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

Neurophysiological mechanisms underlying the differential effect of reward prospect on response selection and inhibition

Reward and cognitive control play crucial roles in shaping goal-directed behavior. Yet, the behavioral and neural underpinnings of interactive effects of both processes in driving our actions towards a particular goal have remained rather unclear. Given the importance of inhibitory control, we investigated the effect of reward prospect on the modulatory influence of automatic versus controlled processes during response inhibition. For this, a performance-contingent monetary reward for both correct response selection and response inhibition was added to a Simon NoGo task, which manipulates the relationship of automatic and controlled processes in Go and NoGo trials. A neurophysiological approach was used by combining EEG temporal signal decomposition and source localization methods. Compared to a non-rewarded control group, rewarded participants showed faster response execution, as well as overall lower response selection and inhibition accuracy (shifted speed-accuracy tradeoff). Interestingly, the reward group displayed a larger interference of the interactive effects of automatic versus controlled processes during response inhibition (i.e., a larger Simon NoGo effect), but not during response selection. The reward-specific behavioral effect was mirrored by the P3 amplitude, underlining the importance of stimulus-response association processes in explaining variability in response inhibition performance. The selective reward-induced neurophysiological modulation was associated with lower activation differences in relevant structures spanning the inferior frontal and parietal cortex, as well as higher activation differences in the somatosensory cortex. Taken together, this study highlights relevant neuroanatomical structures underlying selective reward effects on response inhibition and extends previous reports on the possible detrimental effect of reward-triggered performance trade-offs on cognitive control processes.

Investigation of the Common Independent Component Analysis Approaches in Biological Signals for Removing Cardiac Field Artefact from EEG

Electroencephalography (EEG) signals are often impacted by the cardiac field artefact (CFA), which can compromise EEG analysis. Independent component analysis (ICA) has proven effective in removing such artefacts, including CFA. This paper examines three well-known ICA algorithms commonly utilized in EEG signal processing and assesses their ability to decompose EEG into independent components (ICs) to remove CFA. The paper also investigates whether a new two-level ICA approach can improve performance. Results are evaluated using a synthetic dataset of 10 subjects.

Extracting electromyographic signals from multi-channel LFPs using independent component analysis without direct muscular recording

Electromyography (EMG) has been commonly used for the precise identification of animal behavior. However, it is often not recorded together with in vivo electrophysiology due to the need for additional surgeries and setups and the high risk of mechanical wire disconnection. While independent component analysis (ICA) has been used to reduce noise from field potential data, there has been no attempt to proactively use the removed "noise," of which EMG signals are thought to be one of the major sources. Here, we demonstrate that EMG signals can be reconstructed without direct EMG recording using the "noise" ICA component from local field potentials. The extracted component is highly correlated with directly measured EMG, termed IC-EMG. IC-EMG is useful for measuring an animal's sleep/wake, freezing response, and non-rapid eye movement (NREM)/REM sleep states consistently with actual EMG. Our method has advantages in precise and long-term behavioral measurement in wide-ranging in vivo electrophysiology experiments.

Auricular Transcutaneous Vagus Nerve Stimulation Specifically Enhances Working Memory Gate Closing Mechanism: A System Neurophysiological Study

Everyday tasks and goal-directed behavior involve the maintenance and continuous updating of information in working memory (WM). WM gating reflects switches between these two core states. Neurobiological considerations suggest that the catecholaminergic and the GABAergic are likely involved in these dynamics. Both of these neurotransmitter systems likely underlie the effects to auricular transcutaneous vagus nerve stimulation (atVNS). We examine the effects of atVNS on WM gating dynamics and their underlying neurophysiological and neurobiological processes in a randomized crossover study design in healthy humans of both sexes. We show that atVNS specifically modulates WM gate closing and thus specifically modulates neural mechanisms enabling the maintenance of information in WM. WM gate opening processes were not affected. atVNS modulates WM gate closing processes through the modulation of EEG alpha band activity. This was the case for clusters of activity in the EEG signal referring to stimulus information, motor response information, and fractions of information carrying stimulus-response mapping rules during WM gate closing. EEG-beamforming shows that modulations of activity in fronto-polar, orbital, and inferior parietal regions are associated with these effects. The data suggest that these effects are not because of modulations of the catecholaminergic (noradrenaline) system as indicated by lack of modulatory effects in pupil diameter dynamics, in the inter-relation of EEG and pupil diameter dynamics and saliva markers of noradrenaline activity. Considering other findings, it appears that a central effect of atVNS during cognitive processing refers to the stabilization of information in neural circuits, putatively mediated via the GABAergic system.SIGNIFICANCE STATEMENT Goal-directed behavior depends on how well information in short-term memory can be flexibly updated but also on how well it can be shielded from distraction. These two functions were guarded by a working memory gate. We show how an increasingly popular brain stimulation techniques specifically enhances the ability to close the working memory gate to shield information from distraction. We show what physiological and anatomic aspects underlie these effects.

Mu Rhythm Desynchronization while Observing Rubber Hand Movement in the Mirror: The Interaction of Body Representation with Visuo-Tactile Stimulation

During rubber hand illusion (RHI), participants feel that a rubber (fake) hand is their own (i.e., embodiment of the rubber hand) if the unseen real hand and seen rubber hand are stroked synchronously (i.e., visuo-tactile stimuli). The RHI is also evoked if the real and rubber hands are placed in the same position (i.e., visual-proprioceptive congruency), which can be performed using a mirror setting. Using electroencephalography (EEG) and mirror settings, we compared μ rhythm (8-13 Hz) event-related desynchronization (ERD; an index of sensorimotor activation) while watching the movements of embodied or non-embodied rubber hands, which was preceded by an observation of the rubber hand with or without synchronous visuo-tactile stimuli. The illusory ownership of the fake hand was manipulated using visual continuity with (RHI) and without (non-RHI) a fake forearm. Resultantly, an ownership-dependent μ rhythm ERD was found when delivering visuo-tactile stimuli; a greater and more persistent μ rhythm ERD during the rubber hand movement was identified in the RHI in comparison to the non-RHI condition. However, no difference was observed between the two when observing the fake hand alone. These findings suggest the possibility that a self-related multisensory interaction between body representation (top-down processing) and visuo-tactile inputs (bottom-up processing) before a fake hand movement produces ownership-dependent sensorimotor activations during subsequent movement observations.

