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

Excitatory-inhibitory balance within EEG microstates and resting-state fMRI networks: assessed via simultaneous trimodal PET-MR-EEG imaging

The symbiosis of neuronal activities and glucose energy metabolism is reflected in the generation of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) signals. However, their association with the balance between neuronal excitation and inhibition (E/I-B), which is closely related to the activities of glutamate and γ-aminobutyric acid (GABA) and the receptor availability (RA) of GABAA and mGluR5, remains unexplored. This research investigates these associations during the resting state (RS) condition using simultaneously recorded PET/MR/EEG (trimodal) data. The trimodal data were acquired from three studies using different radio-tracers such as, [11C]ABP688 (ABP) (N = 9), [11C]Flumazenil (FMZ) (N = 10) and 2-[18F]fluoro-2-deoxy-D-glucose (FDG) (N = 10) targeted to study the mGluR5, GABAA receptors and glucose metabolism respectively. Glucose metabolism and neuroreceptor binding availability (non-displaceable binding potential (BPND)) of GABAA and mGluR5 were found to be significantly higher and closely linked within core resting-state networks (RSNs). The neuronal generators of EEG microstates and the fMRI measures were most tightly associated with the BPND of GABAA relative to mGluR5 BPND and the glucose metabolism, emphasising a predominance of inhibitory processes within in the core RSNs at rest. Changes in the neuroreceptors leading to an altered coupling with glucose metabolism may render the RSNs vulnerable to psychiatric conditions. The paradigm employed here will likely help identify the precise neurobiological mechanisms behind these alterations in fMRI functional connectivity and EEG oscillations, potentially benefitting individualised healthcare treatment measures.

Is Brain Dynamics Preserved in the EEG After Automated Artifact Removal? A Validation of the Fingerprint Method and the Automatic Removal of Cardiac Interference Approach Based on Microstate Analysis

The assessment of a method for removing artifacts from electroencephalography (EEG) datasets often disregard verifying that global brain dynamics is preserved. In this study, we verified that the recently introduced optimized fingerprint method and the automatic removal of cardiac interference (ARCI) approach not only remove physiological artifacts from EEG recordings but also preserve global brain dynamics, as assessed with a new approach based on microstate analysis. We recorded EEG activity with a high-resolution EEG system during two resting-state conditions (eyes open, 25 volunteers, and eyes closed, 26 volunteers) known to exhibit different brain dynamics. After signal decomposition by independent component analysis (ICA), the independent components (ICs) related to eyeblinks, eye movements, myogenic interference, and cardiac electromechanical activity were identified with the optimized fingerprint method and ARCI approach and statistically compared with the outcome of the expert classification of the ICs by visual inspection. Brain dynamics in two different groups of denoised EEG signals, reconstructed after having removed the artifactual ICs identified by either visual inspection or the automated methods, was assessed by calculating microstate topographies, microstate metrics (duration, occurrence, and coverage), and directional predominance (based on transition probabilities). No statistically significant differences between the expert and the automated classification of the artifactual ICs were found (p > 0.05). Cronbach’s α values assessed the high test–retest reliability of microstate parameters for EEG datasets denoised by the automated procedure. The total EEG signal variance explained by the sets of global microstate templates was about 80% for all denoised EEG datasets, with no significant differences between groups. For the differently denoised EEG datasets in the two recording conditions, we found that the global microstate templates and the sequences of global microstates were very similar (p < 0.01). Descriptive statistics and Cronbach’s α of microstate metrics highlighted no significant differences and excellent consistency between groups (p > 0.5). These results confirm the ability of the optimized fingerprint method and the ARCI approach to effectively remove physiological artifacts from EEG recordings while preserving global brain dynamics. They also suggest that microstate analysis could represent a novel approach for assessing the ability of an EEG denoising method to remove artifacts without altering brain dynamics.

Effects of a five-day HD-tDCS application to the right IFG depend on current intensity: A study in children and adolescents with ADHD

Impaired executive functions in ADHD are associated with hypoactivity of the right inferior frontal gyrus (IFG). This region was targeted via repetitive applications of anodal, high-definition transcranial direct current simulation (HD-tDCS) on five consecutive days in 33 ADHD patients (10-17years) and in a healthy control group (n=13, only sham). Patients received either sham (n=13) or verum tDCS with 0.5mA (n=9) or 0.25mA (n=11) depending on individual cutaneous sensitivity. During stimulation, participants performed a combined working memory and response inhibition paradigm (n-back/nogo). At baseline, post, and a 4-month follow up, electroencephalography was recorded during this task. Moreover, interference control (flanker task) and spatial working memory (spanboard task) were assessed to explore possible transfer effects. Omission errors and reaction time variability in all tasks served as measures of attention. In the 0.25mA group increased nogo commission errors indicated a detrimental tDCS effect on response inhibition. After the 5-day stimulation, attentional improvements in the 0.5mA group were indicated by reduced omission errors and reaction time variability. Variability improvements were still evident at follow up. In all groups, nogo P3 amplitudes were reduced post-stimulation, but in the 0.5mA group this reduction was smaller than in the 0.25mA group. Results of the current study suggest distinct effects of tDCS with different current intensities demonstrating the importance of a deeper understanding on the impact of stimulation parameters and repeated tDCS applications to develop effective tDCS-based therapy approaches in ADHD.

Neural oscillations reveal differences in the process of word learning among school-aged children from lower socioeconomic status backgrounds

Building a robust vocabulary in grade school is essential for academic success. Children from lower socioeconomic status (SES) households on average perform below their higher SES peers on word learning tasks, negatively impacting their vocabulary; however, significant variability exists within this group. Many children from low SES homes perform as well as, or better than, their higher SES peers on measures of word learning. The current study addresses what processes underlie this variability, by comparing the neural oscillations of 44 better versus worse word learners (ages 8-15 years) from lower SES households as they infer the meaning of unknown words. Better word learners demonstrated increases in theta and beta power as a word was learned, whereas worse word learners exhibited decreases in alpha power. These group differences in neural oscillatory engagement during word learning indicate there may be different strategies employed based on differences in children's skills. Notably, children with greater vocabulary knowledge are more likely to exhibit larger beta increases; a strategy which is associated with better word learning. This sheds new light on the mechanisms that support word learning in children from low SES households.

The dynamic modular fingerprints of the human brain at rest

The human brain is a dynamic modular network that can be decomposed into a set of modules, and its activity changes continually over time. At rest, several brain networks, known as Resting-State Networks (RSNs), emerge and cross-communicate even at sub-second temporal scale. Here, we seek to decipher the fast reshaping in spontaneous brain modularity and its relationships with RSNs. We use Electro/Magneto-Encephalography (EEG/MEG) to track the dynamics of modular brain networks, in three independent datasets (N = 568) of healthy subjects at rest. We show the presence of strikingly consistent RSNs, and a splitting phenomenon of some of these networks, especially the default mode network, visual, temporal and dorsal attentional networks. We also demonstrate that between-subjects variability in mental imagery is associated with the temporal characteristics of specific modules, particularly the visual network. Taken together, our findings show that large-scale electrophysiological networks have modularity-dependent dynamic fingerprints at rest.

