Automatic classification of artifactual ICA-components for artifact removal in EEG signals

EEG power spectral slope differs by ADHD status and stimulant medication exposure in early childhood

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by hyperactivity/impulsivity and inattentiveness. Efforts toward the development of a biologically based diagnostic test have identified differences in the EEG power spectrum; most consistently reported is an increased ratio of theta to beta power during resting state in those with the disorder, compared with controls. Current approaches calculate theta/beta ratio using fixed frequency bands, but the observed differences may be confounded by other relevant features of the power spectrum, including shifts in peak oscillation frequency and altered slope or offset of the aperiodic 1/f-like component of the power spectrum. In the present study, we quantify the spectral slope and offset, peak alpha frequency, and band-limited and band-ratio oscillatory power in the resting-state EEG of 3- to 7-yr-old children with and without ADHD. We found that medication-naive children with ADHD had higher alpha power, greater offsets, and steeper slopes compared with typically developing children. Children with ADHD who were treated with stimulants had comparable slopes and offsets to the typically developing group despite a 24-h medication-washout period. We further show that spectral slope correlates with traditional measures of theta/beta ratio, suggesting the utility of slope as a neural marker over and above traditional approaches. Taken with past research demonstrating that spectral slope is associated with executive functioning and excitatory/inhibitory balance, these results suggest that altered slope of the power spectrum may reflect pathology in ADHD.NEW & NOTEWORTHY This article highlights the clinical utility of comprehensively quantifying features of the EEG power spectrum. Using this approach, we identify, for the first time, differences in the aperiodic components of the EEG power spectrum in children with attention-deficit/hyperactivity disorder (ADHD) and provide evidence that spectral slope is a robust indictor of an increase in low- relative to high-frequency power in ADHD.

Is learning scale-free? Chemistry learning increases EEG fractal power and changes the power law exponent

Learning in chemistry and other areas of science involves developing one's mental models of invisible processes and manipulating temporal and spatial domains during visual information processing. While some aspects learning have been well studied by EEG (e.g., theta and gamma oscillations), the role of spontaneous and scale-free brain activity remains unclear. We used a continuous chemistry learning EEG paradigm to explore how scale-free brain activity is related learning. We found a learning effect in participants (N = 22) with an increase in test accuracy (learning gain) and decrease in test question response times in a counterbalanced pre/post-test experiment. In the brain we found increased overall (mixed) broadband power (1-50 Hz) during learning compared to rest. We then used the IRASA method to separate oscillatory and fractal (i.e. scale-free) spectral components and observed an increase in low-frequency oscillatory band powers during learning. More importantly, we found that fractal power increased during the learning sessions relative to oscillatory power. Finally, the structure of the fractal power spectra (PLE) correlated to the individual participants' learning gains. These findings support the importance of scale-free activity for learning from a complex visual paradigm. We tentatively hypothesize that this fractal component is involved in integrating the different time scales of the learning material with those of the spontaneous activity during learning and mental model shaping.

Risk proneness modulates the impact of impulsivity on brain functional connectivity

Impulsivity and sensation seeking are considered to be among the most important personality traits involved in risk-taking behavior. This study is focused on whether the association of these personality traits and brain functional connectivity depends on individuals' risk proneness. Risk proneness was assessed with the DOSPERT-30 scale and corroborated with performance in a motorcycle simulator. The associations of impulsivity- and sensation seeking-related traits with the between and within coupling of seven major brain functional networks, estimated from electroencefalograma (EEG) recordings, differ according to whether an individual is risk prone or not. In risk-prone individuals, (lack of) premeditation enhanced the coupling of the ventral attention and limbic networks. At the same time, emotion seeking increased the coupling of the frontoparietal network and the default mode networks (DMNs). Finally, (lack of) perseverance had a positive impact on the coupling of anterior temporal nodes of the limbic network whilst having a negative impact on some frontal nodes of the frontoparietal network and the DMNs. In general, the results suggest that the predisposition to behave riskily modulates the way in which impulsivity traits are linked to brain functionality, seemingly making the brain networks prepare for an immediate, automatic, and maladaptive response.

Optimizing the ICA-based removal of ocular EEG artifacts from free viewing experiments

Combining EEG with eye-tracking is a promising approach to study neural correlates of natural vision, but the resulting recordings are also heavily contaminated by activity of the eye balls, eye lids, and extraocular muscles. While Independent Component Analysis (ICA) is commonly used to suppress these ocular artifacts, its performance under free viewing conditions has not been systematically evaluated and many published reports contain residual artifacts. Here I evaluated and optimized ICA-based correction for two tasks with unconstrained eye movements: visual search in images and sentence reading. In a first step, four parameters of the ICA pipeline were varied orthogonally: the (1) high-pass and (2) low-pass filter applied to the training data, (3) the proportion of training data containing myogenic saccadic spike potentials (SP), and (4) the threshold for eye tracker-based component rejection. In a second step, the eye-tracker was used to objectively quantify the correction quality of each ICA solution, both in terms of undercorrection (residual artifacts) and overcorrection (removal of neurogenic activity). As a benchmark, results were compared to those obtained with an alternative spatial filter, Multiple Source Eye Correction (MSEC). With commonly used settings, Infomax ICA not only left artifacts in the data, but also distorted neurogenic activity during eye movement-free intervals. However, correction results could be strongly improved by training the ICA on optimally filtered data in which SPs were massively overweighted. With optimized procedures, ICA removed virtually all artifacts, including the SP and its associated spectral broadband artifact from both viewing paradigms, with little distortion of neural activity. It also outperformed MSEC in terms of SP correction. Matlab code is provided.

Role of the cortex in visuomotor control of arm stability

Whereas numerous motor control theories describe the control of arm trajectory during reach, the control of stabilization in a constant arm position (i.e., visuomotor control of arm posture) is less clear. Three potential mechanisms have been proposed for visuomotor control of arm posture: 1) increased impedance of the arm through co-contraction of antagonistic muscles, 2) corrective muscle activity via spinal/supraspinal reflex circuits, and/or 3) intermittent voluntary corrections to errors in position. We examined the cortical mechanisms of visuomotor control of arm posture and tested the hypothesis that cortical error networks contribute to arm stabilization. We collected electroencephalography (EEG) data from 10 young healthy participants across four experimental planar movement tasks. We examined brain activity associated with intermittent voluntary corrections of position error and antagonist co-contraction during stabilization. EEG beta-band (13-26 Hz) power fluctuations were used as indicators of brain activity, and coherence between EEG electrodes was used as a measure of functional connectivity between brain regions. Cortical activity in the sensory, motor, and visual areas during arm stabilization was similar to activity during volitional arm movements and was larger than activity during co-contraction of the arm. However, cortical connectivity between the sensorimotor and visual regions was higher during arm stabilization compared with volitional arm movements and co-contraction of the arm. The difference in cortical activity and connectivity between tasks might be attributed to an underlying visuomotor error network used to update motor commands for visuomotor control of arm posture.NEW & NOTEWORTHY We examined cortical activity and connectivity during control of stabilization in a constant arm position (i.e., visuomotor control of arm posture). Our findings provide evidence for cortical involvement during control of stabilization in a constant arm position. A visuomotor error network appears to be active and may update motor commands for visuomotor control of arm posture.

