Electroencephalographic Measures Research Articles

Future clinical uses of neurophysiological biomarkers to predict and monitor treatment response for schizophrenia.

Advances in psychiatric neuroscience have transformed our understanding of impaired and spared brain functions in psychotic illnesses. Despite substantial progress, few (if any) laboratory tests have graduated to clinics to inform diagnoses, guide treatments, and monitor treatment response. Providers must rely on careful behavioral observation and interview techniques to make inferences about patients' inner experiences and then secondary deductions about impacted neural systems. Development of more effective treatments has also been hindered by a lack of translational quantitative biomarkers that can span the brain-behavior treatment knowledge gap. Here, we describe an example of a simple, low-cost, and translatable electroencephalography (EEG) measure that offers promise for improving our understanding and treatment of psychotic illnesses: mismatch negativity (MMN). MMN is sensitive to and/or predicts response to some pharmacologic and nonpharmacologic interventions and accounts for substantial portions of variance in clinical, cognitive, and psychosocial functioning in schizophrenia (SZ). This measure has recently been validated for use in large-scale multisite clinical studies of SZ. Finally, MMN greatly improves our ability to forecast which individuals at high clinical risk actually develop a psychotic illness. These attributes suggest that MMN can contribute to personalized biomarker-guided treatment strategies aimed at ameliorating or even preventing the onset of psychosis.

SBCI-Headset—Wearable and Modular Device for Hybrid Brain-Computer Interface

Severely disabled people, like completely paralyzed persons either with tetraplegia or similar disabilities who cannot use their arms and hands, are often considered as a user group of Brain Computer Interfaces (BCI). In order to achieve high acceptance of the BCI by this user group and their supporters, the BCI system has to be integrated into their support infrastructure. Critical disadvantages of a BCI are the time consuming preparation of the user for the electroencephalography (EEG) measurements and the low information transfer rate of EEG based BCI. These disadvantages become apparent if a BCI is used to control complex devices. In this paper, a hybrid BCI is described that enables research for a Human Machine Interface (HMI) that is optimally adapted to requirements of the user and the tasks to be carried out. The solution is based on the integration of a Steady-state visual evoked potential (SSVEP)-BCI, an Event-related (de)-synchronization (ERD/ERS)-BCI, an eye tracker, an environmental observation camera, and a new EEG head cap for wearing comfort and easy preparation. The design of the new fast multimodal BCI (called sBCI) system is described and first test results, obtained in experiments with six healthy subjects, are presented. The sBCI concept may also become useful for healthy people in cases where a “hands-free” handling of devices is necessary.

P300 event-related potential as an indicator of inattentional deafness?

An analysis of airplane accidents reveals that pilots sometimes purely fail to react to critical auditory alerts. This inability of an auditory stimulus to reach consciousness has been coined under the term of inattentional deafness. Recent data from literature tends to show that tasks involving high cognitive load consume most of the attentional capacities, leaving little or none remaining for processing any unexpected information. In addition, there is a growing body of evidence for a shared attentional capacity between vision and hearing. In this context, the abundant information in modern cockpits is likely to produce inattentional deafness. We investigated this hypothesis by combining electroencephalographic (EEG) measurements with an ecological aviation task performed under contextual variation of the cognitive load (high or low), including an alarm detection task. Two different audio tones were played: standard tones and deviant tones. Participants were instructed to ignore standard tones and to report deviant tones using a response pad. More than 31% of the deviant tones were not detected in the high load condition. Analysis of the EEG measurements showed a drastic diminution of the auditory P300 amplitude concomitant with this behavioral effect, whereas the N100 component was not affected. We suggest that these behavioral and electrophysiological results provide new insights on explaining the trend of pilots’ failure to react to critical auditory information. Relevant applications concern prevention of alarms omission, mental workload measurements and enhanced warning designs.

Do Micro-Breaks Increase the Attention Level of an Assembly Worker? An ERP Study

This study investigates the influence of micro-breaks on the attention of an assembly worker, by utilizing wireless electroencephalographic (EEG) measurements. The EEG feature of interest for this study was the P300 event-related potential (ERP) component and P3a and P3b sub-components, as these components reflects the ones attention level. Nine students participated the study and completed the simulated manual assembly task, replicated from one of our industrial partner. We used sustained attention to response task (SART), instead of real industrial information that indicates the beginning of the operation. SART paradigm is “go/no-go” task, and in this version of SART, participants are required to respond to all “go” stimuli, in sense of initiating the action of manual assembly operation, and to withhold the action otherwise. The “no-go” condition was considered as micro-break period, since the participants had approximately five seconds to rest during a continuous simulated working routine. Fifty ERPs preceding and following the “no-go” condition were compared. The results revealed that, on the group level, the values of the amplitude of the P3b sub-component, which is related to the higher-level attention processing, were significantly higher for the ERPs following than preceding the micro-break period. This finding indicates that the frequent micro-breaks increase the attention of the manual assembly workers.