Age and vocabulary knowledge differentially influence the N400 and theta responses during semantic retrieval

Using electroencephalography (EEG) to study the neural oscillations supporting language development is increasingly common; however, a clear understanding of the relationship between neural oscillations and traditional Event Related Potentials (ERPs) is needed to disentangle how maturation of language-related neural networks supports semantic processing throughout grade school. Theta and the N400 are both thought to index semantic retrieval but, in adults, are only weakly correlated with one another indicating they may measure somewhat unique aspects of retrieval. Here, we studied the relationship between the N400 amplitude and theta power during semantic retrieval with key indicators of language abilities including age, vocabulary, reading comprehension and phonological memory in 226 children ages 8-15 years. The N400 and theta responses were positively correlated over posterior areas, but negatively correlated over frontal areas. When controlling for the N400 amplitude, the amplitude of the theta response was predicted by age, but not by language measures. On the other hand, when controlling theta amplitude, the amplitude of the N400 was predicted by both vocabulary knowledge and age. These findings indicate that while there is a clear relationship between the N400 and theta responses, they may each index unique aspects of development related to semantic retrieval.

Associations between EEG trajectories, family income, and cognitive abilities over the first two years of life

We sought to characterize developmental trajectories of EEG spectral power over the first 2 years after birth and examine whether family income or maternal education alter those trajectories. We analyzed EEGs (n = 161 infants, 534 EEGs) collected longitudinally between 2 and 24 months of age, and calculated frontal absolute power across 7 canonical frequency bands. For each frequency band, a piecewise growth curve model was fit, resulting in an estimated intercept and two slope parameters from 2 to 9 months and 9-24 months of age. Across 6/7 frequency bands, absolute power significantly increased over age, with steeper slopes in the 2-9 month period compared to 9-24 months. Increased family income, but not maternal education, was associated with higher intercept (2-3 month power) across delta-gamma bands (p range = 0.002-0.04), and reduced change in power between 2 and 9 months of age in lower frequency bands (delta-alpha, p range = 0.01-0.02). There was no significant effect of income on slope between 9 and 24 months. EEG intercept and slope measures did not mediate relationships between income and 24-month verbal and nonverbal development. These results add to growing literature concerning the role of socioeconomic factors in shaping brain trajectories.

Temporal imprecision and phase instability in schizophrenia resting state EEG

Schizophrenia is characterized by temporal imprecision and irregularities on neuronal, psychological cognitive, and behavioral levels which are usually tested during task-related activity. This leaves open whether analogous temporal imprecision and irregularities can already be observed in the brain's spontaneous activity as measured during the resting state; this is the goal of our study. Building on recent task-related data, we, using EEG, aimed to investigate the temporal precision and regularity of phase coherence over time in healthy, schizophrenia, and bipolar disorder participants. To this end, we developed a novel methodology, nominal frequency phase stability (NFPS), that allows to measure stability over phase angles in selected frequencies. By applying sample entropy quantification to the time-series of the nominal frequency phase angle time series, we found increased irregularities in theta activity over a frontocentral electrode in schizophrenia but not in bipolar disorder. We therefore assume that temporal imprecision and irregularity already occur in the brain's spontaneous activity in schizophrenia.

Unravelling brain connectivity patterns in body dysmorphic disorder during decision-making on visual illusions: A graph theoretical approach

Body dysmorphic disorder (BDD) is characterized by an excessive preoccupation with perceived defects in physical appearance, and is associated with compulsive checking. Visual illusions are illusory or distorted subjective perceptions of visual stimuli, which are induced by specific visual cues or contexts. While previous research has investigated visual processing in BDD, the decision-making processes involved in visual illusion processing remain unknown. The current study addressed this gap by investigating the brain connectivity patterns of BDD patients during decision-making about visual illusions. Thirty-six adults - 18 BDD (9 female) and 18 healthy controls (10 female) - viewed 39 visual illusions while their EEG was recorded. For each image, participants were asked to indicate (1) whether they perceived the illusory features of the images; and (2) their degree of confidence in their response. Our results did not uncover group-level differences in susceptibility to visual illusions, supporting the idea that higher-order differences, as opposed to lower-level visual impairments, can account for the visual processing differences that have previously been reported in BDD. However, the BDD group had lower confidence ratings when they reported illusory percepts, reflecting increased feelings of doubt. At the neural level, individuals with BDD showed greater theta band connectivity while making decisions about the visual illusions, likely reflecting higher intolerance to uncertainty and thus increased performance monitoring. Finally, control participants showed increased left-to-right and front-to-back directed connectivity in the alpha band, which may suggest more efficient top-down modulation of sensory areas in control participants compared to individuals with BDD. Overall, our findings are consistent with the idea that higher-order disruptions in BDD are associated with increased performance monitoring during decision-making, which may be related to constant mental rechecking of responses.

Reliability of resting-state EEG modulation by continuous and intermittent theta burst stimulation of the primary motor cortex: A sham-controlled study

Theta burst stimulation (TBS) is a form of repetitive transcranial magnetic stimulation designed to induce changes of cortical excitability that outlast the period of TBS application. In this study, we explored the effects of continuous TBS (cTBS) and intermittent TBS (iTBS) versus sham TBS stimulation, applied to the primary motor cortex, on modulation of resting state electroencephalography (rsEEG) power. We first conducted hypothesis-driven region-of-interest (ROI) analyses examining changes in alpha (8-12 Hz) and beta (13-21 Hz) bands over the left and right motor cortex. Additionally, we performed data-driven whole-brain analyses across a wide range of frequencies (1-50 Hz) and all electrodes. Finally, we assessed the reliability of TBS effects across two sessions approximately 1 month apart. None of the protocols produced significant group-level effects in the ROI. Whole-brain analysis revealed that cTBS significantly enhanced relative power between 19-43 Hz over multiple sites in both hemispheres. However, these results were not reliable across visits. There were no significant differences between EEG modulation by active and sham TBS protocols. Between-visit reliability of TBS-induced neuromodulatory effects was generally low-to-moderate. We discuss confounding factors and potential approaches for improving the reliability of TBS-induced rsEEG modulation.

Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities

Brain-Computer Interfaces (BCI) is an exciting and emerging research area for researchers and scientists. It is a suitable combination of software and hardware to operate any device mentally. This review emphasizes the significant stages in the BCI domain, current problems, and state-of-the-art findings. This article also covers how current results can contribute to new knowledge about BCI, an overview of BCI from its early developments to recent advancements, BCI applications, challenges, and future directions. The authors pointed to unresolved issues and expressed how BCI is valuable for analyzing the human brain. Humans' dependence on machines has led humankind into a new future where BCI can play an essential role in improving this modern world.

Electrophysiological Evidence Reveals the Asymmetric Transfer from the Right to Left Hemisphere as Key to Reading Proficiency

The present investigation aimed to explore the interhemispheric interactions that contribute to changes in reading proficiency by examining the processing of visual word recognition in relation to word familiarity. A lexical decision task was administered to 25 participants, and their electrophysiological activity was recorded. A behavioral analysis showed the faster and more accurate processing of highly familiar words compared to less familiar ones. An event-related potential analysis uncovered an asymmetric familiarity effect over the N100 and N400 components across the two hemispheres, indicating an asymmetrical word familiarity processing. Granger causality analyses demonstrated a stronger transfer of information from the right hemisphere (RH) to the left hemisphere (LH) during the N100 processing and a weaker transfer from the LH to the RH during the N400 processing for highly familiar word recognition. These findings suggest that the asymmetric coordination between the RH and LH occurs early in visual word recognition and highlight the importance of interhemispheric interactions in efficient visual word recognition and proficient reading.

