Individual differences in brain dynamics: important implications for the calculation of event-related band power

Challenging the Diagnostic Value of Theta/Beta Ratio: Insights From an EEG Subtyping Meta-Analytical Approach in ADHD

The frequently reported high theta/beta ratio (TBR) in the electroencephalograms (EEGs) of children with attention-deficit/hyperactivity disorder (ADHD) has been suggested to include at least two distinct neurophysiological subgroups, a subgroup with high TBR and one with slow alpha peak frequency, overlapping the theta range. We combined three large ADHD cohorts recorded under standardized procedures and used a meta-analytical approach to leverage the large sample size (N = 417; age range: 6-18 years), classify these EEG subtypes and investigate their behavioral correlates to clarify their brain-behavior relationships. To control for the fact that slow alpha might contribute to theta power, three distinct EEG subgroups (non-slow-alpha TBR (NSAT) subgroup, slow alpha peak frequency (SAF) subgroup, not applicable (NA) subgroup) were determined, based on a halfway cut-off in age- and sex-normalized theta and alpha, informed by previous literature. For the meta-analysis, Cohen's d was calculated to assess the differences between EEG subgroups for baseline effects, using means and standard deviations of baseline inattention and hyperactivity-impulsivity scores. Non-significant, small Grand Mean effect sizes (-0.212 < d < 0.218) were obtained when comparing baseline behavioral scores between the EEG subgroups. This study could not confirm any association of EEG subtype with behavioral traits. This confirms previous findings suggesting that TBR has no diagnostic value for ADHD. TBR could, however, serve as an aid to stratify patients between neurofeedback protocols based on baseline TBR. A free online tool was made available for clinicians to calculate age- and sex-corrected TBR decile scores (Brainmarker-IV) for stratification of neurofeedback protocols.

Theta activity and cognitive functioning: Integrating evidence from resting-state and task-related developmental electroencephalography (EEG) research

The theta band is one of the most prominent frequency bands in the electroencephalography (EEG) power spectrum and presents an interesting paradox: while elevated theta power during resting state is linked to lower cognitive abilities in children and adolescents, increased theta power during cognitive tasks is associated with higher cognitive performance. Why does theta power, measured during resting state versus cognitive tasks, show differential correlations with cognitive functioning? This review provides an integrated account of the functional correlates of theta across different contexts. We first present evidence that higher theta power during resting state is correlated with lower executive functioning, attentional abilities, language skills, and IQ. Next, we review research showing that theta power increases during memory, attention, and cognitive control, and that higher theta power during these processes is correlated with better performance. Finally, we discuss potential explanations for the differential correlations between resting/task-related theta and cognitive functioning, and offer suggestions for future research in this area.

Task-irrelevant decorative pictures increase cognitive load during text processing but have no effects on learning or working memory performance: an EEG and eye-tracking study

Decorative pictures (DP) are often used in multimedia task materials and are commonly considered so-called seductive details as they are commonly not task-relevant. Typically, DP result in mixed effects on behavioral performance measures. The current study focused on the effects of DP on the cognitive load during text reading and working memory task performance. The theta and alpha frequency band power of the electroencephalogram (EEG) and pupil dilation served as proxies of cognitive load. The number of fixations, mean fixation durations, and the number of transitions served as proxies of the attentional focus. For both, text reading and n-back working memory tasks, the presence and congruency of DP were manipulated in four task conditions. DP did neither affect behavioral performance nor subjective ratings of emotional-motivational factors. However, in both tasks, DP increased the cognitive load as revealed by the EEG alpha frequency band power and (at least to some extent) by subjective effort ratings. Notably, the EEG alpha frequency band power was a quite reliable and sensitive proxy of cognitive load. Analyzing the EEG data stimulus-locked and fixation-related, the EEG alpha frequency band power revealed a difference in global and local cognitive load. In sum, the current study underlines the feasibility and use of EEG for multimedia research, especially when combined with eye-tracking.