Cortical processing of distracting speech in noisy auditory scenes depends on perceptual demand

Selective attention is essential for the processing of multi-speaker auditory scenes because they require the perceptual segregation of the relevant speech ("target") from irrelevant speech ("distractors"). For simple sounds, it has been suggested that the processing of multiple distractor sounds depends on bottom-up factors affecting task performance. However, it remains unclear whether such dependency applies to naturalistic multi-speaker auditory scenes. In this study, we tested the hypothesis that increased perceptual demand (the processing requirement posed by the scene to separate the target speech) reduces the cortical processing of distractor speech thus decreasing their perceptual segregation. Human participants were presented with auditory scenes including three speakers and asked to selectively attend to one speaker while their EEG was acquired. The perceptual demand of this selective listening task was varied by introducing an auditory cue (interaural time differences, ITDs) for segregating the target from the distractor speakers, while acoustic differences between the distractors were matched in ITD and loudness. We obtained a quantitative measure of the cortical segregation of distractor speakers by assessing the difference in how accurately speech-envelope following EEG responses could be predicted by models of averaged distractor speech versus models of individual distractor speech. In agreement with our hypothesis, results show that interaural segregation cues led to improved behavioral word-recognition performance and stronger cortical segregation of the distractor speakers. The neural effect was strongest in the δ-band and at early delays (0 - 200 ms). Our results indicate that during low perceptual demand, the human cortex represents individual distractor speech signals as more segregated. This suggests that, in addition to purely acoustical properties, the cortical processing of distractor speakers depends on factors like perceptual demand.

What Can Neuromarketing Tell Us about Food Packaging?

Packaging is a powerful tool for brands, which can not only catch consumers' attention but also influence their purchase decisions. The application of neuromarketing techniques to the study of food packaging has recently gained considerable popularity both in academia and practice, but there are still some concerns about the methods and metrics commercially offered and the interpretation of their findings. This represents the motivation of this investigation, whose objective is twofold: (1) to analyze the methodologies and measurements commonly used in neuromarketing commercial research on packaging, and (2) to examine the extent to which the results of food packaging studies applying neuromarketing techniques can be reproduced under similar methodologies. Obtained results shed light on the application of neuromarketing techniques in the evaluation of food packaging and reveal that neuromarketing and declarative methodologies are complementary, and its combination may strengthen the studies' results. Additionally, this study highlights the importance of having a framework that improves the validity and reliability of neuromarketing studies to eradicate mistrust toward the discipline and provide brands with valuable insights into food packing design.

Cross-Frequency Power-Power Coupling Analysis: A Useful Cross-Frequency Measure to Classify ICA-Decomposed EEG

Magneto-/Electro-encephalography (M/EEG) commonly uses (fast) Fourier transformation to compute power spectral density (PSD). However, the resulting PSD plot lacks temporal information, making interpretation sometimes equivocal. For example, consider two different PSDs: a central parietal EEG PSD with twin peaks at 10 Hz and 20 Hz and a central parietal PSD with twin peaks at 10 Hz and 50 Hz. We can assume the first PSD shows a mu rhythm and the second harmonic; however, the latter PSD likely shows an alpha peak and an independent line noise. Without prior knowledge, however, the PSD alone cannot distinguish between the two cases. To address this limitation of PSD, we propose using cross-frequency power-power coupling (PPC) as a post-processing of independent component (IC) analysis (ICA) to distinguish brain components from muscle and environmental artifact sources. We conclude that post-ICA PPC analysis could serve as a new data-driven EEG classifier in M/EEG studies. For the reader's convenience, we offer a brief literature overview on the disparate use of PPC. The proposed cross-frequency power-power coupling analysis toolbox (PowPowCAT) is a free, open-source toolbox, which works as an EEGLAB extension.

Physiological Synchrony in EEG, Electrodermal Activity and Heart Rate Detects Attentionally Relevant Events in Time

Interpersonal physiological synchrony (PS), or the similarity of physiological signals between individuals over time, may be used to detect attentionally engaging moments in time. We here investigated whether PS in the electroencephalogram (EEG), electrodermal activity (EDA), heart rate and a multimodal metric signals the occurrence of attentionally relevant events in time in two groups of participants. Both groups were presented with the same auditory stimulus, but were instructed to attend either to the narrative of an audiobook (audiobook-attending: AA group) or to interspersed emotional sounds and beeps (stimulus-attending: SA group). We hypothesized that emotional sounds could be detected in both groups as they are expected to draw attention involuntarily, in a bottom-up fashion. Indeed, we found this to be the case for PS in EDA or the multimodal metric. Beeps, that are expected to be only relevant due to specific "top-down" attentional instructions, could indeed only be detected using PS among SA participants, for EDA, EEG and the multimodal metric. We further hypothesized that moments in the audiobook accompanied by high PS in either EEG, EDA, heart rate or the multimodal metric for AA participants would be rated as more engaging by an independent group of participants compared to moments corresponding to low PS. This hypothesis was not supported. Our results show that PS can support the detection of attentionally engaging events over time. Currently, the relation between PS and engagement is only established for well-defined, interspersed stimuli, whereas the relation between PS and a more abstract self-reported metric of engagement over time has not been established. As the relation between PS and engagement is dependent on event type and physiological measure, we suggest to choose a measure matching with the stimulus of interest. When the stimulus type is unknown, a multimodal metric is most robust.

A dimensional investigation of error-related negativity (ERN) and self-reported psychiatric symptoms

Alterations in error processing are implicated in a range of DSM-defined psychiatric disorders. For instance, obsessive-compulsive disorder (OCD) and generalised anxiety disorder show enhanced electrophysiological responses to errors-i.e. error-related negativity (ERN)-while others like schizophrenia have an attenuated ERN. However, as diagnostic categories in psychiatry are heterogeneous and also highly intercorrelated, the precise mapping of ERN enhancements/impairments is unclear. To address this, we recorded electroencephalograms (EEG) from 196 participants who performed the Flanker task and collected scores on 9 questionnaires assessing psychiatric symptoms to test if a dimensional framework could reveal specific transdiagnostic clinical manifestations of error processing dysfunctions. Contrary to our hypothesis, we found non-significant associations between ERN amplitude and symptom severity of OCD, trait anxiety, depression, social anxiety, impulsivity, eating disorders, alcohol addiction, schizotypy and apathy. A transdiagnostic approach did nothing to improve signal; there were non-significant associations between all three transdiagnostic dimensions (anxious-depression, compulsive behaviour and intrusive thought, and social withdrawal) and ERN magnitude. In these same individuals, we replicated a previously published transdiagnostic association between goal-directed learning and compulsive behaviour and intrusive thought. Possible explanations discussed are (i) that associations between the ERN and psychopathology might be smaller than previously assumed, (ii) that these associations might depend on a greater level of symptom severity than other transdiagnostic cognitive biomarkers, or (iii) that task parameters, such as the ratio of compatible to incompatible trials, might be crucial for ensuring the sensitivity of the ERN to clinical phenomena.