Neural dynamics underlying the processing of implicit form-meaning connections: The dissociative roles of theta and alpha oscillations

Implicit learning plays an important role in the language acquisition. In addition to helping people acquire the form-level rules (e.g., the word order regularities), implicit learning can also facilitate the acquisition of word meanings (i.e., the establishment of connections between the word form and its meanings). Although some behavioral studies have explored the processing of implicit form-meaning connections, the neural dynamics underlying this processing remains unclear. Through examining whether participants could implicitly acquire the literal and metaphorical meanings of novel words, and applying the time-frequency analysis on the electroencephalogram (EEG) data collected in the testing phase, the neural oscillations corresponding to the processing of implicit form-literal and form-metaphorical meaning connections were explored. The results showed that participants in the experimental group could implicitly acquire the form-literal and form-metaphorical meaning connections after training, while participants in the control group who were not trained did not have access to such form-meaning connections. Meanwhile, during the processing of form-literal meaning connections, the greater suppression of alpha oscillations was induced by the testing items that follow the same rules as the training items (i.e., the regular testing items) in the experimental group, whereas the stronger enhancement of theta oscillations was elicited by the regular testing items in the experimental group during the processing of form-metaphorical meaning connections. Our study provides insights for understanding the processing of implicit form-literal and form-metaphorical meaning connections and the neural dynamics underlying the processing.

A generic neural factor linking resting-state neural dynamics and the brain's response to unexpectedness in multilevel cognition

The brain's response to change is fundamental to learning and adaptation; this implies the presence of a universal neural mechanism under various contexts. We hypothesized that this mechanism manifests in neural activity patterns across low and high levels of cognition during task processing as well as in resting-state neural dynamics, because both these elements are different facets of the same dynamical system. We tested our hypothesis by (i) characterizing (a) the neural response to changes in low-level continuous information stream and unexpectedness at different cognitive levels and (b) the spontaneous neural dynamics in resting state, and (ii) examining the associations among the dynamics according to cross-individual variability (n = 200). Our results showed that the brain's response magnitude was monotonically correlated with the magnitude of information fluctuation in a low-level task, forming a simple psychophysical function; moreover, this effect was found to be associated with the brain's response to unexpectedness in high-level cognitive tasks (including language processing). These coherent multilevel neural effects in task processing were also shown to be strongly associated with resting-state neural dynamics characterized by the waxing and waning of Alpha oscillation. Taken together, our results revealed large-scale consistency between the neural dynamic system and multilevel cognition.

Intrinsic neural timescales mediate the cognitive bias of self - temporal integration as key mechanism

Our perceptions and decisions are not always objectively correct as they are featured by a bias related to our self. What are the behavioral, neural, and computational mechanisms of such cognitive bias? Addressing this yet unresolved question, we here investigate whether the cognitive bias is related to temporal integration and segregation as mediated by the brain's Intrinsic neural timescales (INT). Using Signal Detection Theory (SDT), we operationalize the cognitive bias by the Criterion C as distinguished from the sensitivity index d'. This was probed in a self-task based on morphed self- and other faces. Behavioral data demonstrate clear cognitive bias, i.e., Criterion C. That was related to the EEG-based INT as measured by the autocorrelation window (ACW) in especially the transmodal regions dorsolateral prefrontal cortex (dlPFC) and default-mode network (DMN) as distinct from unimodal visual cortex. Finally, simulation of the same paradigm in a large-scale network model shows high degrees of temporal integration of temporally distinct inputs in CMS/DMN and dlPFC while temporal segregation predominates in visual cortex. Together, we demonstrate a key role of INT-based temporal integration in CMS/DMN and dlPFC including its relation to the brain's uni-transmodal topographical organization in mediating the cognitive bias of our self.

Effects of childhood institutionalization on semantic processing and its neural correlates persist into adolescence and adulthood

Individuals raised in institutionalized care settings are more likely to demonstrate developmental deficits than those raised in biological families. One domain that is vulnerable to the impoverished environments characteristic of some institutionalized care facilities is language development. We used EEG to assess ERPs and source-localized event-related spectral perturbations (ERSPs) associated with semantic processing at different levels of picture-word conflict and low versus high word frequency. Additionally, we assessed behavioral language ability (a synonyms task) and IQ. Participants (N = 454) were adolescents and adults with a history of institutionalized care (N = 187) or raised in biological families (N = 267), both recruited from secondary educational settings to approximately match the groups on age and education. Results indicate that individuals with a history of institutionalization are less accurate at judging whether semantic information in a spoken word matches an image. Additionally, those with a history of institutionalization show reduced cognitive control of conflict and more reactive N400 ERPs and beta ERSPs when handling picture-word conflict, especially in the left hemisphere. Frontal theta is related to semantic and conflict processing; however, in this study it did not vary with institutionalization.

EEG is better left alone

Automated preprocessing methods are critically needed to process the large publicly-available EEG databases, but the optimal approach remains unknown because we lack data quality metrics to compare them. Here, we designed a simple yet robust EEG data quality metric assessing the percentage of significant channels between two experimental conditions within a 100 ms post-stimulus time range. Because of volume conduction in EEG, given no noise, most brain-evoked related potentials (ERP) should be visible on every single channel. Using three publicly available collections of EEG data, we showed that, with the exceptions of high-pass filtering and bad channel interpolation, automated data corrections had no effect on or significantly decreased the percentage of significant channels. Referencing and advanced baseline removal methods were significantly detrimental to performance. Rejecting bad data segments or trials could not compensate for the loss in statistical power. Automated Independent Component Analysis rejection of eyes and muscles failed to increase performance reliably. We compared optimized pipelines for preprocessing EEG data maximizing ERP significance using the leading open-source EEG software: EEGLAB, FieldTrip, MNE, and Brainstorm. Only one pipeline performed significantly better than high-pass filtering the data.