Atypical Temporal Dynamics of Resting State Shapes Stimulus-Evoked Activity in Depression-An EEG Study on Rest-Stimulus Interaction

Major depressive disorder (MDD) is a complex psychiatric disorder characterized by changes in both resting state and stimulus-evoked activity. Whether resting state changes are carried over to stimulus-evoked activity, however, is unclear. We conducted a combined rest (3 min) and task (three-stimulus auditory oddball paradigm) EEG study in n=28 acute depressed MDD patients, comparing them with n=25 healthy participants. Our focus was on the temporal dynamics of both resting state and stimulus-evoked activity for which reason we measured peak frequency (PF), coefficient of variation (CV), Lempel-Ziv complexity (LZC), and trial-to-trial variability (TTV). Our main findings are: i) atypical temporal dynamics in resting state, specifically in the alpha and theta bands as measured by peak frequency (PF), coefficient of variation (CV) and power; ii) decreased reactivity to external deviant stimuli as measured by decreased changes in stimulus-evoked variance and complexity-TTV, LZC, and power and frequency sliding (FS and PS); iii) correlation of stimulus related measures (TTV, LZC, PS, and FS) with resting state measures. Together, our findings show that resting state dynamics alone are atypical in MDD and, even more important, strongly shapes the dynamics of subsequent stimulus-evoked activity. We thus conclude that MDD can be characterized by an atypical temporal dynamic of its rest-stimulus interaction; that, in turn, makes it difficult for depressed patients to react to relevant stimuli such as the deviant tone in our paradigm.

The value of a real face: Differences between affective faces and emojis in neural processing and their social influence on decision-making

Emotional feedback is a crucial part of social interaction, since it may indicate motivations, intentions, and thus, the future behavior of interaction partners. Nowadays, social interaction has been enriched by artificial emotional feedback provided by emojis, which are the means of transporting emotions in mobile messengers. In this study, we examined the influence of emotional feedback by emojis compared to real faces on decision-making and neural processing. We modified the ultimatum game by including proposers represented both by emojis and human faces who reacted specifically toward acceptance or rejection of an offer. We show that proposers who reward acceptance with a smile cause the highest acceptance rates. Interestingly, acceptance rates did not differ between proposers represented by humans compared to emojis. Regarding electrophysiology, emojis evoked more negative N170 and N2 brain potentials compared to human faces both during a mere presentation and as feedback stimuli. Proposers that showed emotional facial expressions evoked larger N170 amplitudes as compared to neutral expressions. Especially the proposers represented by emojis evoked larger P3 amplitudes as feedback stimuli compared to human facial expressions. The comparison of emoji proposers with real-face proposers provides new insight into how relevant social cues influence behavior and its neural underpinnings.

Exploring the effects of visual perspective on the ERP components of empathy for pain

Previous neurophysiological research suggests that there are event-related potential (ERP) components associated with empathy for pain: an early affective component (N2) and two late cognitive components (P3/LPP). The current study investigated whether and how the visual perspective from which a painful event is observed affects these ERP components. Participants viewed images of hands in pain vs. not in pain from a first-person or third-person perspective. We found that visual perspective influences both the early and late components. In the early component (N2), there was a larger mean amplitude during observation of pain vs no-pain exclusively when images were shown from a first-person perspective. We suggest that this effect may be driven by misattributing the on-screen hand to oneself. For the late component (P3), we found a larger effect of pain on mean amplitudes in response to third-person relative to first-person images. We speculate that the P3 may reflect a later process that enables effective recognition of others' pain in the absence of misattribution. We discuss our results in relation to self- vs other-related processing by questioning whether these ERP components are truly indexing empathy (an other-directed process) or a simple misattribution of another's pain as one's own (a self-directed process).

Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity

Functional connectivity of the brain fluctuates even in resting-state condition. It has been reported recently that fluctuations of global functional network topology and those of individual connections between brain regions expressed multifractal scaling. To expand on these findings, in this study we investigated if multifractality was indeed an inherent property of dynamic functional connectivity (DFC) on the regional level as well. Furthermore, we explored if local DFC showed region-specific differences in its multifractal and entropy-related features. DFC analyses were performed on 62-channel, resting-state electroencephalography recordings of twelve young, healthy subjects. Surrogate data testing verified the true multifractal nature of regional DFC that could be attributed to the presumed nonlinear nature of the underlying processes. Moreover, we found a characteristic spatial distribution of local connectivity dynamics, in that frontal and occipital regions showed stronger long-range correlation and higher degree of multifractality, whereas the highest values of entropy were found over the central and temporal regions. The revealed topology reflected well the underlying resting-state network organization of the brain. The presented results and the proposed analysis framework could improve our understanding on how resting-state brain activity is spatio-temporally organized and may provide potential biomarkers for future clinical research.

Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal

BACKGROUND

Prediction of therapeutic outcome of repetitive transcranial magnetic stimulation (rTMS) treatment is an important purpose that eliminates financial and psychological consequences of applying inefficient therapy. To achieve this goal we proposed a method based on machine learning to classify responders (R) and non- responders (NR) to rTMS treatment for major depression disorder (MDD) patients.

METHODS

19 electrodes resting state EEG was recorded from 46 MDD patients before treatment. Then patients underwent 7 weeks of rTMS, and 23 of them responded to treatment. Features extracted from EEG include Lempel-Ziv complexity (LZC), Katz fractal dimension (KFD), correlation dimension (CD), the power spectral density, features based on bispectrum, frontal and prefrontal cordance and combination of them. The most relevant features were selected by the minimal-redundancy-maximal-relevance (mRMR) feature selection algorithm. For classifying two groups of R and NR, k-nearest neighbors (KNN) were applied. The performance of the proposed method was evaluated by leave-1-out cross-validation. For further study, the capability of features in differentiating R and NR was investigated by a statistical test.

RESULTS

Effective EEG features for prediction of rTMS treatment response were found. EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands and CD were the most discriminative features. Power of beta classified R and NR with the high performance of 91.3% accuracy, 91.3% specificity, and 91.3% sensitivity.

LIMITATIONS

Lack of large sample size restricted our method for using in clinical applications.

CONCLUSION

This considerable high accuracy indicates that our proposed method with power and some of the nonlinear and bispectral features can lead to promising results in predicting treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording.

Effects of map design characteristics on users’ search performance and cognitive load

Purpose

This paper aims to investigate the influence of map design characteristics on users’ cognitive load and search performance. Two design conditions (symbolic vs non-symbolic) were used to evaluate users’ ability to locate a place of interest.

Design/methodology/approach

A total of 19 students (10 male and 9 female, 20-23 years old) participated in this study. The time required for subjects to find a place in the two conditions was used to estimate their searching performance. An electroencephalogram (EEG) device was used to examine students’ cognitive load using event-related desynchronization percentages of alpha, beta and theta brain wave rhythms.

Findings

The results showed that subjects needed more time to find a place in the non-symbolic condition than the symbolic condition. The EEG data, however, revealed that users experienced higher cognitive load when searching for a place in the symbolic condition. The authors found that the design characteristics of the map significantly influenced users’ brain activity, thus impacting their search performance.