Sensitivity Analysis of the Forward Electroencephalographic Problem Depending on Head Shape Variations

A crucial aspect in clinical practice is the knowledge of whether Electroencephalographic (EEG) measurements can be assigned to the functioning of the brain or to geometrical deviations of the human cranium. The present work is focused on continuing to advance understanding on how sensitive the solution of the forward EEG problem is in regard to the geometry of the head. This has been achieved by developing a novel analytic algorithm by performing a perturbation analysis in the linear regime using a homogenous spherical model. Notably, the suggested procedure provides a criterion which recognizes whether surface deformations will have an impact on EEG recordings. The presented deformations represent two major cases: (1) acquired alterations of the surface inflicted by external forces and (2) deformations of the upper part of the human head where EEG signals are recorded. Our results illustrate that neglecting geometric variations present on the heads surface leads to errors in the recorded EEG measurements less than 2%. However, for severe instances of deformations combined with cortical brain activity in the vicinity of the distortion site, the errors rise to almost 25%. Therefore, the accurate description of the head shape plays an important role in understanding the forward EEG problem only in these cases.

Longitudinal EEG changes correlate with cognitive measure deterioration in Parkinson's disease.

QEEG could provide physiological biomarkers for changes over time in Parkinson's disease (PD) cognitive decline if they track with longitudinal neuropsychological performance. Our aim was to correlate longitudinal changes in frequency domain quantitative electroencephalography (QEEG) measures with change in neuropsychological performance testing in PD. 71 PD subjects, not demented at baseline, were studied from the Arizona Study of Aging and Neurodegenerative Disorders cohort. Baseline and follow-up digital EEG from PD subjects were analyzed for QEEG measures of background rhythm frequency and global relative power in delta (2.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands. Baseline and subsequent evaluation included Mini Mental Status Examination and five other neuropsychological tests that load on cognitive domains known to decline in PD. Pearson coefficient was used to assess correlations. Multiple linear regression modeling was used to assess the effect of variable combinations of QEEG and other measures, including age and PD duration. Changes in delta bandpower showed the highest and most consistent pattern of correlations with longitudinal changes in neuropsychological testing. The highest correlation was between delta bandpower increase and decline in the Rey Auditory-Verbal Learning Test (-0.59:p < 0.001). Delta bandpower was also increased in the incident dementia group compared to non-dementia at followup. 1) Longitudinal change in the QEEG frequency domain measure of delta bandpower correlated best with longitudinal neuropsychological performance change in PD; 2) These results constitute preliminary evidence that delta bandpower may be a suitable biomarker for evaluating PD cognitive deterioration longitudinally.

Comparison of the Effect of 2D and 3D Action Observation on Arousal and Mirror Neuron Activity

본 연구는 2D와 3D로 제작된 동작관찰신체훈련을 위한 동영상이 각성과 거울신경세포 활성에 미치는 영향을 알아보기 위하여 시행하였다. 34명의 입체지각이 가능한 일반인들이 연구에 참여하였다. 대상자들은 두 군에 17명씩 무작위로 배정되었고, 각 집단의 대상자들에게 2D와 3D로 동작관찰신체훈련 동영상을 각각 보여주었다. EEG를 이용하여 각성 수준을 알아보기 위해 SMR을 측정하였고, 거울신경세포 활성을 알아보기 위해 뮤리듬을 측정하였다. 연구 결과 3D 군이 2D 군 보다 각성과 거울신경세포 활성을 더 크게 증가시키는 것으로 나타났다. 이러한 결과는 입체지각에 문제가 없는 일반인들이 3D로 동작관찰을 하게 되면 더 많은 각성을 하여, 거울신경세포를 더 활성화시킬 수 있다는 것을 의미한다. 따라서 동작관찰신체훈련에 3D가 더 효과적일 수 있음을 제시해 주는 것이다.

Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals

Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain–computer interfaces (BCI) systems, in nearly all source localization methods for spatial whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data.We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for spatial whitening and source localization of MEG signals.