Neurophysiology of sustained attention in early infancy: Investigating longitudinal relations with recognition memory outcomes

The ability to sustain attention is a critical cognitive domain that emerges in infancy and is predictive of a multitude of cognitive processes. Here, we used a heart rate (HR) defined measure of sustained attention to assess corresponding changes in frontal electroencephalography (EEG) power at 3 months of age. Second, we examined how the neural underpinnings of HR-defined sustained attention were associated with sustained attention engagement. Third, we evaluated if neural or behavioral sustained attention measures at 3-months predicted subsequent recognition memory scores at 9 months of age. Seventy-five infants were included at 3 months of age and provided usable attention and EEG data and 25 infants returned to the lab at 9 months and provided usable recognition memory data. The current study focuses on oscillatory power in the theta (4-6 Hz) frequency band during phases of HR-defined sustained attention and inattention phases. Results revealed that theta power was significantly higher during phases of sustained attention. Second, higher theta power during sustained attention was positively associated with proportion of time in sustained attention. Third, longitudinal analyses indicated a significant positive association between theta power during sustained attention on 9-month visual paired comparison scores such that higher theta power predicted higher visual paired comparison scores at 9-months. These results highlight the interrelation of the attention and arousal systems which have longitudinal implications for subsequent recognition memory processes.

Optimization of data pre-processing methods for time-series classification of electroencephalography data

The performance of time-series classification of electroencephalographic data varies strongly across experimental paradigms and study participants. Reasons are task-dependent differences in neuronal processing and seemingly random variations between subjects, amongst others. The effect of data pre-processing techniques to ameliorate these challenges is relatively little studied. Here, the influence of spatial filter optimization methods and non-linear data transformation on time-series classification performance is analyzed by the example of high-frequency somatosensory evoked responses. This is a model paradigm for the analysis of high-frequency electroencephalography data at a very low signal-to-noise ratio, which emphasizes the differences of the explored methods. For the utilized data, it was found that the individual signal-to-noise ratio explained up to 74% of the performance differences between subjects. While data pre-processing was shown to increase average time-series classification performance, it could not fully compensate the signal-to-noise ratio differences between the subjects. This study proposes an algorithm to prototype and benchmark pre-processing pipelines for a paradigm and data set at hand. Extreme learning machines, Random Forest, and Logistic Regression can be used quickly to compare a set of potentially suitable pipelines. For subsequent classification, however, machine learning models were shown to provide better accuracy.

Resting frontal alpha asymmetry as a predictor of executive and affective functioning in children with neurodevelopmental differences

The relative difference of resting EEG frontal alpha activation between left and right hemispheres (FAA; i.e., asymmetry) correlates with global approach and avoidance tendencies. FAA may relate to problems with executive and affective functioning in children with neurodevelopmental differences, including autism and ADHD. We (1) characterize relative left vs. right FAA in autistic, ADHD, and neurotypical children (NT) and (2) investigate whether FAA predicts "hot" executive function or emotion dysregulation. Participants were 97 7- to 11-year-old autistic, ADHD, and NT Children. Children with ADHD displayed greater left (relative to right) FAA compared to autistic and neurotypical children. Children with ADHD displayed greater challenges with "hot" EF on a gambling task than autistic children, whereas children with co-occurring autism and ADHD had greater parent-reported emotion dysregulation than NT and autism-only groups. Greater left FAA predicted worse hot EF for all children but was not significantly related to emotion dysregulation. Regardless of clinical diagnosis, relatively greater left FAA relates to hot EF. While hot EF deficits may be specific to ADHD rather than autism, both together confer additive risk for emotion dysregulation. Future research should explore the functional relation between FAA, reward processing, and affect for children with different EF-related neurodevelopmental differences.

iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG

Motion artifacts hinder source-level analysis of mobile electroencephalography (EEG) data using independent component analysis (ICA). iCanClean is a novel cleaning algorithm that uses reference noise recordings to remove noisy EEG subspaces, but it has not been formally tested in a parameter sweep. The goal of this study was to test iCanClean’s ability to improve the ICA decomposition of EEG data corrupted by walking motion artifacts. Our primary objective was to determine optimal settings and performance in a parameter sweep (varying the window length and r2 cleaning aggressiveness). High-density EEG was recorded with 120 + 120 (dual-layer) EEG electrodes in young adults, high-functioning older adults, and low-functioning older adults. EEG data were decomposed by ICA after basic preprocessing and iCanClean. Components well-localized as dipoles (residual variance < 15%) and with high brain probability (ICLabel > 50%) were marked as ‘good’. We determined iCanClean’s optimal window length and cleaning aggressiveness to be 4-s and r2 = 0.65 for our data. At these settings, iCanClean improved the average number of good components from 8.4 to 13.2 (+57%). Good performance could be maintained with reduced sets of noise channels (12.7, 12.2, and 12.0 good components for 64, 32, and 16 noise channels, respectively). Overall, iCanClean shows promise as an effective method to clean mobile EEG data.

Deep learning-based electroencephalic diagnosis of tinnitus symptom

Tinnitus is a neuropathological phenomenon caused by the recognition of external sound that does not actually exist. Existing diagnostic methods for tinnitus are rather subjective and complicated medical examination procedures. The present study aimed to diagnose tinnitus using deep learning analysis of electroencephalographic (EEG) signals while patients performed auditory cognitive tasks. We found that, during an active oddball task, patients with tinnitus could be identified with an area under the curve of 0.886 through a deep learning model (EEGNet) using EEG signals. Furthermore, using broadband (0.5 to 50 Hz) EEG signals, an analysis of the EEGNet convolutional kernel feature maps revealed that alpha activity might play a crucial role in identifying patients with tinnitus. A subsequent time-frequency analysis of the EEG signals indicated that the tinnitus group had significantly reduced pre-stimulus alpha activity compared with the healthy group. These differences were observed in both the active and passive oddball tasks. Only the target stimuli during the active oddball task yielded significantly higher evoked theta activity in the healthy group compared with the tinnitus group. Our findings suggest that task-relevant EEG features can be considered as a neural signature of tinnitus symptoms and support the feasibility of EEG-based deep-learning approach for the diagnosis of tinnitus.

Exposure to nature is associated with decreased functional connectivity within the distress network: A resting state EEG study

Introduction

Despite the well-established evidence supporting the restorative potential of nature exposure, the neurophysiological underpinnings of the restorative cognitive/emotional effect of nature are not yet fully understood. The main purpose of the current study was to investigate the association between exposure to nature and electroencephalography (EEG) functional connectivity in the distress network.

Methods

Fifty-three individuals (11 men and 42 women; mean age 21.38 ± 1.54 years) were randomly assigned to two groups: (i) a green group and (ii) a gray group. A slideshow consisting of images depicting natural and urban scenarios were, respectively, presented to the green and the gray group. Before and after the slideshow, 5 min resting state (RS) EEG recordings were performed. The exact low-resolution electromagnetic tomography (eLORETA) software was used to execute all EEG analyses.

Results

Compared to the gray group, the green group showed a significant increase in positive emotions (F _{1; 50} = 9.50 p = 0.003) and in the subjective experience of being full of energy and alive (F _{1; 50} = 4.72 p = 0.035). Furthermore, as compared to urban pictures, the exposure to natural images was associated with a decrease of delta functional connectivity in the distress network, specifically between the left insula and left subgenual anterior cingulate cortex (T = -3.70, p = 0.023).