Originality/value

Outcomes from this study can be used by cartographic designers and scholars to understand how certain design characteristics can trigger cognitive activity to improve users' searching experience and efficiency.

Post-hoc Labeling of Arbitrary M/EEG Recordings for Data-Efficient Evaluation of Neural Decoding Methods

Many cognitive, sensory and motor processes have correlates in oscillatory neural source activity, which is embedded as a subspace in the recorded brain signals. Decoding such processes from noisy magnetoencephalogram/electroencephalogram (M/EEG) signals usually requires data-driven analysis methods. The objective evaluation of such decoding algorithms on experimental raw signals, however, is a challenge: the amount of available M/EEG data typically is limited, labels can be unreliable, and raw signals often are contaminated with artifacts. To overcome some of these problems, simulation frameworks have been introduced which support the development of data-driven decoding algorithms and their benchmarking. For generating artificial brain signals, however, most of the existing frameworks make strong and partially unrealistic assumptions about brain activity. This limits the generalization of results observed in the simulation to real-world scenarios. In the present contribution, we show how to overcome several shortcomings of existing simulation frameworks. We propose a versatile alternative, which allows for an objective evaluation and benchmarking of novel decoding algorithms using real neural signals. It allows to generate comparatively large datasets with labels being deterministically recoverable from the arbitrary M/EEG recordings. A novel idea to generate these labels is central to this framework: we determine a subspace of the true M/EEG recordings and utilize it to derive novel labels. These labels contain realistic information about the oscillatory activity of some underlying neural sources. For two categories of subspace-defining methods, we showcase how such labels can be obtained-either by an exclusively data-driven approach (independent component analysis-ICA), or by a method exploiting additional anatomical constraints (minimum norm estimates-MNE). We term our framework post-hoc labeling of M/EEG recordings. To support the adoption of the framework by practitioners, we have exemplified its use by benchmarking three standard decoding methods-i.e., common spatial patterns (CSP), source power-comodulation (SPoC), and convolutional neural networks (ConvNets)-wrt. Varied dataset sizes, label noise, and label variability. Source code and data are made available to the reader for facilitating the application of our post-hoc labeling framework.

Reduced frontal gamma power at 24 months is associated with better expressive language in toddlers at risk for autism

Frontal gamma power has been associated with early language development in typically developing toddlers, and gamma band abnormalities have been observed in individuals with autism spectrum disorder (ASD), as well as high-risk infant siblings (those having an older sibling with ASD), as early as 6 months of age. The current study investigated differences in baseline frontal gamma power and its association with language development in toddlers at high versus low familial risk for autism. Electroencephalography recordings as well as cognitive and behavioral assessments were acquired at 24 months as part of prospective, longitudinal study of infant siblings of children with and without autism. Diagnosis of autism was determined at 24-36 months, and data were analyzed across three outcome groups-low-risk without ASD (n = 43), high-risk without ASD (n = 42), and high-risk with ASD (n = 16). High-risk toddlers without ASD had reduced baseline frontal gamma power (30-50 Hz) compared to low-risk toddlers. Among high-risk toddlers increased frontal gamma was only marginally associated with ASD diagnosis (P = 0.06), but significantly associated with reduced expressive language ability (P = 0.007). No association between gamma power and language was present in the low-risk group. These findings suggest that differences in gamma oscillations in high-risk toddlers may represent compensatory mechanisms associated with improved developmental outcomes. Autism Res 2019, 12: 1211-1224. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: This study looked at differences in neural activity in the gamma range and its association with language in toddlers with and without increased risk for ASD. At 2 years of age, gamma power was lower in high-risk toddlers without ASD compared to a low-risk comparison group. Among high-risk toddlers both with and without later ASD, reduced gamma power was also associated with better language outcomes, suggesting that gamma power may be a marker of language development in high-risk children.

A neural perspective on when and why trait greed comes at the expense of others

Depending on the point of view, conceptions of greed range from being a desirable and inevitable feature of a well-regulated, well-balanced economy to the root of all evil - radix omnium malorum avaritia (Tim 6.10). Regarding the latter, it has been proposed that greedy individuals strive for obtaining desired goods at all costs. Here, we show that trait greed predicts selfish economic decisions that come at the expense of others in a resource dilemma. This effect was amplified when individuals strived for obtaining real money, as compared to points, and when their revenue was at the expense of another person, as compared to a computer. On the neural level, we show that individuals high, compared to low in trait greed showed a characteristic signature in the EEG, a reduced P3 effect to positive, compared to negative feedback, indicating that they may have a lack of sensitivity to adjust behavior according to positive and negative stimuli from the environment. Brain-behavior relations further confirmed this lack of sensitivity to behavior adjustment as a potential underlying neuro-cognitive mechanism which explains selfish and reckless behavior that may come at the expense of others.

An Electrophysiological Study of Cognitive and Emotion Processing in Type I Chiari Malformation

Type I Chiari malformation (CMI) is a neurological condition in which the cerebellar tonsils descend into the cervical spinal subarachnoid space resulting in cervico-medullary compression. Early case-control investigations have indicated cognitive deficits in the areas of attention, memory, processing speed, and visuospatial function. The present study further examined cognitive and emotional processing deficits associated with CMI using a dual-task paradigm. Nineteen CMI patients were recruited during pre-surgical consultation and 19 matched control participants identified emotional expressions in separate single and asynchronous dual-task designs. To extend earlier behavioral studies of cognitive effects in CMI, we recorded event-related potentials (ERPs) in the dual-task design. Though response times were slower for CMI patients across the two tasks, behavioral and ERP analyses indicated that patients did not differ from matched controls in the ability to allocate attentional resources between the two tasks. P1 ERP component analyses provided no indication of an emotional arousal deficit in our CMI sample while P3 ERP component analyses suggested a CMI-related deficit in emotional regulation. P3 analysis also yielded evidence for a frontalization of neurophysiological activity in CMI patients. Pain and related depression and anxiety factors accounted for CMI deficits in single-task, but not dual-task, response times. Results are consistent with a dysfunctional fronto-parietal attentional network resulting from either the indirect effects of chronic pain or the direct effects of CMI pathophysiology stemming from cervico-medullary compression.

Long-term impact of prenatal exposure to chemotherapy on executive functioning: An ERP study

OBJECTIVE

This study examines the long-term impact of prenatal exposure to chemotherapy on executive functioning and the contribution of late-prematurity to this effect, using event-related potentials.

METHODS

Mothers of the prenatal-exposed children (n = 20) were diagnosed with cancer and received chemotherapeutic treatment during pregnancy. We recruited healthy controls (n = 20) who were matched on a 1:1 ratio regarding prematurity, age and sex. We assessed executive functioning at the age of nine, using two event-related potential paradigms: a Go/Nogo paradigm to investigate processes of response inhibition and conflict monitoring, as well as a Posner paradigm to investigate spatial attention.

RESULTS

Lower potentials were found in prenatal-exposed children compared to controls in the Go/Nogo P3 and Posner positive slow wave. Moreover, prenatal-exposed children responded slower on the Posner paradigm compared to controls (p < .033), with more incorrect responses (p = .023). In the control group, the N2 Go/Nogo wave was more pronounced in children born after a longer gestation.