An EEG channel selection method for motor imagery based brain-computer interface and neurofeedback using Granger causality

Motor imagery (MI) brain-computer interface (BCI) and neurofeedback (NF) with electroencephalogram (EEG) signals are commonly used for motor function improvement in healthy subjects and to restore neurological functions in stroke patients. Generally, in order to decrease noisy and redundant information in unrelated EEG channels, channel selection methods are used which provide feasible BCI and NF implementations with better performances. Our assumption is that there are causal interactions between the channels of EEG signal in MI tasks that are repeated in different trials of a BCI and NF experiment. Therefore, a novel method for EEG channel selection is proposed which is based on Granger causality (GC) analysis. Additionally, the machine-learning approach is used to cluster independent component analysis (ICA) components of the EEG signal into artifact and normal EEG clusters. After channel selection, using the common spatial pattern (CSP) and regularized CSP (RCSP), features are extracted and with the k-nearest neighbor (k-NN), support vector machine (SVM) and linear discriminant analysis (LDA) classifiers, MI tasks are classified into left and right hand MI. The goal of this study is to achieve a method resulting in lower EEG channels with higher classification performance in MI-based BCI and NF by causal constraint. The proposed method based on GC, with only eight selected channels, results in 93.03% accuracy, 92.93% sensitivity, and 93.12% specificity, with RCSP feature extractor and best classifier for each subject, after being applied on Physionet MI dataset, which is increased by 3.95%, 3.73%, and 4.13%, in comparison with correlation-based channel selection method.

Comparison of two tDCS protocols on pain and EEG alpha-2 oscillations in women with fibromyalgia

Transcranial Direct Current Stimulation (tDCS) has been used as an alternative treatment for pain reduction in fibromyalgia. In this study, in addition to behavioral measures, we analyzed oscillations in alpha 2 frequency band in the frontal, occipital, and parietal regions, in response to the application of two neuromodulation protocols in fibromyalgia. The study was a randomized, double-blind, placebo-controlled clinical trial with 31 women diagnosed with fibromyalgia. The participants were allocated to three groups with the anodic stimulation applied on the left motor cortex: Group 1, for five consecutive days; Group 2, for 10 consecutive days; and Group 3, sham stimulation for five consecutive days. Statistical analysis showed a reduction in pain intensity after treatment for groups in general [F (1.28) = 8.02; p = 0.008; η2 = 0.223], in addition to a reduction in alpha 2 in the frontal (p = 0.039; d = 0.384) and parietal (p = 0.021; d = 0.520) regions after the treatment on five consecutive days. We conclude that neuromodulation protocols produced similar effects on pain reduction, but differed with respect to the changes in the alpha 2 frequency band in the frontal and parietal regions.

Temporal integration as "common currency" of brain and self-scale-free activity in resting-state EEG correlates with temporal delay effects on self-relatedness

The self is a multifaceted phenomenon that integrates information and experience across multiple time scales. How temporal integration on the psychological level of the self is related to temporal integration on the neuronal level remains unclear. To investigate temporal integration on the psychological level, we modified a well-established self-matching paradigm by inserting temporal delays. On the neuronal level, we indexed temporal integration in resting-state EEG by two related measures of scale-free dynamics, the power law exponent and autocorrelation window. We hypothesized that the previously established self-prioritization effect, measured as decreased response times or increased accuracy for self-related stimuli, would change with the insertion of different temporal delays between the paired stimuli, and that these changes would be related to temporal integration on the neuronal level. We found a significant self-prioritization effect on accuracy in all conditions with delays, indicating stronger temporal integration of self-related stimuli. Further, we observed a relationship between temporal integration on psychological and neuronal levels: higher degrees of neuronal integration, that is, higher power-law exponent and longer autocorrelation window, during resting-state EEG were related to a stronger increase in the self-prioritization effect across longer temporal delays. We conclude that temporal integration on the neuronal level serves as a template for temporal integration of the self on the psychological level. Temporal integration can thus be conceived as the "common currency" of neuronal and psychological levels of self.

EEG Integrated Platform Lossless (EEG-IP-L) pre-processing pipeline for objective signal quality assessment incorporating data annotation and blind source separation

BACKGROUND

The methods available for pre-processing EEG data are rapidly evolving as researchers gain access to vast computational resources; however, the field currently lacks a set of standardized approaches for data characterization, efficient interactive quality control review procedures, and large-scale automated processing that is compatible with High Performance Computing (HPC) resources.

NEW METHOD

In this paper we describe an infrastructure for the development of standardized procedures for semi and fully automated pre-processing of EEG data. Our pipeline incorporates several methods to isolate cortical signal from noise, maintain maximal information from raw recordings and provide comprehensive quality control and data visualization. In addition, batch processing procedures are integrated to scale up analyses for processing hundreds or thousands of data sets using HPC clusters.

RESULTS

We demonstrate here that by using the EEG Integrated Platform Lossless (EEG-IP-L) pipeline's signal quality annotations, significant increase in data retention is achieved when applying subsequent post-processing ERP segment rejection procedures. Further, we demonstrate that the increase in data retention does not attenuate the ERP signal.

CONCLUSIONS

The EEG-IP-L state provides the infrastructure for an integrated platform that includes long-term data storage, minimal data manipulation and maximal signal retention, and flexibility in post processing strategies.

Using Biosignals for Objective Measurement of Presence in Virtual Reality Environments

The concept of 'presence' in the context of virtual reality (VR) refers to the experience of being in the virtual environment, even when one is physically situated in the real world. Therefore, it is a key parameter of assessing a VR system, based on which, improvements can be made to it. To overcome the limitations of existing methods that are based on standard questionnaires and behavioral analysis, this study proposes to investigate the suitability of biosignals of the user to derive an objective measure of presence. The proposed approach includes experiments conducted on 20 users, recording EEG, ECG and electrodermal activity (EDA) signals while experiencing custom designed VR scenarios with factors contributing to presence suppressed and unsuppressed. Mutual Information based feature selection and subsequent paired t-tests used to identify significant variations in biosignal features when each factor of presence is suppressed revealed significant (p < 0.05) differences in the mean values of EEG signal power and coherence within alpha, beta and gamma bands distributed in specific regions of the brain. Statistical features showed a significant variation with the suppression of realism factor. The variations of activity in the temporal region lead to the assumption of insula activation which may be related to the sense of presence. Therefore, the use of biosignals for an objective measurement of presence in VR systems indicates promise.

Dynamic Causal Modelling of the Reduced Habituation to Painful Stimuli in Migraine: An EEG Study

A consistent finding in migraine is reduced cortical habituation to repetitive sensory stimuli. This study investigated brain dynamics underlying the atypical habituation to painful stimuli in interictal migraine. We investigated modulations in effective connectivity between the sources of laser evoked potentials (LEPs) from a first to final block of trigeminal LEPs using dynamic causal modelling (DCM) in a group of 23 migraine patients and 20 controls. Additionally, we looked whether the strength of dynamical connections in the migrainous brain is initially different. The examined network consisted of the secondary somatosensory areas (lS2, rS2), insulae (lIns, rIns), anterior cingulate cortex (ACC), contralateral primary somatosensory cortex (lS1), and a hidden source assumed to represent the thalamus. Results suggest that migraine patients show initially heightened communication between lS1 and the thalamus, in both directions. After repetitive stimulations, connection strengths from the thalamus to all somatosensory areas habituated in controls whereas this was not apparent in migraine. Together with further abnormalities in initial connectivity strengths and modulations between the thalamus and the insulae, these results are in line with altered thalamo-cortical network dynamics in migraine. Group differences in connectivity from and to the insulae including interhemispheric connections, suggests an important role of the insulae.