Discussion

Our results would seem to be in accordance with previous neurophysiological studies suggesting that experiencing natural environments is associated with brain functional dynamics linked to emotional restorative processes.

A novel ANN adaptive Riemannian-based kernel classification for motor imagery

More recently, a number of studies show the interest of the use of the Riemannian geometry in EEG classification. The idea is to exploit the EEG covariance matrices, instead of the raw EEG data, and use the Riemannian geometry to directly classify these matrices. This paper presents a novel Artificial Neural Network approach based on an Adaptive Riemannian Kernel, named ARK-ANN, to classify Electroencephalographic (EEG) motor imaging signals in the context of Brain Computer Interface (BCI). A multilayer perceptron is used to classify the covariance matrices of Motor Imagery (MI) signals employing an adaptive optimization of the testing set. The contribution of a geodesic filter is also assessed for the ANN and the original method which uses an SVM classifier. The results demonstrate that the ARK-ANN performs better than the other methods and the geodesic filter gives slightly better results in the ARK-SVM, considered here as the reference method, in the case of inter-subject classification (accuracy of 87.4% and 86% for ARK-ANN and ARK-SVM, respectively). Regarding the cross-subject classification, the proposed method gives an accuracy of 77.3% and increases the precision by 8.2% in comparison to the SVM based method.

Removing ocular artifacts from magnetoencephalographic data on naturalistic reading of continuous texts

Naturalistic reading paradigms and stimuli consisting of long continuous texts are essential for characterizing the cortical basis of reading. Due to the highly dynamic nature of the reading process, electrophysiological brain imaging methods with high spatial and temporal resolution, such as magnetoencephalography (MEG), are ideal for tracking them. However, as electrophysiological recordings are sensitive to electromagnetic artifacts, data recorded during naturalistic reading is confounded by ocular artifacts. In this study, we evaluate two different pipelines for removing ocular artifacts from MEG data collected during continuous, naturalistic reading, with the focus on saccades and blinks. Both pipeline alternatives are based on blind source separation methods but differ fundamentally in their approach. The first alternative is a multi-part process, in which saccades are first extracted by applying Second-Order Blind Identification (SOBI) and, subsequently, FastICA is used to extract blinks. The other alternative uses a single powerful method, Adaptive Mixture ICA (AMICA), to remove all artifact types at once. The pipelines were tested, and their effects compared on MEG data recorded from 13 subjects in a naturalistic reading task where the subjects read texts with the length of multiple pages. Both pipelines performed well, extracting the artifacts in a single component per artifact type in most subjects. Signal power was reduced across the whole cortex in all studied frequency bands from 1 to 90 Hz, but especially in the frontal cortex and temporal pole. The results were largely similar for the two pipelines, with the exception that SOBI-FastICA reduced signal in the right frontal cortex in all studied frequency bands more than AMICA. However, there was considerable interindividual variation in the effects of the pipelines. As a holistic conclusion, we choose to recommend AMICA for removing artifacts from MEG data on naturalistic reading but note that the SOBI-FastICA pipeline has also various favorable characteristics.

HArtMuT-modeling eye and muscle contributors in neuroelectric imaging

Objective.Magneto- and electroencephalography (M/EEG) measurements record a mix of signals from the brain, eyes, and muscles. These signals can be disentangled for artifact cleaning e.g. using spatial filtering techniques. However, correctly localizing and identifying these components relies on head models that so far only take brain sources into account.Approach.We thus developed the Head Artifact Model using Tripoles (HArtMuT). This volume conduction head model extends to the neck and includes brain sources as well as sources representing eyes and muscles that can be modeled as single dipoles, symmetrical dipoles, and tripoles. We compared a HArtMuT four-layer boundary element model (BEM) with the EEGLAB standard head model on their localization accuracy and residual variance (RV) using a HArtMuT finite element model (FEM) as ground truth. We also evaluated the RV on real-world data of mobile participants, comparing different HArtMuT BEM types with the EEGLAB standard head model.Main results.We found that HArtMuT improves localization for all sources, especially non-brain, and localization error and RV of non-brain sources were in the same range as those of brain sources. The best results were achieved by using cortical dipoles, muscular tripoles, and ocular symmetric dipoles, but dipolar sources alone can already lead to convincing results.Significance.We conclude that HArtMuT is well suited for modeling eye and muscle contributions to the M/EEG signal. It can be used to localize sources and to identify brain, eye, and muscle components. HArtMuT is freely available and can be integrated into standard software.

Pathological Slow-Wave Activity and Impaired Working Memory Binding in Post-Traumatic Amnesia

Associative binding is key to normal memory function and is transiently disrupted during periods of post-traumatic amnesia (PTA) following traumatic brain injury (TBI). Electrophysiological abnormalities, including low-frequency activity, are common following TBI. Here, we investigate associative memory binding during PTA and test the hypothesis that misbinding is caused by pathological slowing of brain activity disrupting cortical communication. Thirty acute moderate to severe TBI patients (25 males; 5 females) and 26 healthy controls (20 males; 6 females) were tested with a precision working memory paradigm requiring the association of object and location information. Electrophysiological effects of TBI were assessed using resting-state EEG in a subsample of 17 patients and 21 controls. PTA patients showed abnormalities in working memory function and made significantly more misbinding errors than patients who were not in PTA and controls. The distribution of localization responses was abnormally biased by the locations of nontarget items for patients in PTA, suggesting a specific impairment of object and location binding. Slow-wave activity was increased following TBI. Increases in the δ-α ratio indicative of an increase in low-frequency power specifically correlated with binding impairment in working memory. Connectivity changes in TBI did not correlate with binding impairment. Working memory and electrophysiological abnormalities normalized at 6 month follow-up. These results show that patients in PTA show high rates of misbinding that are associated with a pathological shift toward lower-frequency oscillations.SIGNIFICANCE STATEMENT How do we remember what was where? The mechanism by which information (e.g., object and location) is integrated in working memory is a central question for cognitive neuroscience. Following significant head injury, many patients will experience a period of post-traumatic amnesia (PTA) during which this associative binding is disrupted. This may be because of electrophysiological changes in the brain. Using a precision working memory test and resting-state EEG, we show that PTA patients demonstrate impaired binding ability, and this is associated with a shift toward slower-frequency activity on EEG. Abnormal EEG connectivity was observed but was not specific to PTA or binding ability. These findings contribute to both our mechanistic understanding of working memory binding and PTA pathophysiology.