From ergolines to indoles: improved inhibitors of the human H3 receptor for the treatment of narcolepsy.

Ergolines were recently identified as a novel class of H3 receptor (H3R) inverse agonists. Although their optimization led to drug candidates with encouraging properties for the treatment of narcolepsy, brain penetration remained low. To overcome this issue, ergoline 1 ((6aR,9R,10aR)-4-(2-(dimethylamino)ethyl)-N-phenyl-9-(pyrrolidine-1-carbonyl)-6,6a,8,9,10,10a-hexahydroindolo[4,3-fg]quinoline-7(4H)-carboxamide)) was transformed into a series of indole derivatives with high H3R affinity. These new molecules were profiled by simultaneous determination of their brain receptor occupancy (RO) levels and pharmacodynamic (PD) effects in mice. These efforts culminated in the discovery of 15 m ((R)-1-isopropyl-5-(1-(2-(2-methylpyrrolidin-1-yl)ethyl)-1H-indol-4-yl)pyridin-2(1H)-one), which has an ideal profile showing a strong correlation of PD effects with RO, and no measurable safety liabilities. Its desirably short duration of action was confirmed by electroencephalography (EEG) measurements in rats.

The brain's response to pleasant touch: an EEG investigation of tactile caressing.

Somatosensation as a proximal sense can have a strong impact on our attitude toward physical objects and other human beings. However, relatively little is known about how hedonic valence of touch is processed at the cortical level. Here we investigated the electrophysiological correlates of affective tactile sensation during caressing of the right forearm with pleasant and unpleasant textile fabrics. We show dissociation between more physically driven differential brain responses to the different fabrics in early somatosensory cortex – the well-known mu-suppression (10–20 Hz) – and a beta-band response (25–30 Hz) in presumably higher-order somatosensory areas in the right hemisphere that correlated well with the subjective valence of tactile caressing. Importantly, when using single trial classification techniques, beta-power significantly distinguished between pleasant and unpleasant stimulation on a single trial basis with high accuracy. Our results therefore suggest a dissociation of the sensory and affective aspects of touch in the somatosensory system and may provide features that may be used for single trial decoding of affective mental states from simple electroencephalographic measurements.

Preservation of electroencephalographic organization in patients with impaired consciousness and imaging-based evidence of command-following.

Standard clinical characterization of patients with disorders of consciousness (DOC) relies on observation of motor output and may therefore lead to the misdiagnosis of vegetative state or minimally conscious state in patients with preserved cognition. We used conventional electroencephalographic (EEG) measures to assess a cohort of DOC patients with and without functional magnetic resonance imaging (fMRI)-based evidence of command-following, and correlated the findings with standard clinical behavioral evaluation and brain metabolic activity. We enrolled 44 patients with severe brain injury. Behavioral diagnosis was established using standardized clinical assessments. Long-term EEG recordings were analyzed to determine wakeful background organization and presence of elements of sleep architecture. A subset of patients had fMRI testing of command-following using motor imagery paradigms (26 patients) and resting brain metabolism measurement using (18) fluorodeoxyglucose positron emission tomography (31 patients). All 4 patients with fMRI evidence of covert command-following consistently demonstrated well-organized EEG background during wakefulness, spindling activity during sleep, and relative preservation of cortical metabolic activity. In the entire cohort, EEG organization and overall brain metabolism showed no significant association with bedside behavioral testing, except in a few cases when EEG was severely abnormal. These findings suggest that conventional EEG is a simple strategy that complements behavioral and imaging characterization of DOC patients. Preservation of specific EEG features may be used to assess the likelihood of unrecognized cognitive abilities in severely brain-injured patients with very limited or no motor responses.

Amplitude-integrated electroencephalographic activity and middle cerebral artery Doppler flow measurements in preterm small for gestational age infants.

Amplitude-integrated encephalography (EEG) is frequently used in neonatal intensive care units to monitor brain functions. Its bedside application and easy interpretation are the most important features. Brain development of small for gestational age infants can be affected by intrauterine chronic hypoxia. The current study aimed to evaluate cerebral functions of small for gestational age infants by means of amplitude-integrated EEG. Thirty- to 34-week-old 22 small for gestational age and 27 appropriate for gestational age preterm infants were included in the study. The mode of delivery, gender, birth weight, and Apgar scores of the patients were recorded. Following middle cerebral artery mean velocity measurement with cranial Doppler at the 24th hour of birth, an amplitude-integrated EEG recording was performed on all infants, for a period of 4 to 24 hours. Small for gestational age infants had significantly higher middle cerebral artery mean velocity than appropriate for gestational age infants (21.09 ± 4.25 vs 17.8 ± 4.07; P = .029). The amplitude-integrated EEG recordings showed lower "lower border of quiet sleep" and total Burdjalov score in small for gestational age infants when compared with appropriate for gestational age infants (2.5 [1-3.25] µV vs 3 [2.75-4] µV; P = .04, 8 [6-10], 9 [9-11]; P = .04, respectively). Increased middle cerebral artery blood flow observed in small for gestational age infants might be a marker of chronic intrauterine hypoxia to which these infants were exposed. These infants demonstrated a more immature pattern of amplitude-integrated EEG.