Brain health in diverse settings: How age, demographics and cognition shape brain function

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

Alpha Oscillations and Temporal Binding Windows in Perception-A Critical Review and Best Practice Guidelines

An intriguing question in cognitive neuroscience is whether alpha oscillations shape how the brain transforms the continuous sensory inputs into distinct percepts. According to the alpha temporal resolution hypothesis, sensory signals arriving within a single alpha cycle are integrated, whereas those in separate cycles are segregated. Consequently, shorter alpha cycles should be associated with smaller temporal binding windows and higher temporal resolution. However, the evidence supporting this hypothesis is contentious, and the neural mechanisms remain unclear. In this review, we first elucidate the alpha temporal resolution hypothesis and the neural circuitries that generate alpha oscillations. We then critically evaluate study designs, experimental paradigms, psychophysics, and neurophysiological analyses that have been employed to investigate the role of alpha frequency in temporal binding. Through the lens of this methodological framework, we then review evidence from between-subject, within-subject, and causal perturbation studies. Our review highlights the inherent interpretational ambiguities posed by previous study designs and experimental paradigms and the extensive variability in analysis choices across studies. We also suggest best practice recommendations that may help to guide future research. To establish a mechanistic role of alpha frequency in temporal parsing, future research is needed that demonstrates its causal effects on the temporal binding window with consistent, experimenter-independent methods.

Alpha transcranial alternating current stimulation modulates auditory perception

BACKGROUND

Studies using transcranial alternating current stimulation (tACS), a type of non-invasive brain stimulation, have demonstrated a relationship between the positive versus negative phase of both alpha and delta/theta oscillations with variable near-threshold auditory perception. These findings have not been directly compared before. Furthermore, as perception was better in the positive versus negative phase of two different frequencies, it is unclear whether changes in polarity (independent of a specific frequency) could also modulate auditory perception.

OBJECTIVE

We investigated whether auditory perception depends on the phase of alpha, delta/theta, or polarity alone.

METHODS

We stimulated participants with alpha, delta, and positive and negative direct current (DC) over temporal and central scalp sites while they identified near-threshold tones-in-noise. A Sham condition without tACS served as a control condition. A repeated-measures analysis of variance was used to assess differences in proportions of hits between conditions and polarities. Permutation-based circular-logistic regressions were used to assess the relationship between circular-predictors and single-trial behavioral responses. An exploratory analysis compared the full circular-logistic regression model to the intercept-only model.

RESULTS

Overall, there were a greater proportion of hits in the Alpha condition in comparison to Delta, DC, and Sham conditions. We also found an interaction between polarity and stimulation condition; post-hoc analyses revealed a greater proportion of hits in the positive versus negative phase of Alpha tACS. In contrast, no significant differences were found in the Delta, DC, or Sham conditions. The permutation-based circular-logistic regressions did not reveal a statistically significant difference between the obtained RMS of the sine and cosine coefficients and the mean of the surrogate distribution for any of the conditions. However, our exploratory analysis revealed that circular-predictors explained the behavioral data significantly better than an intercept-only model for the Alpha condition, and not the other three conditions.

CONCLUSION

These findings suggest that alpha tACS, and not delta nor polarity alone, modulates auditory perception.

Distraction descriptor for brainprint authentication modelling using probability-based Incremental Fuzzy-Rough Nearest Neighbour

This paper aims to design distraction descriptor, elicited through the object variation, to refine the granular knowledge incrementally, using the proposed probability-based incremental update strategy in Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique. Most of the brainprint authentication models were tested in well-controlled environments to minimize the influence of ambient disturbance on the EEG signals. These settings significantly contradict the real-world situations. Thus, making use of the distraction is wiser than eliminating it. The proposed probability-based incremental update strategy is benchmarked with the ground truth (actual class) incremental update strategy. Besides, the proposed technique is also benchmarked with First-In-First-Out (FIFO) incremental update strategy in K-Nearest Neighbour (KNN). The experimental results have shown equivalence discriminatory performance in both high distraction and quiet conditions. This has proven that the proposed distraction descriptor is able to utilize the unique EEG response towards ambient distraction to complement person authentication modelling in uncontrolled environment. The proposed probability-based IncFRNN technique has significantly outperformed the KNN technique for both with and without defining the window size threshold. Nevertheless, its performance is slightly worse than the actual class incremental update strategy since the ground truth represents the gold standard. In overall, this study demonstrated a more practical brainprint authentication model with the proposed distraction descriptor and the probability-based incremental update strategy. However, the EEG distraction descriptor may vary due to intersession variability. Future research may focus on the intersession variability to enhance the robustness of the brainprint authentication model.

Fast changes in default and control network activity underlying intraindividual response time variability in childhood: Does age and sex matter?