Does unfairness sound wrong? A cross-domain investigation of expectations in music and social decision-making

This study was interested in investigating the existence of a shared psychological mechanism for the processing of expectations across domains. The literature on music and language shows that violations of expectations produce similar neural responses and violating the expectation in one domain may influence the processing of stimuli in the other domain. Like music and language, our social world is governed by a system of inherent rules or norms, such as fairness. The study therefore aimed to draw a parallel to the social domain and investigate whether a manipulation of melodic expectation can influence the processing of higher-level expectations of fairness. Specifically, we aimed to investigate whether the presence of an unexpected melody enhances or reduces participants' sensitivity to the violations of fairness and the behavioural reactions associated with these. We embedded a manipulation of melodic expectation within a social decision-making paradigm, whereby musically expected and unexpected stimuli will be simultaneously presented with fair and unfair divisions in a third-party altruistic punishment game. Behavioural and electroencephalographic responses were recorded. Results from the pre-planned analyses show that participants are less likely to punish when melodies are more unexpected and that violations of fairness norms elicit medial frontal negativity (MFN)-life effects. Because no significant interactions between melodic expectancy and fairness of the division were found, results fail to provide evidence of a shared mechanism for the processing of expectations. Exploratory analyses show two additional effects: (i) unfair divisions elicit an early attentional component (P2), probably associated with stimulus saliency, and (ii) mid-value divisions elicit a late MFN-like component, probably reflecting stimulus ambiguity. Future studies could build on these results to further investigate the effect of the cross-domain influence of music on the processing of social stimuli on these early and late components.

Neurophysiological and Psychological Consequences of Social Exclusion: The Effects of Cueing In-Group and Out-Group Status

Exclusion by outgroups is often attributed to external factors such as prejudice. Recently, event-related potential studies have demonstrated that subtle cues influence expectations of exclusion, altering the P3b response to inclusion or exclusion. We investigated whether a visual difference between participants and interaction partners could activate expectations of exclusion, indexed by P3b activity, and whether this difference would influence psychological responses to inclusion and exclusion. Participants played a ball-tossing game with two computer-controlled coplayers who were believed to be real. One period involved fair play inclusion while the other involved partial exclusion. Avatars represented participants, with their color matching participant skin tone, and either matching or differing from the color of coplayer avatars. This created the impression that the participant was an ingroup or outgroup member. While ingroup members elicited enhanced P3b activation when receiving the ball during exclusion, outgroup members showed this pattern for both inclusion and exclusion, suggesting that they formed robust a-priori expectations of exclusion. Self-reports indicated that while these expectations were psychologically protective during exclusion, they were detrimental during inclusion. Ultimately, this study reveals that expectations of exclusion can be formed purely based on visual group differences, regardless of the actual minority or majority status of individuals.

Patients with fibromyalgia show increased beta connectivity across distant networks and microstates alterations in resting-state electroencephalogram

Fibromyalgia (FM) is a chronic condition characterized by widespread pain of unknown etiology associated with alterations in the central nervous system. Although previous studies demonstrated altered patterns of brain activity during pain processing in patients with FM, alterations in spontaneous brain oscillations, in terms of functional connectivity or microstates, have been barely explored so far. Here we recorded the EEG from 43 patients with FM and 51 healthy controls during open-eyes resting-state. We analyzed the functional connectivity between different brain networks computing the phase lag index after group Independent Component Analysis, and also performed an EEG microstates analysis. Patients with FM showed increased beta band connectivity between different brain networks and alterations in some microstates parameters (specifically lower occurrence and coverage of microstate class C). We speculate that the observed alterations in spontaneous EEG may suggest the dominance of endogenous top-down influences; this could be related to limited processing of novel external events and the deterioration of flexible behavior and cognitive control frequently reported for FM. These findings provide the first evidence of alterations in long-distance phase connectivity and microstate indices at rest, and represent progress towards the understanding of the pathophysiology of fibromyalgia and the identification of novel biomarkers for its diagnosis.

Enhanced Performance of a Brain Switch by Simultaneous Use of EEG and NIRS Data for Asynchronous Brain-Computer Interface

Previous studies have shown the superior performance of hybrid electroencephalography (EEG)/ near-infrared spectroscopy (NIRS) brain-computer interfaces (BCIs). However, it has been veiled whether the use of a hybrid EEG/NIRS modality can provide better performance for a brain switch that can detect the onset of the intention to turn on a BCI. In this study, we developed such a hybrid EEG/NIRS brain switch and compared its performance with single modality EEG- and NIRS-based brain switch respectively, in terms of true positive rate (TPR), false positive rate (FPR), onset detection time (ODT), and information transfer rate (ITR). In an offline analysis, the performance of a hybrid EEG/NIRS brain switch was significantly improved over that of EEG- and NIRS-based brain switches in general, and in particular a significantly lower FPR was observed for the hybrid EEG/NIRS brain switch. A pseudo-online analysis was additionally performed to confirm the feasibility of implementing an online BCI system with our hybrid EEG/NIRS brain switch. The overall trend of pseudo-online analysis results generally coincided with that of the offline analysis results. No significant difference in all performance measures was also found between offline and pseudo online analysis schemes when the amount of training data was same, with one exception for the ITRs of an EEG brain switch. These offline and pseudo-online results demonstrate that a hybrid EEG/NIRS brain switch can be used to provide a better onset detection performance than that of a single neuroimaging modality.

Physiological synchrony in EEG, electrodermal activity and heart rate reflects shared selective auditory attention

OBJECTIVE

Concurrent changes in physiological signals across multiple listeners (physiological synchrony-PS), as caused by shared affective or cognitive processes, may be a suitable marker of selective attentional focus. We aimed to identify the selective attention of participants based on PS with individuals sharing attention with respect to different stimulus aspects.

APPROACH

We determined PS in electroencephalography (EEG), electrodermal activity (EDA) and electrocardiographic inter-beat interval (IBI) of participants who all heard the exact same audio track, but were instructed to either attend to the audiobook or to interspersed auditory events such as affective sounds and beeps that attending participants needed to keep track of.

MAIN RESULTS

PS in all three measures reflected the selective attentional focus of participants. In EEG and EDA, PS was higher for participants when linked to participants with the same attentional instructions than when linked to participants instructed to focus on different stimulus aspects, but in IBI this effect did not reach significance. Comparing PS between a participant and members from the same or the different attentional group allowed for the correct identification of the participant's attentional instruction in 96%, 73% and 73% of the cases, for EEG, EDA and IBI, respectively, all well above chance level. PS with respect to the attentional groups also predicted performance on post-audio questions about the groups' stimulus content.

SIGNIFICANCE

Our results show that selective attention of participants can be monitored using PS, not only in EEG, but also in EDA and IBI. These results are promising for real-world applications, where wearables measuring peripheral signals like EDA and IBI may be preferred over EEG sensors.

A Critical Analysis on Characterizing the Meditation Experience Through the Electroencephalogram

Meditation practices, originated from ancient traditions, have increasingly received attention due to their potential benefits to mental and physical health. The scientific community invests efforts into scrutinizing and quantifying the effects of these practices, especially on the brain. There are methodological challenges in describing the neural correlates of the subjective experience of meditation. We noticed, however, that technical considerations on signal processing also don't follow standardized approaches, which may hinder generalizations. Therefore, in this article, we discuss the usage of the electroencephalogram (EEG) as a tool to study meditation experiences in healthy individuals. We describe the main EEG signal processing techniques and how they have been translated to the meditation field until April 2020. Moreover, we examine in detail the limitations/assumptions of these techniques and highlight some good practices, further discussing how technical specifications may impact the interpretation of the outcomes. By shedding light on technical features, this article contributes to more rigorous approaches to evaluate the construct of meditation.