CONCLUSIONS

This is the first study that demonstrates an effect of prenatal exposure to chemotherapy on the development of executive functioning, not limited to the effect of late-prematurity.

SIGNIFICANCE

This study emphasizes the necessity of a long-term follow-up of prenatal-exposed children to re-inform clinical practice on the costs and benefits of late-premature induction over treatment during pregnancy.

Cross-frequency coupling of alpha oscillatory power to the entrainment rhythm of a spatially attended input stream

Neural entrainment and alpha oscillatory power (8-14 Hz) are mechanisms of selective attention. The extent to which these two mechanisms interact, especially in the context of visuospatial attention, is unclear. Here, we show that spatial attention to a delta-frequency, rhythmic visual stimulus in one hemifield results in phase-amplitude coupling between the delta-phase of an entrained frontal source and alpha power generated by ipsilateral visuocortical regions. The driving of ipsilateral alpha power by frontal delta also correlates with task performance. Our analyses suggest that neural entrainment may serve a previously underappreciated role in coordinating macroscale brain networks and that inhibition of processing by alpha power can be coupled to an attended temporal structure. Finally, we note that the observed coupling bolsters one dominant hypothesis of modern cognitive neuroscience, that macroscale brain networks and distributed neural computation are coordinated by oscillatory synchrony and cross-frequency interactions.

Time Warping Solutions for Classifying Artifacts in EEG

The most common brain-computer interface (BCI) devices use electroencephalography (EEG). EEG signals are noisy owing to the presence of many artifacts, namely head movement, and facial movements like eye blinks or jaw movements. Removal of these artifacts from EEG signals is essential for the success of any downstream BCI application. These artifacts influence different sensors of the EEG. In this paper, we devise algorithms for detection and classification of artifacts. Classification of artifacts into head nod, jaw movement and eye-blink is performed using two different varieties of time warping; namely, linear time warping, and dynamic time warping. The average accuracy of 85% and 90% is obtained using the former, and the later, respectively.

The peripheral preview effect with faces: Combined EEG and eye-tracking suggests multiple stages of trans-saccadic predictive and non-predictive processing

The world appears stable despite saccadic eye-movements. One possible explanation for this phenomenon is that the visual system predicts upcoming input across saccadic eye-movements based on peripheral preview of the saccadic target. We tested this idea using concurrent electroencephalography (EEG) and eye-tracking. Participants made cued saccades to peripheral upright or inverted face stimuli that changed orientation (invalid preview) or maintained orientation (valid preview) while the saccade was completed. Experiment 1 demonstrated better discrimination performance and a reduced fixation-locked N170 component (fN170) with valid than with invalid preview, demonstrating integration of pre- and post-saccadic information. Moreover, the early fixation-related potentials (FRP) showed a preview face inversion effect suggesting that some pre-saccadic input was represented in the brain until around 170 ms post fixation-onset. Experiment 2 replicated Experiment 1 and manipulated the proportion of valid and invalid trials to test whether the preview effect reflects context-based prediction across trials. A whole-scalp Bayes factor analysis showed that this manipulation did not alter the fN170 preview effect but did influence the face inversion effect before the saccade. The pre-saccadic inversion effect declined earlier in the mostly invalid block than in the mostly valid block, which is consistent with the notion of pre-saccadic expectations. In addition, in both studies, we found strong evidence for an interaction between the pre-saccadic preview stimulus and the post-saccadic target as early as 50 ms (Experiment 2) or 90 ms (Experiment 1) into the new fixation. These findings suggest that visual stability may involve three temporal stages: prediction about the saccadic target, integration of pre-saccadic and post-saccadic information at around 50-90 ms post fixation onset, and post-saccadic facilitation of rapid categorization.

EEG Frontal Alpha Asymmetry and Dream Affect: Alpha Oscillations over the Right Frontal Cortex during REM Sleep and Presleep Wakefulness Predict Anger in REM Sleep Dreams

Affective experiences are central not only to our waking life but also to rapid eye movement (REM) sleep dreams. Despite our increasing understanding of the neural correlates of dreaming, we know little about the neural correlates of dream affect. Frontal alpha asymmetry (FAA) is considered a marker of affective states and traits as well as affect regulation in the waking state. Here, we explored whether FAA during REM sleep and during evening resting wakefulness is related to affective experiences in REM sleep dreams. EEG recordings were obtained from 17 human participants (7 men) who spent 2 nights in the sleep laboratory. Participants were awakened 5 min after the onset of every REM stage after which they provided a dream report and rated their dream affect. Two-minute preawakening EEG segments were analyzed. Additionally, 8 min of evening presleep and morning postsleep EEG were recorded during resting wakefulness. Mean spectral power in the alpha band (8-13 Hz) and corresponding FAA were calculated over the frontal (F4-F3) sites. Results showed that FAA during REM sleep, and during evening resting wakefulness, predicted ratings of dream anger. This suggests that individuals with greater alpha power in the right frontal hemisphere may be less able to regulate (i.e., inhibit) strong affective states, such as anger, in dreams. Additionally, FAA was positively correlated across wakefulness and REM sleep. Together, these findings imply that FAA may serve as a neural correlate of affect regulation not only in the waking but also in the dreaming state.SIGNIFICANCE STATEMENT We experience emotions not only during wakefulness but also during dreaming. Despite our increasing understanding of the neural correlates of dreaming, we know little about the neural correlates of dream emotions. Here we used electroencephalography to explore how frontal alpha asymmetry (FAA)-the relative difference in alpha power between the right and left frontal cortical areas that is associated with emotional processing and emotion regulation in wakefulness-is related to dream emotions. We show that individuals with greater FAA (i.e., greater right-sided alpha power) during rapid eye movement sleep, and during evening wakefulness, experience more anger in dreams. FAA may thus reflect the ability to regulate emotions not only in the waking but also in the dreaming state.

Comparison of a Pragmatic and Regression Approach for Wearable EEG Signal Quality Assessment

Wearable electroencephalogram (EEG) solutions allow portability and real-time measurements in uncontrolled conditions. For reliable and reproducible interpretation of the EEG data, it is essential to accurately identify EEG segments contaminated by artefacts. Two data quality indicator approaches are proposed: pragmatic and regression based. The former extracts statistical features and applies data-driven thresholding, while the latter uses a regression model on the same set of statistical features to predict data quality. The performance of the approaches is validated against EEG data recorded during uncontrolled laboratory and free-living conditions, and compared to a validated approach. The proposed approaches achieve average accuracy of over [Formula: see text] in detecting artefactual data, which is higher than the FORCe signal quality estimation method ([Formula: see text]). The main strength of the proposed algorithms is in the significant increase of specificity over the state-of-the-art. The two models perform equally across different databases. Training of the two approaches on free-living conditions data showed better generalization when tested on different types of databases, i.e., uncontrolled laboratory and free-living. Although the accuracy in determining artefact-contaminated data is highest when using a window size of 8 s, the accuracy drop is minor when using shorter window size, demonstrating another advantage over existing methods. Given low complexity of both pragmatic and regression approach, it facilitates a real-time implementation, which is demonstrated using a wearable EEG headset system available at IMEC.

