale-freeness of visibility graph has been used as an effective method for measuring fractality in the EEG signal. The scale-freeness of the visibility graph has also been observed after averaging the statistically independent samples of the signal. Scale-freeness of the visibility graph has been calculated for 5 sets of EEG data patterns varying from normal eye closed to epileptic. The change in the values is analyzed further, and it has been observed that it reduces uniformly from normal eye closed to epileptic.

Empirically Validating Fully Automated EOG Artifact Correction using Independent Components Analysis

Empirically Validating Fully Automated EOG Artifact Correction using Independent Components Analysis

Functional connectivity in slow-wave sleep: identification of synchronous cortical activity during wakefulness and sleep using time series analysis of electroencephalographic data

Sleep is a behavioral state ideal for studying functional connectivity because it minimizes many sources of between-subject variability that confound waking analyses. This is particularly important for potential connectivity studies in mental illness where cognitive ability, internal milieu and active psychotic symptoms can vary widely across subjects. We, therefore, sought to adapt techniques applied to magnetoencephalography for use in high-density electroencephalography (EEG), the gold-standard in brain-recording methods during sleep. Autoregressive integrative moving average modeling was used to reduce spurious correlations between recording sites (electrodes) in order to identify functional networks. We hypothesized that identified network characteristics would be similar to those found with magnetoencephalography, and would demonstrate sleep stage-related differences in a control population. We analysed 60-s segments of low-artifact data from seven healthy human subjects during wakefulness and sleep. EEG analysis of eyes-closed wakefulness revealed widespread nearest-neighbor positive synchronous interactions, similar to magnetoencephalography, though less consistent across subjects. Rapid eye movement sleep demonstrated positive synchronous interactions akin to wakefulness but weaker. Slow-wave sleep (SWS), instead, showed strong positive interactions in a large left fronto-temporal-parietal cluster markedly more consistent across subjects. Comparison of connectivity from early SWS to SWS from a later sleep cycle indicated sleep-related reduction in connectivity in this region. The consistency of functional connectivity during SWS within and across subjects suggests this may be a promising technique for comparing functional connectivity between mental illness and health.

High-performance computing system for electroencephalographic data analysis

High-performance computing system for electroencephalographic data analysis

Kernel-based regression of drift and diffusion coefficients of stochastic processes

To improve the estimation of drift and diffusion coefficients of stochastic processes in case of a limited amount of usable data due to e.g. non-stationarity of natural systems we suggest to use kernel-based instead of histogram-based regression. We propose a method for bandwidth selection and compare it to a widely used cross-validation method. Kernel-based regression reveals an enhanced ability to estimate drift and diffusion especially for a small amount of data. This allows one to improve resolvability of changes in complex dynamical systems as evidenced by an exemplary analysis of electroencephalographic data recorded from a human epileptic brain.

Musical obsession or pseudohallucination: Electrophysiological standpoint

Reported herein is a case of obsessive-compulsive disorder with persistent and distressing musical obsessions along with other symptoms. Advanced source analysis of electroencephalographic data indicated high spectral power over the bifrontal region. The musical symptoms were resistant to pharmacotherapy but there was some reduction in frequency and duration of musical obsessions with thought-stopping technique.

Connectivity Inference between Neural Structures via Partial Directed Coherence

This paper describes the rigorous asymptotic distributions of the recently introduced partial directed coherence (PDC) – a frequency domain description of Granger causality between multivariate time series represented by vector autoregressive models. We show that, when not zero, PDC is asymptotically normally distributed and therefore provides means of comparing different strengths of connection between observed time series. Zero PDC indicates an absence of a direct connection between time series, and its otherwise asymptotically normal behavior degenerates into that of a mixture of variables allowing the computation of rigorous thresholds for connectivity tests using either numerical integration or approximate numerical methods. A Monte Carlo study illustrates the power of the test under PDC nullity. An analysis of electroencephalographic data, before and during an epileptic seizure episode, is used to portray the usefulness of the test in a real application.

Dietary boron, brain function, and cognitive performance.

Although the trace element boron has yet to be recognized as an essential nutrient for humans, recent data from animal and human studies suggest that boron may be important for mineral metabolism and membrane function. To investigate further the functional role of boron, brain electrophysiology and cognitive performance were assessed in response to dietary manipulation of boron (approximately 0.25 versus approximately 3.25 mg boron/2000 kcal/day) in three studies with healthy older men and women. Within-subject designs were used to assess functional responses in all studies. Spectral analysis of electroencephalographic data showed effects of dietary boron in two of the three studies. When the low boron intake was compared to the high intake, there was a significant (p < 0.05) increase in the proportion of low-frequency activity, and a decrease in the proportion of higher-frequency activity, an effect often observed in response to general malnutrition and heavy metal toxicity. Performance (e.g., response time) on various cognitive and psychomotor tasks also showed an effect of dietary boron. When contrasted with the high boron intake, low dietary boron resulted in significantly poorer performance (p < 0.05) on tasks emphasizing manual dexterity (studies II and III); eye-hand coordination (study II); attention (all studies); perception (study III); encoding and short-term memory (all studies); and long-term memory (study I). Collectively, the data from these three studies indicate that boron may play a role in human brain function and cognitive performance, and provide additional evidence that boron is an essential nutrient for humans.

Goodness-of-Fit Tests for Correlated Data

Goodness-of-fit tests for stationary processes are a problem of practical importance, e.g. in the analysis of electroencephalographic data. The distribution of the chi-squared statistic under the normal hypothesis is studied by simulation; power is investigated by an inverse filtering procedure for processes which can be well represented by an autoregressive-moving average model. For a second model, consisting of a Gaussian or non-Gaussian signal plus Gaussian noise, sample skewness and kurtosis are suggested as test statistics. The asymptotic normality and the asymptotic variance of these statistics are derived, as well as the behaviour for a broad class of alternatives. The second model is of primary interest in E.E.G.-analysis.

Goodness-of-fit tests for correlated data

SUMMARY Goodness-of-fit tests for stationary processes are a problem of practical importance, e.g. in the analysis of electroencephalographic data. The distribution of the chi-squared statistic under the normal hypothesis is studied by simulation; power is investigated by an inverse filtering procedure for processes which can be well represented by an autoregressive-moving average model. For a second model, consisting of a Gaussian or non-Gaussian signal plus Gaussian noise, sample skewness and kurtosis are suggested as test statistics. The asymptotic normality and the asymptotic variance of these statistics are derived, as well as the behaviour for a broad class of alternatives. The second model is of primary interest in E.E.G..analysis.

On-line power spectra on small computers

A critical view of the possibilities and limitations on power spectral analysis in the environment typical for a biomedical laboratory is presented. A performance measurement tool is shown in light of spectral analysis of electroencephalographic data using the Fast Fourier transform algorithm.

Combined analysis of electroencephalographic data by multiple analyzer and electronic computer (halfwave analysis method)

Combined analysis of electroencephalographic data by multiple analyzer and electronic computer (halfwave analysis method)