Adjusting ADJUST: Optimizing the ADJUST algorithm for pediatric data using geodesic nets

A major challenge for electroencephalograph (EEG) studies on pediatric populations is that large amounts of data are lost due to artifacts (e.g., movement and blinks). Independent component analysis (ICA) can separate artifactual and neural activity, allowing researchers to remove such artifactual activity and retain a greater percentage of EEG data for analyses. However, manual identification of artifactual components is time-consuming and requires subjective judgment. Automated algorithms, like ADJUST and ICLabel, have been validated on adults, but to our knowledge, no such algorithms have been optimized for pediatric data. Therefore, in an attempt to automate artifact selection for pediatric data collected with geodesic nets, we modified ADJUST's algorithm. Our "adjusted-ADJUST" algorithm was compared to the "original-ADJUST" algorithm and ICLabel in adults, children, and infants on three different performance measures: respective classification agreement with expert coders, the number of trials retained following artifact removal, and the reliability of the EEG signal after preprocessing with each algorithm. Overall, the adjusted-ADJUST algorithm performed better than the original-ADJUST algorithm and no ICA correction with adult and pediatric data. Moreover, in some measures, it performed better than ICLabel for pediatric data. These results indicate that optimizing existing algorithms improves artifact classification and retains more trials, potentially facilitating EEG studies with pediatric populations. Adjusted-ADJUST is freely available under the terms of the GNU General Public License at: https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline/tree/master/adjusted_adjust_scripts.

Multifractal and Entropy-Based Analysis of Delta Band Neural Activity Reveals Altered Functional Connectivity Dynamics in Schizophrenia

Dynamic functional connectivity (DFC) was established in the past decade as a potent approach to reveal non-trivial, time-varying properties of neural interactions – such as their multifractality or information content –, that otherwise remain hidden from conventional static methods. Several neuropsychiatric disorders were shown to be associated with altered DFC, with schizophrenia (SZ) being one of the most intensely studied among such conditions. Here we analyzed resting-state electroencephalography recordings of 14 SZ patients and 14 age- and gender-matched healthy controls (HC). We reconstructed dynamic functional networks from delta band (0.5–4 Hz) neural activity and captured their spatiotemporal dynamics in various global network topological measures. The acquired network measure time series were made subject to dynamic analyses including multifractal analysis and entropy estimation. Besides group-level comparisons, we built a classifier to explore the potential of DFC features in classifying individual cases. We found stronger delta-band connectivity, as well as increased variance of DFC in SZ patients. Surrogate data testing verified the true multifractal nature of DFC in SZ, with patients expressing stronger long-range autocorrelation and degree of multifractality when compared to controls. Entropy analysis indicated reduced temporal complexity of DFC in SZ. When using these indices as features, an overall cross-validation accuracy surpassing 89% could be achieved in classifying individual cases. Our results imply that dynamic features of DFC such as its multifractal properties and entropy are potent markers of altered neural dynamics in SZ and carry significant potential not only in better understanding its pathophysiology but also in improving its diagnosis. The proposed framework is readily applicable for neuropsychiatric disorders other than schizophrenia.

The Self and Its Prolonged Intrinsic Neural Timescale in Schizophrenia

Schizophrenia (SCZ) can be characterized as a basic self-disorder that is featured by abnormal temporal integration on phenomenological (experience) and psychological (information processing) levels. Temporal integration on the neuronal level can be measured by the brain's intrinsic neural timescale using the autocorrelation window (ACW) and power-law exponent (PLE). Our goal was to relate intrinsic neural timescales (ACW, PLE), as a proxy of temporal integration on the neuronal level, to temporal integration related to self-disorder on psychological (Enfacement illusion task in electroencephalography) and phenomenological (Examination of Anomalous Self-Experience [EASE]) levels. SCZ participants exhibited prolonged ACW and higher PLE during the self-referential task (Enfacement illusion), but not during the non-self-referential task (auditory oddball). The degree of ACW/PLE change during task relative to rest was significantly reduced in self-referential task in SCZ. A moderation model showed that low and high ACW/PLE exerted differential impact on the relationship of self-disorder (EASE) and negative symptoms (PANSS). In sum, we demonstrate abnormal prolongation in intrinsic neural timescale during self-reference in SCZ including its relation to basic self-disorder and negative symptoms. Our results point to abnormal relation of self and temporal integration at the core of SCZ constituting a "common currency" of neuronal, psychological, and phenomenological levels.

In context: emotional intent and temporal immediacy of contextual descriptions modulate affective ERP components to facial expressions

In this study, we explored how contextual information about threat dynamics affected the electrophysiological correlates of face perception. Forty-six healthy native Swedish speakers read verbal descriptions signaling an immediate vs delayed intent to escalate or deescalate an interpersonal conflict. Each verbal description was followed by a face with an angry or neutral expression, for which participants rated valence and arousal. Affective ratings confirmed that the emotional intent expressed in the descriptions modulated emotional reactivity to the facial stimuli in the expected direction. The electrophysiological data showed that compared to neutral faces, angry faces resulted in enhanced early and late event-related potentials (VPP, P300 and LPP). Additionally, emotional intent and temporal immediacy modulated the VPP and P300 similarly across angry and neutral faces, suggesting that they influence early face perception independently of facial affect. By contrast, the LPP amplitude to faces revealed an interaction between facial expression and emotional intent. Deescalating descriptions eliminated the LPP differences between angry and neutral faces. Together, our results suggest that information about a person’s intentions modulates the processing of facial expressions.

Potential pitfalls of widely used implementations of common spatial patterns

We have uncovered serious flaws in handling EEG signals with a decreased rank in implementations of the common spatial patterns (CSP). The CSP algorithm assumes covariance matrices of the signal to have full rank. However, preprocessing techniques, such as artifact removal using independent component analysis, may decrease the rank of the signal, leading to potential errors in the CSP decomposition. We inspect what could go wrong when CSP implementations do not take this into consideration on a binary motor imagery classification task. We review CSP implementations in open-source toolboxes for EEG signal analysis (FieldTrip, BBCI Toolbox, BioSig, EEGLAB, BCILAB, and MNE). We show that unprotected implementations decreased mean classification accuracy by up to 32%, with spatial filters resulting in complex numbers, for which corresponding spatial patterns do not have a clear interpretation. We encourage researchers to check their implementations and analysis pipelines.