The ICLabel dataset of electroencephalographic (EEG) independent component (IC) features

The ICLabel dataset is comprised of training and test sets of a set of spatiotemporal features of electroencephalographic (EEG) independent components (IC). The ICLabel training set feature sets were computed for over 200,000 EEG ICs from more than 6,000 existing EEG recordings. More than 8,000 of these ICs have accompanying crowdsourced IC labels across seven IC categories: Brain, Muscle, Eye, Heart, Line Nosie, Channel Noise, and Other. The feature-sets included in the ICLabel dataset are scalp topography images, channel-based scalp topography measures, power spectral densities (PSD) measures (median, variance and kurtosis) and autocorrelation functions, equivalent current dipole (ECD) model fits for single and bilaterally symmetric dipole models, plus features used in several published IC classifier approaches. The ICLabel test set is comprised of 130 ICs from 10 datasets not included in the training set. Each of the test set ICs has an associated IC label estimated based on labels provided by six ICA-EEG experts. Files necessary for adding to and amending the dataset are also included, plus a python class containing useful methods for interacting with the dataset, and IC classifications produced by several existing IC classifiers. These data are linked to the article, "ICLabel: An automated electroencephalographic independent component classifier, dataset, and website" [1]. An active tutorial and crowdsourcing website is available: iclabel.ucsd.edu/tutorial/overview.

An Unsupervised Method for Artefact Removal in EEG Signals

Objective: The activity of the brain can be recorded by means of an electroencephalogram (EEG). An EEG is a multichannel signal related to brain activity. However, EEG presents a wide variety of undesired artefacts. Removal of these artefacts is often done using blind source separation methods (BSS) and mainly those based on Independent Component Analysis (ICA). ICA-based methods are well-accepted in the literature for filtering artefacts and have proved to be satisfactory in most scenarios of interest. Our goal is to develop a generic and unsupervised ICA-based algorithm for EEG artefacts removal. Approach: The proposed algorithm makes use of a new unsupervised artefact detection, ICA and a statistical criterion to automatically select the artefact related independent components (ICs) requiring no human intervention. The algorithm is evaluated using both simulated and real EEG data with artefacts (SEEG and AEEG). A comparison between the proposed unsupervised selection of ICs related to the artefact and other supervised selection is also presented. Main results: A new unsupervised ICA-based algorithm to filter artefacts, where ICs related to each artefact are automatically selected. It can be used in online applications, it preserves most of the original information among the artefacts and removes different types of artefacts. Significance: ICA-based methods for filtering artefacts prevail in the literature. The work in this article is important insofar as it addresses the problem of automatic selection of ICs in ICA-based methods. The selection is unsupervised, avoiding the manual ICs selection or a learning process involved in other methods. Our method is a generic algorithm that allows removing EEG artefacts of various types and, unlike some ICA-based algorithms, it retains most of the original information among the artefacts. Within the algorithm, the artefact detection method implemented does not require human intervention either.

ICLabel: An automated electroencephalographic independent component classifier, dataset, and website

The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low-cost measure of mesoscale brain dynamics with high temporal resolution. Although signals recorded in parallel by multiple, near-adjacent EEG scalp electrode channels are highly-correlated and combine signals from many different sources, biological and non-biological, independent component analysis (ICA) has been shown to isolate the various source generator processes underlying those recordings. Independent components (IC) found by ICA decomposition can be manually inspected, selected, and interpreted, but doing so requires both time and practice as ICs have no order or intrinsic interpretations and therefore require further study of their properties. Alternatively, sufficiently-accurate automated IC classifiers can be used to classify ICs into broad source categories, speeding the analysis of EEG studies with many subjects and enabling the use of ICA decomposition in near-real-time applications. While many such classifiers have been proposed recently, this work presents the ICLabel project comprised of (1) the ICLabel dataset containing spatiotemporal measures for over 200,000 ICs from more than 6000 EEG recordings and matching component labels for over 6000 of those ICs, all using common average reference, (2) the ICLabel website for collecting crowdsourced IC labels and educating EEG researchers and practitioners about IC interpretation, and (3) the automated ICLabel classifier, freely available for MATLAB. The ICLabel classifier improves upon existing methods in two ways: by improving the accuracy of the computed label estimates and by enhancing its computational efficiency. The classifier outperforms or performs comparably to the previous best publicly available automated IC component classification method for all measured IC categories while computing those labels ten times faster than that classifier as shown by a systematic comparison against other publicly available EEG IC classifiers.

Automatic Removal of Cardiac Interference (ARCI): A New Approach for EEG Data

EEG recordings are generally affected by interference from physiological and non-physiological sources which may obscure underlying brain activity and hinder effective EEG analysis. In particular, cardiac interference can be caused by the electrical activity of the heart and/or cardiovascular activity related to blood flow. Successful EEG application in sports science settings requires a method for artifact removal that is automatic and flexible enough to be applied in a variety of acquisition conditions without requiring simultaneous ECG recordings that could restrict movement. We developed an automatic method for classifying and removing both electrical cardiac and cardiovascular artifacts (ARCI) that does not require additional ECG recording. Our method employs independent component analysis (ICA) to isolate data independent components (ICs) and identifies the artifactual ICs by evaluating specific IC features in the time and frequency domains. We applied ARCI to EEG datasets with cued artifacts and acquired during an eyes-closed condition. Data were recorded using a standard EEG wet cap with either 128 or 64 electrodes and using a novel dry electrode cap with either 97 or 64 dry electrodes. All data were decomposed into different numbers of components to evaluate the effect of ICA decomposition level on effective cardiac artifact detection. ARCI performance was evaluated by comparing automatic ICs classifications with classifications performed by experienced investigators. Automatic and investigator classifications were highly consistent resulting in an overall accuracy greater than 99% in all datasets and decomposition levels, and an average sensitivity greater than 90%. Best results were attained when data were decomposed into a fewer number of components where the method achieved perfect sensitivity (100%). Performance was also evaluated by comparing automatic component classification with externally recorded ECG. Results showed that ICs automatically classified as artifactual were significantly correlated with ECG activity whereas the other ICs were not. We also assessed that the interference affecting EEG signals was reduced by more than 82% after automatic artifact removal. Overall, ARCI represents a significant step in the detection and removal of cardiac-related EEG artifacts and can be applied in a variety of acquisition settings making it ideal for sports science applications.

Trial-by-trial surprise-decoding model for visual and auditory binary oddball tasks

Having to survive in a continuously changing environment has driven the human brain to actively predict the future state of its surroundings. Oddball tasks are specific types of experiments in which this nature of the human brain is studied. Detailed mathematical models have been constructed to explain the brain's perception in these tasks. These models consider a subject as an ideal observer who abstracts a hypothesis from the previous stimuli, and estimates its hyper-parameters - in order to make the next prediction. The corresponding prediction error is assumed to manifest the subjective surprise of the brain. While the approach of earlier works to this problem has been to suggest an encoding model, we investigated the reverse model: if the stimuli's surprise is assumed as the cause of the observer's surprise, it must be possible to decode the surprise of each stimulus, for every single subject, given only their neural responses, i.e. to tell how unexpected a specific stimulus has been for them. Employing machine learning tools, we developed a surprise decoding model for binary oddball tasks. We constructed our model using the ideal observer proposed by Meyniel et al. in 2016, and applied it to three datasets, one with visual, one with auditory, and one with both visual and auditory stimuli. We demonstrated that our decoding model performs very well for both of the sensory modalities with or without the presence of the subject's motor response.