Intraindividual response time variability (RTV) is considered as a general marker of neurological health. In adults, the central executive and salience networks (task-positive networks, TPN) and the default mode network (DMN) are critical for RTV. Given that RTV decreases with growing up, and that boys are likely somewhat behind girls with respect to the network development, we aimed to clarify age and sex effects. Electroencephalogram was recorded during Stroop-like test performance in 124 typically developing children aged 5-12 years. Network fluctuations were calculated as changes of current source density (CSD) in regions of interest (ROIs) from pretest to 1-s test interval. In boys, TPN activation (CSD increase in ROIs included in the TPN) was associated with lower RTV, suggesting a greater engagement of attentional control. In children younger than 9.5 years, higher response stability was associated with the predominance of TPN activation over DMN activation (CSD increase in ROIs included in the TPN > that in the DMN); this predominance increased with age, suggesting that variability among younger children may be due to network immaturity. These findings suggest that the TPN and DMN may play different roles within the network mechanisms of RTV in boys and girls and at different developmental stages.

Neural Signatures of Actively Controlled Self-Motion and the Subjective Encoding of Distance

Navigating through an environment requires knowledge about one's direction of self-motion (heading) and traveled distance. Behavioral studies showed that human participants can actively reproduce a previously observed travel distance purely based on visual information. Here, we employed electroencephalography (EEG) to investigate the underlying neural processes. We measured, in human observers, event-related potentials (ERPs) during visually simulated straight-forward self-motion across a ground plane. The participants' task was to reproduce (active condition) double the distance of a previously seen self-displacement (passive condition) using a gamepad. We recorded the trajectories of self-motion during the active condition and played it back to the participants in a third set of trials (replay condition). We analyzed EEG activity separately for four electrode clusters: frontal (F), central (C), parietal (P), and occipital (O). When aligned to self-motion onset or offset, response modulation of the ERPs was stronger, and several ERP components had different latencies in the passive as compared with the active condition. This result is in line with the concept of predictive coding, which implies modified neural activation for self-induced versus externally induced sensory stimulation. We aligned our data also to the times when subjects passed the (objective) single distance d_obj and the (subjective) single distance d_sub. Remarkably, wavelet-based temporal-frequency analyses revealed enhanced theta-band activation for F, P, and O-clusters shortly before passing d_sub. This enhanced activation could be indicative of a navigation related representation of subjective distance. More generally, our study design allows to investigate subjective perception without interfering neural activation because of the required response action.

Behavioral and neurophysiological aspects of working memory impairment in children with dyslexia

The present study aimed to identify behavioral and neurophysiological correlates of dyslexia which could potentially predict reading difficulty. One hundred and three Chinese children with and without dyslexia (Grade 2 or 3, 7- to 11-year-old) completed both verbal and visual working memory (n-back) tasks with concurrent EEG recording. Data of 74 children with sufficient usable EEG data are reported here. Overall, the typically developing control group (N = 28) responded significantly faster and more accurately than the group with dyslexia (N = 46), in both types of tasks. Group differences were also found in EEG band power in the retention phase of the tasks. Moreover, forward stepwise logistic regression demonstrated that both behavioral and neurophysiological measures predicted reading difficulty uniquely. Dyslexia was associated with higher frontal midline theta activity and reduced upper-alpha power in the posterior region. This finding is discussed in relation to the neural efficiency hypothesis. Whether these behavioral and neurophysiological patterns can longitudinally predict later reading development among preliterate children requires further investigation.

Brain Waves Analysis Via a Non-Parametric Bayesian Mixture of Autoregressive Kernels

The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify frequency bands, defined a priori, that have the most substantial relative contributions to the overall variance of the signal. However, a limitation of this approach is that the precise frequency and bandwidth of oscillations are not uniform across different cognitive demands. Thus, these bands should not be arbitrarily set in any analysis. To overcome this limitation, the Bayesian mixture auto-regressive decomposition (BMARD) method is proposed, as a data-driven approach that identifies (i) the number of prominent spectral peaks, (ii) the frequency peak locations, and (iii) their corresponding bandwidths (or spread of power around the peaks). Using the BMARD method, the standardized SDF is represented as a Dirichlet process mixture based on a kernel derived from second-order auto-regressive processes which completely characterize the location (peak) and scale (bandwidth) parameters. A Metropolis-Hastings within the Gibbs algorithm is developed for sampling the posterior distribution of the mixture parameters. Simulations demonstrate the robust performance of the proposed method. Finally, the BMARD method is applied to analyze local field potential (LFP) activity from the hippocampus of laboratory rats across different conditions in a non-spatial sequence memory experiment, to identify the most prominent frequency bands and examine the link between specific patterns of brain oscillatory activity and trial-specific cognitive demands.