Neural dynamics of sentiment processing during naturalistic sentence reading

When we read, our eyes move through the text in a series of fixations and high-velocity saccades to extract visual information. This process allows the brain to obtain meaning, e.g., about sentiment, or the emotional valence, expressed in the written text. How exactly the brain extracts the sentiment of single words during naturalistic reading is largely unknown. This is due to the challenges of naturalistic imaging, which has previously led researchers to employ highly controlled, timed word-by-word presentations of custom reading materials that lack ecological validity. Here, we aimed to assess the electrical neural correlates of word sentiment processing during naturalistic reading of English sentences. We used a publicly available dataset of simultaneous electroencephalography (EEG), eye-tracking recordings, and word-level semantic annotations from 7129 words in 400 sentences (Zurich Cognitive Language Processing Corpus; Hollenstein et al., 2018). We computed fixation-related potentials (FRPs), which are evoked electrical responses time-locked to the onset of fixations. A general linear mixed model analysis of FRPs cleaned from visual- and motor-evoked activity showed a topographical difference between the positive and negative sentiment condition in the 224-304 ​ms interval after fixation onset in left-central and right-posterior electrode clusters. An additional analysis that included word-, phrase-, and sentence-level sentiment predictors showed the same FRP differences for the word-level sentiment, but no additional FRP differences for phrase- and sentence-level sentiment. Furthermore, decoding analysis that classified word sentiment (positive or negative) from sentiment-matched 40-trial average FRPs showed a 0.60 average accuracy (95% confidence interval: [0.58, 0.61]). Control analyses ruled out that these results were based on differences in eye movements or linguistic features other than word sentiment. Our results extend previous research by showing that the emotional valence of lexico-semantic stimuli evoke a fast electrical neural response upon word fixation during naturalistic reading. These results provide an important step to identify the neural processes of lexico-semantic processing in ecologically valid conditions and can serve to improve computer algorithms for natural language processing.

Revealing Unconscious Consumer Reactions to Advertisements That Include Visual Metaphors. A Neurophysiological Experiment

The main challenge of advertising is to catch consumers' attention and evoke in them positive attitudes to consequently achieve product preference and higher purchase intentions. In modern advertising, visual metaphors are widely used due to their effects such as improving advertising recall, enhancing persuasiveness, and generating consumers' positive attitudes. Previous research has pointed out the existence of an "inverted U-curve" that describes a positive relationship between the conceptual complexity of metaphors and consumers' positive reactions to them, which ends where complexity outweighs comprehension. Despite the dominance of visual metaphors in modern advertising, academic research on this topic has been relatively sparse. The inverted U-curve pattern has been validated regarding ad appreciation, ad liking, and purchase intention by using declarative methods. However, at present, there is no evidence of consumers' neurophysiological responses to visual metaphors included in advertising. Given this gap, the aim of this research is to assess consumer neurophysiological responses to print advertisements that include visual metaphors, using neuroscience-based techniques. Forty-three participants (22W-21M) were exposed to 28 stimuli according to three levels of visual complexity, while their reactions were recorded with an electroencephalogram (EEG), eye tracking (ET), and galvanic skin response (GSR). The results indicated that, regardless of metaphor type, ads with metaphors evoke more positive reactions than non-metaphor ads. EEG results revealed a positive relationship between cognitive load and conceptual complexity that is not mediated by comprehension. This suggests that the cognitive load index could be a suitable indicator of complexity, as it reflects the amount of cognitive resources needed to process stimuli. ET results showed significant differences in the time dedicated to exploring the ads; however, comprehension doesn't mediate this relationship. Moreover, no cognitive load was detected from GSR. ET and GSR results suggest that neither methodology is a suitable measure of cognitive load in the case of visual metaphors. Instead, it seems that they are more related to the attention and/or emotion devoted to the stimuli. Our empirical analysis reveals the importance of using neurophysiological measures to analyze the appropriate use of visual metaphors and to find out how to maximize their impact on advertising effectiveness.

Day-to-Day Test-Retest Reliability of EEG Profiles in Children With Autism Spectrum Disorder and Typical Development

Biomarker development is currently a high priority in neurodevelopmental disorder research. For many types of biomarkers (particularly biomarkers of diagnosis), reliability over short periods is critically important. In the field of autism spectrum disorder (ASD), resting electroencephalography (EEG) power spectral densities (PSD) are well-studied for their potential as biomarkers. Classically, such data have been decomposed into pre-specified frequency bands (e.g., delta, theta, alpha, beta, and gamma). Recent technical advances, such as the Fitting Oscillations and One-Over-F (FOOOF) algorithm, allow for targeted characterization of the features that naturally emerge within an EEG PSD, permitting a more detailed characterization of the frequency band-agnostic shape of each individual's EEG PSD. Here, using two resting EEGs collected a median of 6 days apart from 22 children with ASD and 25 typically developing (TD) controls during the Feasibility Visit of the Autism Biomarkers Consortium for Clinical Trials, we estimate test-retest reliability based on the characterization of the PSD shape in two ways: (1) Using the FOOOF algorithm we estimate six parameters (offset, slope, number of peaks, and amplitude, center frequency and bandwidth of the largest alpha peak) that characterize the shape of the EEG PSD; and (2) using nonparametric functional data analyses, we decompose the shape of the EEG PSD into a reduced set of basis functions that characterize individual power spectrum shapes. We show that individuals exhibit idiosyncratic PSD signatures that are stable over recording sessions using both characterizations. Our data show that EEG activity from a brief 2-min recording provides an efficient window into characterizing brain activity at the single-subject level with desirable psychometric characteristics that persist across different analytical decomposition methods. This is a necessary step towards analytical validation of biomarkers based on the EEG PSD and provides insights into parameters of the PSD that offer short-term reliability (and thus promise as potential biomarkers of trait or diagnosis) vs. those that are more variable over the short term (and thus may index state or other rapidly dynamic measures of brain function). Future research should address the longer-term stability of the PSD, for purposes such as monitoring development or response to treatment.

How Sensitive Are EEG Results to Preprocessing Methods: A Benchmarking Study

Although several guidelines for best practices in EEG preprocessing have been released, even studies that strictly adhere to those guidelines contain considerable variation in the ways that the recommended methods are applied. An open question for researchers is how sensitive the results of EEG analyses are to variations in preprocessing methods and parameters. To address this issue, we analyze the effect of preprocessing methods on downstream EEG analysis using several simple signal and event-related measures. Signal measures include recording-level channel amplitudes, study-level channel amplitude dispersion, and recording spectral characteristics. Event-related methods include ERPs and ERSPs and their correlations across methods for a diverse set of stimulus events. Our analysis also assesses differences in residual signals both in the time and spectral domains after blink artifacts have been removed. Using fully automated pipelines, we evaluate these measures across 17 EEG studies for two ICA-based preprocessing approaches (LARG, MARA) plus two variations of Artifact Subspace Reconstruction (ASR). Although the general structure of the results is similar across these preprocessing methods, there are significant differences, particularly in the low-frequency spectral features and in the residuals left by blinks. These results argue for detailed reporting of processing details as suggested by most guidelines, but also for using a federation of automated processing pipelines and comparison tools to quantify effects of processing choices as part of the research reporting.

Corticomuscular interactions during different movement periods in a multi-joint compound movement

While much is known about motor control during simple movements, corticomuscular communication profiles during compound movement control remain largely unexplored. Here, we aimed at examining frequency band related interactions between brain and muscles during different movement periods of a bipedal squat (BpS) task utilizing regression corticomuscular coherence (rCMC), as well as partial directed coherence (PDC) analyses. Participants performed 40 squats, divided into three successive movement periods (Eccentric (ECC), Isometric (ISO) and Concentric (CON)) in a standardized manner. EEG was recorded from 32 channels specifically-tailored to cover bilateral sensorimotor areas while bilateral EMG was recorded from four main muscles of BpS. We found both significant CMC and PDC (in beta and gamma bands) during BpS execution, where CMC was significantly elevated during ECC and CON when compared to ISO. Further, the dominant direction of information flow (DIF) was most prominent in EEG-EMG direction for CON and EMG-EEG direction for ECC. Collectively, we provide novel evidence that motor control during BpS is potentially achieved through central motor commands driven by a combination of directed inputs spanning across multiple frequency bands. These results serve as an important step toward a better understanding of brain-muscle relationships during multi joint compound movements.