Extraction of discriminative features from EEG signals of dyslexic children; before and after the treatment

Dyslexia is a neurological disorder manifested as difficulty reading and writing. It can occur despite adequate instruction, intelligence, and intact sensory abilities. Different electroencephalogram (EEG) patterns have been demonstrated between dyslexic and healthy subjects in previous studies. This study focuses on the difference between patients before and after treatment. The main goal is to identify the subset of features that adequately discriminate subjects before and after a specific treatment plan. The treatment consists of Transcranial Direct Current Stimulation (tDCS) and occupational therapy using the BrainWare SAFARI software. The EEG signals of sixteen dyslexic children were recorded during the eyes-closed resting state before and after treatment. The preprocessing step was followed by the extraction of a wide range of features to investigate the differences related to the treatment. An optimal subset of features extracted from recorded EEG signals was determined using Principal Component Analysis (PCA) in conjunction with the Sequential Floating Forward Selection (SFFS) algorithm. The results showed that treatment leads to significant changes in EEG features like spectral and phase-related EEG features, in various regions. It has been demonstrated that the extracted subset of discriminative features can be useful for classification applications in treatment assessment. The most discriminative subset of features could classify the data with an accuracy of 92% with SVM classifier. The above result confirms the efficacy of the treatment plans in improving dyslexic children's cognitive skills.

Classification of Parkinson's disease motor phenotype: a machine learning approach

To assess the cortical activity in people with Parkinson's disease (PwP) with different motor phenotype (tremor-dominant-TD and postural instability and gait difficulty-PIGD) and to compare with controls. Twenty-four PwP (during OFF and ON medication) and twelve age-/sex-/handedness-matched healthy controls underwent electrophysiological assessment of spectral ratio analysis through electroencephalography (EEG) at resting state and during the hand movement. We performed a machine learning method with 35 attributes extracted from EEG. To verify the efficiency of the proposed phenotype-based EEG classification the random forest and random tree were tested (performed 30 times, using a tenfolds cross validation in Weka environment). The analyses based on phenotypes indicated a slowing down of cortical activity during OFF medication state in PwP. PD with TD phenotype presented this characteristic at resting and the individuals with PIGD presented during the hand movement. During the ON state, there is no difference between phenotypes at resting nor during the hand movement. PD phenotypes may influence spectral activity measured by EEG. Random forest machine learning provides a slightly more accurate, sensible and specific approach to distinguish different PD phenotypes. The phenotype of PD might be a clinical characteristic that could influence cortical activity.

The low glutamate diet improves cognitive functioning in veterans with Gulf War Illness and resting-state EEG potentially predicts response

Objectives: Gulf War Illness (GWI) is a chronic, multi-symptom disorder with underlying central nervous system dysfunction and cognitive impairments. The objective of this study was to test the low glutamate diet as a novel treatment for cognitive dysfunction among those with GWI, and to explore if baseline resting-state electroencephalography (EEG) could predict cognitive outcomes.Methods: Cognitive functioning was assessed at baseline, after one-month on the diet, and across a two-week double-blind, placebo-controlled crossover challenge with monosodium glutamate (MSG) relative to placebo.Results: Significant improvements were seen after one-month on the diet in overall cognitive functioning, and in all other domains tested (FDR p < 0.05), except for memory. Challenge with MSG resulted in significant inter-individual response variability (p < 0.0001). Participants were clustered according to baseline resting-state EEG using k-means clustering to explore the inter-individual response variability. Three distinct EEG clusters were observed, and each corresponded with differential cognitive effects during challenge with MSG: cluster 1 had cognitive benefit (24% of participants), cluster 2 had cognitive detriment (42% of participants), and cluster 3 had mild/mixed effects (33% of participants).Discussion: These findings suggest that the low glutamate diet may be a beneficial treatment for cognitive impairment in GWI. Future research is needed to understand the extent to which resting-state EEG can predict response to the low glutamate diet and to explore the mechanisms behind the varied response to acute glutamate challenge.

Psychedelics and schizophrenia: Distinct alterations to Bayesian inference

Schizophrenia and states induced by certain psychotomimetic drugs may share some physiological and phenomenological properties, but they differ in fundamental ways: one is a crippling chronic mental disease, while the others are temporary, pharmacologically-induced states presently being explored as treatments for mental illnesses. Building towards a deeper understanding of these different alterations of normal consciousness, here we compare the changes in neural dynamics induced by LSD and ketamine (in healthy volunteers) against those associated with schizophrenia, as observed in resting-state M/EEG recordings. While both conditions exhibit increased neural signal diversity, our findings reveal that this is accompanied by an increased transfer entropy from the front to the back of the brain in schizophrenia, versus an overall reduction under the two drugs. Furthermore, we show that these effects can be reproduced via different alterations of standard Bayesian inference applied on a computational model based on the predictive processing framework. In particular, the effects observed under the drugs are modelled as a reduction of the precision of the priors, while the effects of schizophrenia correspond to an increased precision of sensory information. These findings shed new light on the similarities and differences between schizophrenia and two psychotomimetic drug states, and have potential implications for the study of consciousness and future mental health treatments.

Hemodynamic functional connectivity optimization of frequency EEG microstates enables attention LSTM framework to classify distinct temporal cortical communications of different cognitive tasks

Temporal analysis of global cortical communication of cognitive tasks in coarse EEG information is still challenging due to the underlying complex neural mechanisms. This study proposes an attention-based time-series deep learning framework that processes fMRI functional connectivity optimized quasi-stable frequency microstates for classifying distinct temporal cortical communications of the cognitive task. Seventy volunteers were subjected to visual target detection tasks, and their electroencephalogram (EEG) and functional MRI (fMRI) were acquired simultaneously. At first, the acquired EEG information was preprocessed and bandpass to delta, theta, alpha, beta, and gamma bands and then subjected to quasi-stable frequency-microstate estimation. Subsequently, time-series elicitation of each frequency microstates is optimized with graph theory measures of simultaneously eliciting fMRI functional connectivity between frontal, parietal, and temporal cortices. The distinct neural mechanisms associated with each optimized frequency-microstate were analyzed using microstate-informed fMRI. Finally, these optimized, quasi-stable frequency microstates were employed to train and validate the attention-based Long Short-Term Memory (LSTM) time-series architecture for classifying distinct temporal cortical communications of the target from other cognitive tasks. The temporal, sliding input sampling windows were chosen between 180 to 750 ms/segment based on the stability of transition probabilities of the optimized microstates. The results revealed 12 distinct frequency microstates capable of deciphering target detections' temporal cortical communications from other task engagements. Particularly, fMRI functional connectivity measures of target engagement were observed significantly correlated with the right-diagonal delta (r = 0.31), anterior-posterior theta (r = 0.35), left-right theta (r = - 0.32), alpha (r = - 0.31) microstates. Further, neuro-vascular information of microstate-informed fMRI analysis revealed the association of delta/theta and alpha/beta microstates with cortical communications and local neural processing, respectively. The classification accuracies of the attention-based LSTM were higher than the traditional LSTM architectures, particularly the frameworks that sampled the EEG data with a temporal width of 300 ms/segment. In conclusion, the study demonstrates reliable temporal classifications of global cortical communication of distinct tasks using an attention-based LSTM utilizing fMRI functional connectivity optimized quasi-stable frequency microstates.