Beta band oscillatory deficits during working memory encoding in adolescents with attention-deficit hyperactive disorder

Attention-deficit hyperactivity disorder (ADHD) is a neurobehavioural disorder, characterized by symptoms of inattention and/or hyperactivity/impulsivity, in addition to various cognitive deficits, including working memory impairments. This pathology arises from a complex constellation of genetic, structural and neurotransmission abnormalities, which give rise to the aberrant electrophysiological patterns evident in patients with ADHD. Among such, findings have consistently provided support in favour of weaker power across the beta frequency range. Evidence has also emerged that beta rhythmic decrements are linked to working memory encoding. The catecholaminergic modulation of both working memory and beta oscillations may suggest that the link between the two might be rooted at the neurotransmission level. Studies have consistently shown that ADHD involves significant catecholaminergic dysregulation, which is also supported by other clinical studies that demonstrate stimulant-induced amelioration of ADHD symptomology. In this study, we explore the possible ways that might relate ADHD, working memory, beta rhythms and catecholaminergic signalling altogether by investigating the integrity of encoding-relevant electroencephalographic beta rhythms in medication-naïve and stimulant-medicated adolescent patients. The aberrant parietal and frontal encoding-related beta rhythm revealed in the ADHD patients together with a working memory (WM) deficit as observed herein was reversed by methylphenidate in the latter case but not with regard to the beta rhythm. This finding per se raises the issue of the role played by beta rhythms in the WM deficits associated with ADHD.

Artefacts Removal to Detect Visual Evoked Potentials in Brain Computer Interface Systems

The interference of artefacts with evoked scalp electroencephalogram (EEG) responses is a problem in event related brain computer interface (BCI) system that reduces signal quality and interpretability of user's intentions. Many strategies have been proposed to reduce the effects of non-neural artefacts, while the activity of neural sources that do not reflect the considered stimulation has been neglected. However discerning such activities from those to be retained is important, but subtle and difficult as most of their features are the same. We propose an automated method based on a combination of a genetic algorithm (GA) and a support vector machine (SVM) to select only the sources of interest. Temporal, spectral, wavelet, autoregressive and spatial properties of independent components (ICs) of EEG are inspected. The method selects the most distinguishing subset of features among this comprehensive fused set of information and identifies the components to be preserved. EEG data were recorded from 12 healthy subjects in a visual evoked potential (VEP) based BCI paradigm and the corresponding ICs were classified by experts to train and test the algorithm. They were contaminated with different sources of artefacts, including electromyogram (EMG), electrode connection problems, blinks and electrocardiogram (ECG), together with neural contributions not related to VEPs. The accuracy of ICs classification was about 88.5% and the energetic residual error in recovering the clean signals was 3%. These performances indicate that this automated method can effectively identify and remove main artefacts derived from either neural or non-neural sources while preserving VEPs. This could have important potential applications, contributing to speed and remove subjectivity of the cleaning procedure by experts. Moreover, it could be included in a real time BCI as a pre-processing step before the identification of the user’s intention.

Interindividual neural differences in moral decision-making are mediated by alpha power and delta/theta phase coherence

As technology in Artificial Intelligence has developed, the question of how to program driverless cars to respond to an emergency has arisen. It was recently shown that approval of the consequential behavior of driverless cars varied with the number of lives saved and showed interindividual differences, with approval increasing alongside the number of lives saved. In the present study, interindividual differences in individualized moral decision-making at both the behavioral and neural level were investigated using EEG. It was found that alpha event-related spectral perturbation (ERSP) and delta/theta phase-locking - intertrial coherence (ITC) and phase-locking value (PLV) - play a central role in mediating interindividual differences in Moral decision-making. In addition, very late alpha activity differences between individualized and shared stimuli, and delta/theta ITC, where shown to be closely related to reaction time and subjectively perceived emotional distress. This demonstrates that interindividual differences in Moral decision-making are mediated neuronally by various markers - late alpha ERSP, and delta/theta ITC - as well as psychologically by reaction time and perceived emotional distress. Our data show, for the first time, how and according to which neuronal and behavioral measures interindividual differences in Moral dilemmas can be measured.

Spontaneous eye movements during focused-attention mindfulness meditation

Oculometric measures have been proven to be useful markers of mind-wandering during visual tasks such as reading. However, little is known about ocular activity during mindfulness meditation, a mental practice naturally involving mind-wandering episodes. In order to explore this issue, we extracted closed-eyes ocular movement measurements via a covert technique (EEG recordings) from expert meditators during two repetitions of a 7-minute mindfulness meditation session, focusing on the breath, and two repetitions of a 7-minute instructed mind-wandering task. Power spectral density was estimated on both the vertical and horizontal components of eye movements. The results show a significantly smaller average amplitude of eye movements in the delta band (1–4 Hz) during mindfulness meditation than instructed mind-wandering. Moreover, participants’ meditation expertise correlated significantly with this average amplitude during both tasks, with more experienced meditators generally moving their eyes less than less experienced meditators. These findings suggest the potential use of this measure to detect mind-wandering episodes during mindfulness meditation and to assess meditation performance.

EEG correlation at a distance: A re-analysis of two studies using a machine learning approach

Background: In this paper, data from two studies relative to the relationship between the electroencephalogram (EEG) activities of two isolated and physically separated subjects were re-analyzed using machine-learning algorithms.

The first dataset comprises the data of 25 pairs of participants where one member of each pair was stimulated with a visual and an auditory 500 Hz signals of 1 second duration.

The second dataset consisted of the data of 20 pairs of participants where one member of each pair received visual and auditory stimulation lasting 1 second duration with on-off modulation at 10, 12, and 14 Hz.

Methods and Results: Applying a ‘linear discriminant classifier’ to the first dataset, it was possible to correctly classify 50.74% of the EEG activity of non-stimulated participants, correlated to the remote sensorial stimulation of the distant partner.

In the second dataset, the percentage of correctly classified EEG activity in the non-stimulated partners was 51.17%, 50.45% and 51.91%, respectively, for the 10, 12, and 14 Hz stimulations, with respect the condition of no stimulation in the distant partner.

Conclusions: The analysis of EEG activity using machine-learning algorithms has produced advances in the study of the connection between the EEG activities of the stimulated partner and the isolated distant partner, opening new insight into the possibility to devise practical application for non-conventional “mental telecommunications” between physically and sensorially separated participants.

Deep Convolutional Neural Networks for Feature-Less Automatic Classification of Independent Components in Multi-Channel Electrophysiological Brain Recordings

OBJECTIVE

Interpretation of the electroencephalographic (EEG) and magnetoencephalographic (MEG) signals requires off-line artifacts removal. Since artifacts share frequencies with brain activity, filtering is insufficient. Blind source separation, mainly through independent component analysis (ICA), is the gold-standard procedure for the identification of artifacts in multi-dimensional recordings. However, a classification of brain and artifactual independent components (ICs) is still required. Since ICs exhibit recognizable patterns, classification is usually performed by experts' visual inspection. This procedure is time consuming and prone to errors. Automatic ICs classification has been explored, often through complex ICs features extraction prior to classification. Relying on deep-learning ability of self-extracting the features of interest, we investigated the capabilities of convolutional neural networks (CNNs) for off-line, automatic artifact identification through ICs without feature selection.