Microstates-based resting frontal alpha asymmetry approach for understanding affect and approach/withdrawal behavior

The role of resting frontal alpha-asymmetry in explaining neural-mechanisms of affect and approach/withdrawal behavior is still debatable. The present study explores the ability of the quasi-stable resting EEG asymmetry information and the associated neurovascular synchronization/desynchronization in bringing more insight into the understanding of neural-mechanisms of affect and approach/withdrawal behavior. For this purpose, a novel frontal alpha-asymmetry based on microstates, that assess quasi-stable EEG scalp topography information, is proposed and compared against standard frontal-asymmetry. Both proposed and standard frontal alpha-asymmetries were estimated from thirty-nine healthy volunteers resting-EEG simultaneously acquired with resting-fMRI. Further, neurovascular mechanisms of these asymmetry measures were estimated through EEG-informed fMRI. Subsequently, the Hemodynamic Lateralization Index (HLI) of the neural-underpinnings of both asymmetry measures was assessed. Finally, the robust correlation of both asymmetry-measures and their HLI’s with PANAS, BIS/BAS was carried out. The standard resting frontal-asymmetry and its HLI yielded no significant correlation with any psychological-measures. However, the microstate resting frontal-asymmetry correlated significantly with negative affect and its neural underpinning’s HLI significantly correlated with Positive/Negative affect and BIS/BAS measures. Finally, alpha-BOLD desynchronization was observed in neural-underpinning whose HLI correlated significantly with negative affect and BIS. Hence, the proposed resting microstate-frontal asymmetry better assesses the neural-mechanisms of affect, approach/withdrawal behavior.

Artificial food coloring affects EEG power and ADHD symptoms in college students with ADHD: a pilot study

Objectives: Removing artificial food coloring (AFC) is a common dietary intervention for children with Attention-Deficit/Hyperactivity Disorder (ADHD), but has not been tested in young adults. This pilot study examined the effects of AFC on ADHD symptoms and electroencephalography (EEG) in college students with and without ADHD.Methods: At baseline, control and ADHD participants completed the Adult ADHD Self-Report Scale (ASRS), simple and complex attention measures, and resting-state EEG recordings. ADHD participants (n = 18) and a subset of controls (extended control group or EC, n = 11) avoided AFC in their diet for 2 weeks and then were randomized to a double-blind, placebo-controlled crossover challenge. Subjects received either 225 mg AFC disguised in chocolate cookies or placebo chocolate cookies for 3 days each week, with testing on the third day each week. Baseline comparisons were made using Student's t-test or Wilcoxon rank sum tests and challenge period analyses were run using General Linear Modeling.Results: The ADHD group had significantly greater scores on the ASRS (p < 0.001), confirming a symptom differential between groups; however, there were no differences in attentional measures or EEG at baseline. The AFC challenge resulted in an increase in posterior mean gamma power (p = 0.05), a decrease in posterior relative alpha power (p = 0.04), and a marginal increase in inattentive symptoms (p = 0.08) in the ADHD group. There were no effects of AFC in the EC group.Discussion: This study indicates that AFC exposure may affect brainwave activity and ADHD symptoms in college students with ADHD. Larger studies are needed to confirm these findings.

It's costly punishment, not altruistic: Low midfrontal theta and state anger predict punishment

Punishment in economic games has been interpreted as "altruistic." However, it was shown that punishment is related to trait anger instead of trait altruism in a third-party dictator game if compensation is also available. Here, we investigated the influence of state anger on punishment and compensation in the third-party dictator game. Therefore, we used movie sequences for emotional priming, including the target states anger, happy, and neutral. We measured the Feedback-Related Negativity (FRN) and midfrontal theta band activation, to investigate an electro-cortical correlate of the processing of fair and unfair offers. Also, we assessed single-trial FRN and midfrontal theta band activation as a predictor for punishment and compensation. We found that punishment was linked to state anger. Midfrontal theta band activation, which has previously been linked to altruistic acts and cognitive control, predicted less punishment. Additionally, trait anger led to enhanced FRN for unfair offers. This led to the interpretation that the FRN depicts the evaluation of fairness, while midfrontal theta band activation captures an aspect of cognitive control and altruistic motivation. We conclude that we need to redefine "altruistic punishment" into "costly punishment," as no direct link of altruism and punishment is given. Additionally, midfrontal theta band activation complements the FRN and offers additional insights into complex responses and decision processes, especially as a single trial predictor.

Looking through blue glasses: bioelectrical measures to assess the awakening after a calm situation

Colors can elicit cognitive and emotional states. In particular, blue colour is associated to "refresh" and "restart" effects and is suggested to enhance a wake-up after a calm situation. In this exploratory study, these claims are investigated using Electroencephalographic (EEG), Skin Conductance (SC) and pupil diameter data. The results confirmed the "wake-up effect" for subjects wearing the lenses, as measured by Global Field Power (GFP) in Theta Band, Skin Conductance Response (SCR) and pupil diameter data.

Decreased transfer of value to action in Tourette syndrome

OBJECTIVE

Tourette syndrome is a neurodevelopmental disorder putatively associated with a hyperdopaminergic state. Therefore, it seems plausible that excessive dopamine transmission in Tourette syndrome alters the ability to learn based on rewards and punishments. We tested whether Tourette syndrome patients exhibited altered reinforcement learning and corresponding feedback-related EEG deflections.

METHODS

We used a reinforcement learning task providing factual and counterfactual feedback in a sample of 15 Tourette syndrome patients and matched healthy controls whilst recording EEG. The paradigm presented various reward probabilities to enforce adaptive adjustments. We employed a computational model to derive estimates of the prediction error, which we used for single-trial regression analysis of the EEG data.

RESULTS

We found that Tourette syndrome patients showed increased choice stochasticity compared to controls. The feedback-related negativity represented an axiomatic prediction error for factual feedback and did not differ between groups. We observed attenuated P3a modulation specifically for factual feedback in Tourette syndrome patients, representing impaired coding of attention allocation.

CONCLUSION

Our findings indicate that cortical prediction error coding is unaffected by Tourette syndrome. Nonetheless, the transfer of learned values into choice formation is degraded, in line with a hyperdopaminergic state.

Surprise response as a probe for compressed memory states

The limited capacity of recent memory inevitably leads to partial memory of past stimuli. There is also evidence that behavioral and neural responses to novel or rare stimuli are dependent on one’s memory of past stimuli. Thus, these responses may serve as a probe of different individuals’ remembering and forgetting characteristics. Here, we utilize two lossy compression models of stimulus sequences that inherently involve forgetting, which in addition to being a necessity under many conditions, also has theoretical and behavioral advantages. One model is based on a simple stimulus counter and the other on the Information Bottleneck (IB) framework which suggests a more general, theoretically justifiable principle for biological and cognitive phenomena. These models are applied to analyze a novelty-detection event-related potential commonly known as the P300. The trial-by-trial variations of the P300 response, recorded in an auditory oddball paradigm, were subjected to each model to extract two stimulus-compression parameters for each subject: memory length and representation accuracy. These parameters were then utilized to estimate the subjects’ recent memory capacity limit under the task conditions. The results, along with recently published findings on single neurons and the IB model, underscore how a lossy compression framework can be utilized to account for trial-by-trial variability of neural responses at different spatial scales and in different individuals, while at the same time providing estimates of individual memory characteristics at different levels of representation using a theoretically-based parsimonious model.