Influence of Wilbraham-Gibbs Phenomenon on Digital Stochastic Measurement of EEG Signal Over an Interval

Abstract Measurement methods, based on the approach named Digital Stochastic Measurement, have been introduced, and several prototype and small-series commercial instruments have been developed based on these methods. These methods have been mostly investigated for various types of stationary signals, but also for non-stationary signals. This paper presents, analyzes and discusses digital stochastic measurement of electroencephalography (EEG) signal in the time domain, emphasizing the problem of influence of the Wilbraham-Gibbs phenomenon. The increase of measurement error, related to the Wilbraham-Gibbs phenomenon, is found. If the EEG signal is measured and measurement interval is 20 ms wide, the average maximal error relative to the range of input signal is 16.84 %. If the measurement interval is extended to 2s, the average maximal error relative to the range of input signal is significantly lowered - down to 1.37 %. Absolute errors are compared with the error limit recommended by Organisation Internationale de Métrologie Légale (OIML) and with the quantization steps of the advanced EEG instruments with 24-bit A/D conversion

Effects of amplitude cueing on postural responses and preparatory cortical activity of people with Parkinson disease.

Persons with Parkinson disease (PD) are unable to modify their postural responses, and show an associated increase in cortical preparatory activity for anticipated postural perturbations. In this study we asked whether participants with PD could modify their postural responses and cortical preparatory activity when cued to focus on increasing movement amplitude before a series of predictable postural perturbations. Twelve participants with PD performed postural responses to 30 identical backward surface translations. We cued participants to focus on increasing movement amplitude, and examined the effects of cueing by measuring postural responses (center-of-pressure initial rate of change, automatic postural response stability, peak trunk flexion, peak ankle extension) and preparatory cortical activity (electroencephalographic measures of contingent negative variation, alpha and beta event-related desynchronization). Participants with PD modified their postural responses during the amplitude trials by increasing trunk flexion, slowing center-of-pressure initial rate of change, and decreasing automatic postural response stability. However, no significant differences in contingent negative variation amplitude or alpha or beta event-related desynchronization were observed with versus without amplitude cueing. Persons with PD were able to modify their feet-in-place postural responses with amplitude cueing. These changes were not associated with changes in cortical preparation during amplitude cue trials, suggesting that other regions or measures of brain function were responsible for changes in postural responses. Future studies are needed to determine the effects of long-term amplitude-cueing practice on cortical preparation and postural stability.Video Abstract available. See Video (Supplemental Digital Content 1, http://links.lww.com/JNPT/A78) for more insights from the authors.

Towards a gaze-independent hybrid-BCI based on SSVEPs, alpha-band modulations and the P300.

In recent years it has been shown to be possible to create a Brain Computer Interface (BCI) using non-invasive electroencephalographic (EEG) measurements of covert visual spatial attention. For example, that both Steady-State Visual Evoked Potentials (SSVEP) and parieto-occipital alpha band activity have been shown to be sensitive to covert attention and this has been exploited to provide simple communication control without the need for any physical movement. In this study, potential improvements in the speed and accuracy of such a BCI are investigated by exploring the possibility of incorporating a P300 task into an SSVEP covert attention paradigm. Should this be possible it would pave the way for a gaze-independent hybrid BCI based on three somewhat independent EEG signals. Within a well-established SSVEP-based attention paradigm we show that it is possible to make a binary classification of covert attention using just the P300 with an average accuracy of 71% across three subjects. We also validate previously published research by showing robust attention effects on the SSVEP and alpha band activity within this paradigm. In future work, it is hoped that by integrating the three signals into a hybrid BCI a significant improvement in performance will be forthcoming leading to an easily usable real time communication device for patients with severe disabilities such as Locked-In Syndrome (LIS).

Postoperative delirium: disconnecting the network?