METHODS

A CNN was applied to spectrum and topography of a large dataset of few thousand samples of ICs obtained from multi-channel EEG and MEG recordings acquired during heterogeneous experimental settings and on different subjects. CNN performances, when applied to EEG, MEG, and combined EEG and MEG ICs, were explored and compared with state-of-the-art feature-based automatic classification.

RESULTS

Beyond state-of-the-art automatic classification accuracies were demonstrated through cross validation (92.4% EEG, 95.4% MEG, 95.6% EEG+MEG).

CONCLUSION

High CNN classification performances were achieved through heuristical selection of machinery hyperparameters and through the CNN self-selection of the features of interest.

SIGNIFICANCE

Considering the large data availability of multi-channel EEG and MEG recordings, CNNs may be suited for classification of ICs of multi-channel brain electrophysiological recordings.

Cortical auditory evoked potentials and hemispheric specialization of speech in individuals with learning disability and healthy controls: A preliminary study

Background: Dichotic listening (DL) technique is a behavioral non-invasive tool which is used in studying hemispheric lateralization. Previous studies using behavioral DL have hypothesized that individuals with learning disabilities (LD) exhibit a lack of cortical specialization for processing speech stimulus. However, there is no event related potential (ERP) evidence, hence the main objective of the study is to explore hemispheric asymmetry using cortical auditory evoked potential (CAEPs) in normal hearing adults and also to compare the same in children with LD and healthy controls.

Methods: CAEPs were recorded in 16 normal hearing young adults, eight right-handed children with LD and their age matched controls. Two stop constants (/Pa/ – voiceless, bilabial, stop: /Ta/ - voiceless, alveolar, stop) were chosen for this experiment and presented in each ear and dichotically in two different orders (/pa-ta/, /ta-pa/). ERPs were processed using a standard pipeline, and electrodes readings over the left and right hemispheres were averaged to create left and right regions of interest (ROI). The CAEPs were analyzed for mean amplitude and peak latency of P1-N1-P2 components.

Results: The current study results suggest no statistically significant difference between the two stimulus in monaural condition and absence of order effect in dichotic condition. In healthy controls the CAEP latencies were shorter over the left hemisphere in both monaural and dichotic conditions in adults and control children. However, it was very evident that such a difference was lacking in children with LD.

Conclusions: Hemispheric asymmetry can be detected using CAEPs for speech stimulus. The measures are consistent and void of stimulus or order effect. Taken together, the findings of current study, both monaural and dichotic condition illustrates the hemispheric differences in processing speech stimuli in normal hearers. Absence of latency differences between hemispheres in children with LD indicate a lack of hemispheric asymmetry.

ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading

We present the Zurich Cognitive Language Processing Corpus (ZuCo), a dataset combining electroencephalography (EEG) and eye-tracking recordings from subjects reading natural sentences. ZuCo includes high-density EEG and eye-tracking data of 12 healthy adult native English speakers, each reading natural English text for 4-6 hours. The recordings span two normal reading tasks and one task-specific reading task, resulting in a dataset that encompasses EEG and eye-tracking data of 21,629 words in 1107 sentences and 154,173 fixations. We believe that this dataset represents a valuable resource for natural language processing (NLP). The EEG and eye-tracking signals lend themselves to train improved machine-learning models for various tasks, in particular for information extraction tasks such as entity and relation extraction and sentiment analysis. Moreover, this dataset is useful for advancing research into the human reading and language understanding process at the level of brain activity and eye-movement.

Automatic Independent Component Scalp Map Analysis of Electroencephalogram During Motor Preparation

This work presents a method for automatic independent component (IC) scalp map analysis of electroencephalogram during motor preparation in visuomotor tasks. The strength of this approach is the analysis of the IC scalp maps based on the apriori given mask. This uses an image processing approach, comparable to visual classification used by experts, to automate the selection of relevant ICs in visuomotor tasks. Thirty iterations of the Infomax ICA algorithm were used to test the reliability of the ICs. ICs above 95% quality index were used for IC scalp topography image analysis. Here, we used a linkage-clustering algorithm for IC clustering and gap statistic to estimate the number of clusters. After classifying the components with our approach, the labels were compared to those from well-known MARA ("Multiple Artifact Rejection Algorithm") - an open-source EEGLAB plug-in. It was found that 334 of the 568 labels were in-agreement. MARA labeled 81 out of the 177 source-related components, and 238 out of the 319 non-source-related components, as artifacts. Here, the strength of our approach lies in using an image-processing algorithm to identify the task-specific ICs whereas MARA focuses on the automatic classification of the artifactual ICs by combining stereotyped artifact-specific spatial and temporal features that depend on the electrode montage. After "artefactual" ICs are removed, task-specific ICs still remains to be identified from the remaining "good" ICs where our scalp topography image analysis approach can be applied. Our IC scalp topography image analysis is focused on task-specific IC selection based on an apriori mask, which is not limited to specific EEG features and/or electrode configurations for high-density EEG.

Cognitive resilience after prolonged task performance: an ERP investigation

Deleterious consequences of cognitive fatigue might be avoided if people respond with increased effort to increased demands. In this study, we hypothesized that the effects of fatigue would be more pronounced in cognitive functions reflecting compensatory effort. Given that the P3a event-related potential is sensitive to the direction and amount of attention allocated to a stimulus array, we reasoned that compensatory effort would manifest in increased P3a amplitudes. Therefore, we compared P3a before (pre-test) and after (post-test) a 2 h long cognitively demanding (fatigue group, n = 18) or undemanding task (control group, n = 18). Two auditory tasks, a three-stimulus novelty oddball and a duration discrimination two-choice response task were presented to elicit P3a. In the fatigue group, we used the multi-attribute task battery as a fatigue-inducing task. This task draws on a broad array of attentional functions and imposed considerable workload. The control group watched mood-neutral documentary films. The fatigue manipulation was effective as subjective fatigue increased significantly in the fatigue group compared to controls. Contrary to expectations, however, fatigue failed to affect P3a in the post-test phase. Similar null effects were obtained for other neurobehavioral measures (P3b and behavioral performance). Results indicate that a moderate increase in subjective fatigue does not hinder cognitive functions profoundly. The lack of objective performance loss in the present study suggests that the cognitive system can be resilient against challenges instigated by demanding task performance.