Surprise responses reflect expectations based on preceding stimuli representations, and hence can be used to infer the characteristics of memory utilized for a task. We suggest a quantitative method for extracting an individual estimate of effective memory capacity dedicated for a task based on the correspondence between a theoretical surprise model and electrophysiological single-trial surprise responses. We demonstrate this method on EEG responses recorded while participants were performing a simple auditory task; we show the correspondence between the theoretical and physiological surprise, and calculate an estimate of the utilized memory. The generality of this framework allows it to be applied to different EEG features that reflect different modes and levels of the processing hierarchy, as well as other physiological measures of surprise responses. Future studies may use this framework to construct a handy diagnostic tool for a quantitative, individualized characterization of memory-related disorders.

Smiling as negative feedback affects social decision-making and its neural underpinnings

A crucial aspect of social decision-making is the ability to learn from the outcomes of preceding decisions. In particular, learning might be influenced by the expectedness of feedback and its valence. Expectedness has largely been operationalized as the frequency of stimulus occurrence and not in terms of its social context. Therefore, we investigated the influence of socially unexpected feedback, i.e., smiling upon adverse events, on behavioral and neural responses. We used a modified version of the ultimatum game, a commonly used paradigm for economic decision-making, by implementing different proposer identities with a distinct reaction pattern towards accepted and rejected monetary offers. We could show that an identity, who reacted with a smile towards rejected offers, evoked lower acceptance rates compared to identities, who reward acceptance with a smile. Electrophysiological correlates indicate N170 effects for emotional identities compared to a neutral control identity. Regarding FRN and P3 brain potentials, we detected a particular function of the smiling face when used as a socially unexpected, negative feedback stimulus. Hence, individuals seek an unexpected smile despite the associated monetary loss, which is accompanied by distinct neural patterns.

Use of longitudinal EEG measures in estimating language development in infants with and without familial risk for autism spectrum disorder

Language development in children with autism spectrum disorder (ASD) varies greatly among affected individuals and is a strong predictor of later outcomes. Younger siblings of children with ASD have increased risk of ASD, but also language delay. Identifying neural markers of language outcomes in infant siblings could facilitate earlier intervention and improved outcomes. This study aimed to determine whether EEG measures from the first 2-years of life can explain heterogeneity in language development in children at low- and high-risk for ASD, and to determine whether associations between EEG measures and language development are different depending on ASD risk status or later ASD diagnosis. In this prospective longitudinal study EEG measures collected between 3-24 months were used in a multivariate linear regression model to estimate participants' 24-month language development. Individual baseline longitudinal EEG measures included (1) the slope of EEG power across 3-12 months or 3-24 months of life for 6 canonical frequency bands, (2) estimated EEG power at age 6-months for the same frequency bands, and (3) terms representing the interaction between ASD risk status and EEG power measures. Modeled 24-month language scores using EEG data from either the first 2-years (Pearson R = 0.70, 95% CI 0.595-0.783, P=1x10^-18) or the first year of life (Pearson R=0.66, 95% CI 0.540-0.761, P=2.5x10^-14) were highly correlated with observed scores. All models included significant interaction effects of risk on EEG measures, suggesting that EEG-language associations are different depending on risk status, and that different brain mechanisms effect language development in low-versus high-risk infants.

Sensory and Cognitive Components of Auditory Processing in Individuals With Tinnitus

Purpose The main purpose of the study is to explore the auditory selective attention abilities (using event-related potentials) and the neuronal oscillatory activity in the default mode network sites (using electroencephalogram [EEG]) in individuals with tinnitus. Method Auditory selective attention was measured using P300, and the resting state EEG was assessed using the default mode function analysis. Ten individuals with continuous and bothersome tinnitus along with 10 age- and gender-matched control participants underwent event-related potential testing and 5 min of EEG recording (at wakeful rest). Results Individuals with tinnitus were observed to have larger N1 and P3 amplitudes along with prolonged P3 latency. The default mode function analysis revealed no significant oscillatory differences between the groups. Conclusion The current study shows changes in both the early sensory and late cognitive components of auditory processing. The change in the P3 component is suggestive of selective auditory attention deficit, and the sensory component (N1) suggests an altered bottom-up processing in individuals with tinnitus.

Using the N400 event-related potential to study word learning from context in children from low- and higher-socioeconomic status homes

Children from low-socioeconomic status (SES) homes have significantly smaller vocabularies than their higher-SES peers, a gap that increases over the course of the school years. One reason for the increase in this vocabulary gap during the school years is that children from low-SES homes learn fewer words from context than their higher-SES peers. To better understand how the process of word learning from context might differ in children related to SES, we investigated changes in the N400 event-related potential (ERP) as children from low- and higher-SES homes learned new words using only the surrounding linguistic context. There were no differences in the N400 response to known words related to SES. In response to the target word being learned, children from higher-SES homes, like adults in previous studies, exhibited an attenuation of the N400 across exposures as they attached meaning to it. Children from low-SES homes did not show this same attenuation. These findings support previous work showing that children from low-SES homes may have differences or more variability in the neural components supporting language processing, and they extend previous work to illustrate how this variability may relate to word learning and, ultimately, vocabulary growth.

A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data

Evaluation of cognitive workload finds its application in many areas, from educational program assessment through professional driver health examination to monitoring the mental state of people carrying out jobs of high responsibility, such as pilots or airline traffic dispatchers. Estimation of multilevel cognitive workload is a task usually realized in a subject-dependent way, while the present research is focused on developing the procedure of subject-independent evaluation of cognitive workload level. The aim of the paper is to estimate cognitive workload level in accordance with subject-independent approach, applying classical machine learning methods combined with feature selection techniques. The procedure of data acquisition was based on registering the EEG signal of the person performing arithmetical tasks divided into six intervals of advancement. The analysis included the stages of preprocessing, feature extraction, and selection, while the final step covered multiclass classification performed with several models. The results discussed show high maximal accuracies achieved: ~91% for both the validation dataset and for the cross-validation approach for kNN model.

Improved EOG Artifact Removal Using Wavelet Enhanced Independent Component Analysis

Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. By decomposing the signals into neural and artifactual components and artifact components can be eliminated before signal reconstruction. Unfortunately, removing entire components may result in losing important neural information present in the component and eventually may distort the spectral characteristics of the reconstructed signals. An alternative approach is to correct artifacts within the independent components instead of rejecting the entire component, for which wavelet transform based decomposition methods have been used with good results. An improved, fully automatic wavelet-based component correction method is presented for EOG artifact removal that corrects EOG components selectively, i.e., within EOG activity regions only, leaving other parts of the component untouched. In addition, the method does not rely on reference EOG channels. The results show that the proposed method outperforms other component rejection and wavelet-based EOG removal methods in its accuracy both in the time and the spectral domain. The proposed new method represents an important step towards the development of accurate, reliable and automatic EOG artifact removal methods.