The HAPPE plus Event-Related (HAPPE+ER) software: A standardized preprocessing pipeline for event-related potential analyses

Event-Related Potential (ERP) designs are a common method for interrogating neurocognitive function with electroencephalography (EEG). However, the traditional method of preprocessing ERP data is manual-editing - a subjective, time-consuming processes. A number of automated pipelines have recently been created to address the need for standardization, automation, and quantification of EEG data pre-processing; however, few are optimized for ERP analyses (especially in developmental or clinical populations). We propose and validate the HAPPE plus Event-Related (HAPPE+ER) software, a standardized and automated pre-processing pipeline optimized for ERP analyses across the lifespan. HAPPE+ER processes event-related potential data from raw files through preprocessing and generation of event-related potentials for statistical analyses. HAPPE+ER also includes post-processing reports of both data quality and pipeline quality metrics to facilitate the evaluation and reporting of data processing in a standardized manner. Finally, HAPPE+ER includes post-processing scripts to facilitate validating HAPPE+ER performance and/or comparing to performance of other preprocessing pipelines in users' own data via simulated ERPs. We describe multiple approaches with simulated and real ERP data to optimize pipeline performance and compare to other methods and pipelines. HAPPE+ER software is freely available under the terms of GNU General Public License at https://www.gnu.org/licenses/#GPL.

Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF)

Electroencephalogram (EEG) data are typically affected by artifacts. The detection and removal of bad channels (i.e., with poor signal-to-noise ratio) is a crucial initial step. EEG data acquired from different populations require different cleaning strategies due to the inherent differences in the data quality, the artifacts' nature, and the employed experimental paradigm. To deal with such differences, we propose a robust EEG bad channel detection method based on the Local Outlier Factor (LOF) algorithm. Unlike most existing bad channel detection algorithms that look for the global distribution of channels, LOF identifies bad channels relative to the local cluster of channels, which makes it adaptable to any kind of EEG. To test the performance and versatility of the proposed algorithm, we validated it on EEG acquired from three populations (newborns, infants, and adults) and using two experimental paradigms (event-related and frequency-tagging). We found that LOF can be applied to all kinds of EEG data after calibrating its main hyperparameter: the LOF threshold. We benchmarked the performance of our approach with the existing state-of-the-art (SoA) bad channel detection methods. We found that LOF outperforms all of them by improving the F1 Score, our chosen performance metric, by about 40% for newborns and infants and 87.5% for adults.

Brain functional connectivity analysis in patients with relapsing-remitting multiple sclerosis: A graph theory approach of EEG resting state

Multiple sclerosis (MS) is an autoimmune disease related to the central nervous system (CNS). This study aims to investigate the effects of MS on the brain's functional connectivity network using the electroencephalogram (EEG) resting-state signals and graph theory approach. Resting-state eyes-closed EEG signals were recorded from 20 patients with relapsing-remitting MS (RRMS) and 18 healthy cases. In this study, the prime objective is to calculate the connectivity between EEG channels to assess the differences in brain functional network global features. The results demonstrated lower cortical activity in the alpha frequency bands and higher activity for the gamma frequency bands in patients with RRMS compared to the healthy group. In this study, graph metric calculations revealed a significant difference in the diameter of the functional brain network based on the directed transfer function (DTF) measure between the two groups, indicating a higher diameter in RRMS cases for the alpha frequency band. A higher diameter for the functional brain network in MS cases can result from anatomical damage. In addition, considerable differences between the networks' global efficiency and transitivity based on the imaginary part of the coherence (iCoh) measure were observed, indicating higher global efficiency and transitivity in the delta, theta, and beta frequency bands for RRMS cases, which can be related to the compensatory functional reaction from the brain. This study indicated that in RRMS cases, some of the global characteristics of the brain's functional network, such as diameter and global efficiency, change and can be illustrated even in the resting-state condition when the brain is not under cognitive load.

ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals

Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML's popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository.

IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal

Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative to develop a practical and reliable artifact removal method to prevent the misinterpretation of neural signals and the underperformance of brain-computer interfaces. Based on the U-Net architecture, we developed a new artifact removal model, IC-U-Net, for removing pervasive EEG artifacts and reconstructing brain signals. IC-U-Net was trained using mixtures of brain and non-brain components decomposed by independent component analysis. It uses an ensemble of loss functions to model complex signal fluctuations in EEG recordings. The effectiveness of the proposed method in recovering brain activities and removing various artifacts (e.g., eye blinks/movements, muscle activities, and line/channel noise) was demonstrated in a simulation study and four real-world EEG experiments. IC-U-Net can reconstruct a multi-channel EEG signal and is applicable to most artifact types, offering a promising end-to-end solution for automatically removing artifacts from EEG recordings. It also meets the increasing need to image natural brain dynamics in a mobile setting. The code and pre-trained IC-U-Net model are available at https://github.com/roseDwayane/AIEEG.

EPIC: Annotated epileptic EEG independent components for artifact reduction

Scalp electroencephalogram is a non-invasive multi-channel biosignal that records the brain’s electrical activity. It is highly susceptible to noise that might overshadow important data. Independent component analysis is one of the most used artifact removal methods. Independent component analysis separates data into different components, although it can not automatically reject the noisy ones. Therefore, experts are needed to decide which components must be removed before reconstructing the data. To automate this method, researchers have developed classifiers to identify noisy components. However, to build these classifiers, they need annotated data. Manually classifying independent components is a time-consuming task. Furthermore, few labelled data are publicly available. This paper presents a source of annotated electroencephalogram independent components acquired from patients with epilepsy (EPIC Dataset). This dataset contains 77,426 independent components obtained from approximately 613 hours of electroencephalogram, visually inspected by two experts, which was already successfully utilised to develop independent component classifiers.

Measurement(s)	Electroencephalogram Independent Components
Technology Type(s)	MATLAB and Python

Automatic Diagnosis of Mild Cognitive Impairment Based on Spectral, Functional Connectivity, and Nonlinear EEG-Based Features

Accurate and early diagnosis of mild cognitive impairment (MCI) is necessary to prevent the progress of Alzheimer's and other kinds of dementia. Unfortunately, the symptoms of MCI are complicated and may often be misinterpreted as those associated with the normal ageing process. To address this issue, many studies have proposed application of machine learning techniques for early MCI diagnosis based on electroencephalography (EEG). In this study, a machine learning framework for MCI diagnosis is proposed in this study, which extracts spectral, functional connectivity, and nonlinear features from EEG signals. The sequential backward feature selection (SBFS) algorithm is used to select the best subset of features. Several classification models and different combinations of feature sets are measured to identify the best ones for the proposed framework. A dataset of 16 and 18 EEG data of normal and MCI subjects is used to validate the proposed system. Metrics including accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR) are evaluated using 10-fold crossvalidation. An average AC of 99.4%, SE of 98.8%, SP of 100%, F1 of 99.4%, and FDR of 0% have been provided by the best performance of the proposed framework using the linear support vector machine (LSVM) classifier and the combination of all feature sets. The acquired results confirm that the proposed framework provides an accurate and robust performance for recognizing MCI cases and outperforms previous approaches. Based on the obtained results, it is possible to be developed in order to use as a computer-aided diagnosis (CAD) tool for clinical purposes.