Comparison of EEG microstates with resting state fMRI and FDG-PET measures in the default mode network via simultaneously recorded trimodal (PET/MR/EEG) data

Simultaneous trimodal positron emission tomography/magnetic resonance imaging/electroencephalography (PET/MRI/EEG) resting state (rs) brain data were acquired from 10 healthy male volunteers. The rs-functional MRI (fMRI) metrics, such as regional homogeneity (ReHo), degree centrality (DC) and fractional amplitude of low-frequency fluctuations (fALFFs), as well as 2-[18F]fluoro-2-desoxy-d-glucose (FDG)-PET standardised uptake value (SUV), were calculated and the measures were extracted from the default mode network (DMN) regions of the brain. Similarly, four microstates for each subject, showing the diverse functional states of the whole brain via topographical variations due to global field power (GFP), were estimated from artefact-corrected EEG signals. In this exploratory analysis, the GFP of microstates was nonparametrically compared to rs-fMRI metrics and FDG-PET SUV measured in the DMN of the brain. The rs-fMRI metrics (ReHO, fALFF) and FDG-PET SUV did not show any significant correlations with any of the microstates. The DC metric showed a significant positive correlation with microstate C (r_s  = 0.73, p = .01). FDG-PET SUVs indicate a trend for a negative correlation with microstates A, B and C. The positive correlation of microstate C with DC metrics suggests a functional relationship between cortical hubs in the frontal and occipital lobes. The results of this study suggest further exploration of this method in a larger sample and in patients with neuropsychiatric disorders. The aim of this exploratory pilot study is to lay the foundation for the development of such multimodal measures to be applied as biomarkers for diagnosis, disease staging, treatment response and monitoring of neuropsychiatric disorders.

Multi-class EEG classification of motor imagery signal by finding optimal time segments and features using SNR-based mutual information

The electroencephalogram signals are used to distinguish different motor imagery tasks in brain-computer interfaces. In most studies, in order to classify the EEG signals recorded in a cue-guided BCI paradigm, time segments for feature extraction after the onset of the visual cue were selected manually. In addition, in these studies the authors have selected a single identical time segment for different subjects. The present study emphasized on the inter-individual variability and difference between different motor imagery tasks as the potential source of erroneous results and used mutual information and the subject specific time interval to overcome this problem. More specifically, a new method was proposed to automatically find the best subject specific time intervals for the classification of four-class motor imagery tasks by using MI between the BCI input and output. Moreover, the signal-to-noise ratio was used to calculate the MI values, while the MI values were used as feature selection criteria to select the discriminative features. The time segments and the best discriminative features were found by using training data and used to assess the evaluation data. Furthermore, the CSP algorithm was used to extract signal features. The dataset 2A of BCI competition IV used in this study consisted of four different motor imagery signals, which were obtained from nine different subjects. One Vs One decomposition scheme was used to deal with the multi-class nature of the problem. The MI values showed that the obtained time segments not only varied between different subjects but also varied between different classifiers of different pair of classes. Finally, the results suggested that the proposed method was efficient in classifying multi-class motor imagery signals as compared to other classification strategies proposed by the other studies.

Visual cues of threat elicit greater steady-state electroencephalographic responses than visual reminders of death

Terror management theory (TMT) suggests that reminders of death activate an exclusive anxiety mechanism different from the one activated by other types of symbolic threats. This notion is supported by evidence showing how experimental participants verbally reflecting on their own death are then influenced in their opinions and behaviours. A previous study showed that magnitude of electroencephalography (EEG) activity is greater when images depicting death-related content are coupled with painful thermal stimuli compared to threat-related content. Here we expand on previous research by testing whether similar effects may be brought about by passive observation of generic visual reminders of death. More precisely, we hypothesised that fast periodic presentation of death-related vs. more generic threat-related images determine a preferential modulation of brain activity measured by means of EEG. In two experiments, we found that images depicting death content elicit lower frequency-tagged EEG response compared to more generic threat images. Visual evoked potentials revealed that a brief change of the scene from neutral to threat content elicits greater amplitude at the late latencies (compatible with a P300 potential), particularly at the parieto-occipital sites. Altogether, our findings suggest that, in a context where no reflection on death cues is allowed and no threatening stimuli in other modality occur, visual death cues trigger lower neural synchronisation than that elicited by similarly negative and arousing cues with divergent threatening meaning.

Eyes-Closed Increases the Usability of Brain-Computer Interfaces Based on Auditory Event-Related Potentials

Recent research has demonstrated how brain-computer interfaces (BCI) based on auditory stimuli can be used for communication and rehabilitation. In these applications, users are commonly instructed to avoid eye movements while keeping their eyes open. This secondary task can lead to exhaustion and subjects may not succeed in suppressing eye movements. In this work, we investigate the option to use a BCI with eyes-closed. Twelve healthy subjects participated in a single electroencephalography (EEG) session where they were listening to a rapid stream of bisyllabic words while alternatively having their eyes open or closed. In addition, we assessed usability aspects for the two conditions with a questionnaire. Our analysis shows that eyes-closed does not reduce the number of eye artifacts and that event-related potential (ERP) responses and classification accuracies are comparable between both conditions. Importantly, we found that subjects expressed a significant general preference toward the eyes-closed condition and were also less tensed in that condition. Furthermore, switching between eyes-closed and eyes-open and vice versa is possible without a severe drop in classification accuracy. These findings suggest that eyes-closed should be considered as a viable alternative in auditory BCIs that might be especially useful for subjects with limited control over their eye movements.

Effect of Non-invasive Vagus Nerve Stimulation on Resting-State Electroencephalography and Laser-Evoked Potentials in Migraine Patients: Mechanistic Insights

A recent multicenter trial provided Class I evidence that for patients with an episodic migraine, non-invasive vagus nerve stimulation (nVNS) significantly increases the probability of having mild pain or being pain-free 2 h post-stimulation. Here we aimed to investigate the potential effect of nVNS in the modulation of spontaneous and pain related bioelectrical activity in a subgroup of migraine patients enrolled in the PRESTO trial by using resting-state electroencephalography and trigeminal laser-evoked potentials (LEPs). LEPs were recorded for 27 migraine patients who received active or sham nVNS over the cervical vagus nerve. We measured power values for frequencies between 1–100 Hz in a resting-state condition and the latency and amplitude of N1, N2, and P2 components of LEPs in a basal condition during and after active or sham vagus nerve stimulation (T0, T1, T2). The P2 evoked by the right and the left trigeminal branch was smaller during active nVNS. The sham device also attenuated the P2 amplitude evoked by the left trigeminal branch at T1 and T2, but this attenuation did not reach significance. No changes were observed for N1 amplitude, N1, N2, P2 latency, or pain rating. nVNS induced an increase of EEG power in both slow and fast rhythms, but this effect was not significant as compared to the sham device. These findings suggest that nVNS acts on the cortical areas that are responsible for trigeminal pain control and pave the ground for future studies aimed at confirming the possible correlations with clinical outcomes, including the effect on symptoms that are directly correlated with trigeminal pain processing and modulation.

BEAPP: The Batch Electroencephalography Automated Processing Platform

Electroencephalography (EEG) offers information about brain function relevant to a variety of neurologic and neuropsychiatric disorders. EEG contains complex, high-temporal-resolution information, and computational assessment maximizes our potential to glean insight from this information. Here we present the Batch EEG Automated Processing Platform (BEAPP), an automated, flexible EEG processing platform incorporating freely available software tools for batch processing of multiple EEG files across multiple processing steps. BEAPP does not prescribe a specified EEG processing pipeline; instead, it allows users to choose from a menu of options for EEG processing, including steps to manage EEG files collected across multiple acquisition setups (e.g., for multisite studies), minimize artifact, segment continuous and/or event-related EEG, and perform basic analyses. Overall, BEAPP aims to streamline batch EEG processing, improve accessibility to computational EEG assessment, and increase reproducibility of results.