Postoperative delirium: disconnecting the network?

Tracing the evolution of multi-scale functional networks in a mouse model of depression using persistent brain network homology

Many brain diseases or disorders, such as depression, are known to be associated with abnormal functional connectivity in neural networks in the brain. Some bivariate measures of electroencephalography (EEG) for coupling analysis have been used widely in attempts to explain abnormalities related with depression. However, brain network evolution based on persistent functional connections in EEG signals could not be easily unveiled. For a geometrical exploration of brain network evolution, here, we used persistent brain network homology analysis with EEG signals from a corticosterone (CORT)-induced mouse model of depression. EEG signals were obtained from eight cortical regions (frontal, somatosensory, parietal, and visual cortices in each hemisphere). The persistent homology revealed a significantly different functional connectivity between the control and CORT model, but no differences in common coupling measures, such as cross correlation and coherence, were apparent. The CORT model showed a more localized connectivity and decreased global connectivity than the control. In particular, the somatosensory and parietal cortices were loosely connected in the CORT model. Additionally, the CORT model displayed altered connections among the cortical regions, especially between the frontal and somatosensory cortices, versus the control. This study demonstrates that persistent homology is useful for brain network analysis, and our results indicate that the CORT-induced depression mouse model shows more localized and decreased global connectivity with altered connections, which may facilitate characterization of the abnormal brain network underlying depression.

Beat-induced fluctuations in auditory cortical beta-band activity: using EEG to measure age-related changes.

People readily extract regularity in rhythmic auditory patterns, enabling prediction of the onset of the next beat. Recent magnetoencephalography (MEG) research suggests that such prediction is reflected by the entrainment of oscillatory networks in the brain to the tempo of the sequence. In particular, induced beta-band oscillatory activity from auditory cortex decreases after each beat onset and rebounds prior to the onset of the next beat across tempi in a predictive manner. The objective of the present study was to examine the development of such oscillatory activity by comparing electroencephalography (EEG) measures of beta-band fluctuations in 7-year-old children to adults. EEG was recorded while participants listened passively to isochronous tone sequences at three tempi (390, 585, and 780 ms for onset-to-onset interval). In adults, induced power in the high beta-band (20–25 Hz) decreased after each tone onset and rebounded prior to the onset of the next tone across tempo conditions, consistent with MEG findings. In children, a similar pattern was measured in the two slower tempo conditions, but was weaker in the fastest condition. The results indicate that the beta-band timing network works similarly in children, although there are age-related changes in consistency and the tempo range over which it operates.

A regularized matrix factorization approach to induce structured sparse-low-rank solutions in the EEG inverse problem

We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy electroencephalographic (EEG) measurements, commonly named as the EEG inverse problem. We propose a new method to induce neurophysiological meaningful solutions, which takes into account the smoothness, structured sparsity, and low rank of the BES matrix. The method is based on the factorization of the BES matrix as a product of a sparse coding matrix and a dense latent source matrix. The structured sparse-low-rank structure is enforced by minimizing a regularized functional that includes the ℓ21-norm of the coding matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We analyze the convergence of the optimization procedure, and we compare, under different synthetic scenarios, the performance of our method with respect to the Group Lasso and Trace Norm regularizers when they are applied directly to the target matrix.

Assessment of aging by cortical functional connectivity estimated through synchronization electroencephalographic measurements

This work was aimed to study the physiological cerebral aging from the analysis of functional electroencephalographic (EEG) connectivity between different cortical areas in two groups of healthy subjects between 50-65 years and between 66-80 years. With this purpose, digital monopolar EEG recordings were carried out at rest and closed eyes, taking as sampling rate, 256 Hz and as reference, the average of the 16 channels recorded (6-frontals, 4-temporals, 2-centrals, 2-parietal, 2-occipital). The functional cortical connectivity was estimated from the interdependence between channels using the coherence function in the frequency bands delta/theta/alpha/beta, and an index (L) of nonlinear generalized synchronization. The pairwise connectivity between all possible channels and a global connectivity between each channel and all others was computed. The results show that certain local interdependencies measured by the index L and by the delta band coherence, decreased with age; these were especially significant for the temporal-occipital-central connections. Moreover, a significant linear decrease with age of the interdependence indices aforementioned for the pair and local averages was observed. Also global interdependencies, especially those of nonlinear type, for certain central-lateral and posterior areas, decline with age. It is concludes that individual and global measures of EEG functional connectivity between certain brain areas can help to objectively evaluate the neurophysiological aging.