Peak frequency of the sensorimotor mu rhythm varies with autism-spectrum traits

Autism spectrum disorder (ASD) is a neurodevelopmental syndrome characterized by impairments in social perception and communication. Growing evidence suggests that the relationship between deficits in social perception and ASD may extend into the neurotypical population. In electroencephalography (EEG), high autism-spectrum traits in both ASD and neurotypical samples are associated with changes to the mu rhythm, an alpha-band (8–12 Hz) oscillation measured over sensorimotor cortex which typically shows reductions in spectral power during both one’s own movements and observation of others’ actions. This mu suppression is thought to reflect integration of perceptual and motor representations for understanding of others’ mental states, which may be disrupted in individuals with autism-spectrum traits. However, because spectral power is usually quantified at the group level, it has limited usefulness for characterizing individual variation in the mu rhythm, particularly with respect to autism-spectrum traits. Instead, individual peak frequency may provide a better measure of mu rhythm variability across participants. Previous developmental studies have linked ASD to slowing of individual peak frequency in the alpha band, or peak alpha frequency (PAF), predominantly associated with selective attention. Yet individual variability in the peak mu frequency (PMF) remains largely unexplored, particularly with respect to autism-spectrum traits. Here we quantified peak frequency of occipitoparietal alpha and sensorimotor mu rhythms across neurotypical individuals as a function of autism-spectrum traits. High-density 128-channel EEG data were collected from 60 participants while they completed two tasks previously reported to reliably index the sensorimotor mu rhythm: motor execution (bimanual finger tapping) and action observation (viewing of whole-body human movements). We found that individual measurement in the peak oscillatory frequency of the mu rhythm was highly reliable within participants, was not driven by resting vs. task states, and showed good correlation across action execution and observation tasks. Within our neurotypical sample, higher autism-spectrum traits were associated with slowing of the PMF, as predicted. This effect was not likely explained by volume conduction of the occipitoparietal PAF associated with attention. Together, these data support individual peak oscillatory alpha-band frequency as a correlate of autism-spectrum traits, warranting further research with larger samples and clinical populations.

Automaticity of Early Sexual Attention: An Event-Related Potential Study

A promising line of research on forensic assessment of paraphilic sexual interest focuses on behavioral measures of visual attention using sexual stimuli as distractors. The present study combined event-related potentials (ERPs) with behavioral measures to investigate whether detection of a hidden sexual preference can be improved with ERPs. Normal variants of sexual orientation were used for a proof-of-concept investigation. Accordingly, 40 heterosexual and 40 gay men participated in the study. Within each group, half of the participants were instructed to hide their sexual orientation. The results showed that a match between sexual orientation and stimulus delays responses and influences ERP before motor responses. Late ERP components showed higher potential in differentiating hidden sexual preferences than motor responses, thereby showing how ERPs can be used in combination with reaction time measures to potentially facilitate the detection of hidden sexual preferences.

Electroencephalographic Microstates as Novel Functional Biomarkers for Adult Attention-Deficit/Hyperactivity Disorder

BACKGROUND

Research on the electroencephalographic (EEG) signatures of attention-deficit/hyperactivity disorder (ADHD) has historically concentrated on its frequency spectrum or event-related evoked potentials. In this work, we investigate EEG microstates (MSs), an alternative framework defined by the clustering of recurring topographical patterns, as a novel approach for examining large-scale cortical dynamics in ADHD.

METHODS

Using k-means clustering, we studied the spatiotemporal dynamics of ADHD during the rest condition by comparing the MS segmentations between adult patients with ADHD and neurotypical control subjects across two independent datasets: the first dataset consisted of 66 patients with ADHD and 66 control subjects, and the second dataset comprised 22 patients with ADHD and 22 control subjects and was used for out-of-sample validation.

RESULTS

Spatially, patients with ADHD and control subjects displayed equivalent MS topographies (canonical maps), indicating the preservation of prototypical EEG generators in patients with ADHD. However, this concordance was accompanied by significant differences in temporal dynamics. At the group level, and across both datasets, ADHD diagnosis was associated with longer mean durations of a frontocentral topography (MS D), indicating that its electrocortical generator(s) could be acting as pronounced attractors of global cortical dynamics. In addition, its spatiotemporal metrics were correlated with sleep disturbance, the latter being known to have a strong relationship with ADHD. Finally, in the first (larger) dataset, we also found evidence of decreased time coverage and mean duration of a left-right diagonal topography (MS A), which inversely correlated with ADHD scores.

CONCLUSIONS

Overall, our study underlines the value of EEG MSs as promising functional biomarkers for ADHD, offering an additional lens through which to examine its neurophysiological mechanisms.

Standardizing continuous data classifications in a virtual T-maze using two-layer feedforward networks

There continues to be difficulties when it comes to replication of studies in the field of Psychology. In part, this may be caused by insufficiently standardized analysis methods that may be subject to state dependent variations in performance. In this work, we show how to easily adapt the two-layer feedforward neural network architecture provided by Huang1 to a behavioral classification problem as well as a physiological classification problem which would not be solvable in a standardized way using classical regression or "simple rule" approaches. In addition, we provide an example for a new research paradigm along with this standardized analysis method. This paradigm as well as the analysis method can be adjusted to any necessary modification or applied to other paradigms or research questions. Hence, we wanted to show that two-layer feedforward neural networks can be used to increase standardization as well as replicability and illustrate this with examples based on a virtual T-maze paradigm2-5 including free virtual movement via joystick and advanced physiological data signal processing.

Separating the aperiodic and periodic components of neural activity in Parkinson's disease

Most studies on electrophysiology have not separated aperiodic activity from the spectra but have rather evaluated a combined periodic oscillatory component and the aperiodic component. As the understanding of aperiodic activity gradually deepens, its potential physiological significance has acquired increased appreciation. Herein, we investigated the two components in scalp electroencephalogram in 16 healthy controls and 15 patients with Parkinson's disease (PD); the results revealed that aperiodic parameters were approximately symmetrically distributed in topography in patients with PD and were significantly modulated by dopaminergic medication in channels C4, C3, CP5 and FC5. In sum, our findings might provide indicators for evaluating treatment response in PD and highlight the importance of re-evaluating the neuronal power spectra parameterization.