Resting and TMS-EEG markers of treatment response in major depressive disorder: A systematic review

Electroencephalography (EEG) is a non-invasive method to identify markers of treatment response in major depressive disorder (MDD). In this review, existing literature was assessed to determine how EEG markers change with different modalities of MDD treatments, and to synthesize the breadth of EEG markers used in conjunction with MDD treatments. PubMed and EMBASE were searched from 2000 to 2021 for studies reporting resting EEG (rEEG) and transcranial magnetic stimulation combined with EEG (TMS-EEG) measures in patients undergoing MDD treatments. The search yielded 966 articles, 204 underwent full-text screening, and 51 studies were included for a narrative synthesis of findings along with confidence in the evidence. In rEEG studies, non-linear quantitative algorithms such as theta cordance and theta current density show higher predictive value than traditional linear metrics. Although less abundant, TMS-EEG measures show promise for predictive markers of brain stimulation treatment response. Future focus on TMS-EEG measures may prove fruitful, given its ability to target cortical regions of interest related to MDD.

Oscillatory and Aperiodic Neural Activity Jointly Predict Language Learning

Memory formation involves the synchronous firing of neurons in task-relevant networks, with recent models postulating that a decrease in low-frequency oscillatory activity underlies successful memory encoding and retrieval. However, to date, this relationship has been investigated primarily with face and image stimuli; considerably less is known about the oscillatory correlates of complex rule learning, as in language. Furthermore, recent work has shown that nonoscillatory (1/ƒ) activity is functionally relevant to cognition, yet its interaction with oscillatory activity during complex rule learning remains unknown. Using spectral decomposition and power-law exponent estimation of human EEG data (17 females, 18 males), we show for the first time that 1/ƒ and oscillatory activity jointly influence the learning of word order rules of a miniature artificial language system. Flexible word-order rules were associated with a steeper 1/ƒ slope, whereas fixed word-order rules were associated with a shallower slope. We also show that increased theta and alpha power predicts fixed relative to flexible word-order rule learning and behavioral performance. Together, these results suggest that 1/ƒ activity plays an important role in higher-order cognition, including language processing, and that grammar learning is modulated by different word-order permutations, which manifest in distinct oscillatory profiles.

EEG Spectral Power Analysis: A Comparison Between Heroin Dependent and Control Groups

Previous studies indicated that heroin abuse would result in abnormal functional organization of the brain. However, studies of heroin abuse- related brain dysfunction are scarce. The purpose of the present study was to investigate heroin effects on brain function by studying relationships between Electroencephalograph (EEG) spectral power and heroin abuse. The resting EEG signals were acquired from 15 male heroin dependent group and 15 male control group. The differences in the EEG components of each group were evaluated using the statistical Mann-Whitney examination and Davis Bouldin Index. The results show that heroin dependent group has an attenuated relative beta-2 power compared with other EEG frequency sub bands. Nevertheless, the results indicate heroin dependent group have an increase of power spectrum density for theta at all locations, as well as delta in the temporal, frontal and central areas compared with control group. Compared to control group, the heroin dependent group decreased its spectral power more than the control group in all three alpha bands. The present findings using the Davis Bouldin Index provide evidence that alpha-3 band in the FZ channel is more affected by heroin abuse than other frequency sub bands.

Electroencephalography Correlates of Well-Being Using a Low-Cost Wearable System

Electroencephalography (EEG) alpha asymmetry is thought to reflect crucial brain processes underlying executive control, motivation, and affect. It has been widely used in psychopathology and, more recently, in novel neuromodulation studies. However, inconsistencies remain in the field due to the lack of consensus in methodological approaches employed and the recurrent use of small samples. Wearable technologies ease the collection of large and diversified EEG datasets that better reflect the general population, allow longitudinal monitoring of individuals, and facilitate real-world experience sampling. We tested the feasibility of using a low-cost wearable headset to collect a relatively large EEG database (N = 230, 22-80 years old, 64.3% female), and an open-source automatic method to preprocess it. We then examined associations between well-being levels and the alpha center of gravity (CoG) as well as trait EEG asymmetries, in the frontal and temporoparietal (TP) areas. Robust linear regression models did not reveal an association between well-being and alpha (8-13 Hz) asymmetry in the frontal regions, nor with the CoG. However, well-being was associated with alpha asymmetry in the TP areas (i.e., corresponding to relatively less left than right TP cortical activity as well-being levels increased). This effect was driven by oscillatory activity in lower alpha frequencies (8-10.5 Hz), reinforcing the importance of dissociating sub-components of the alpha band when investigating alpha asymmetries. Age was correlated with both well-being and alpha asymmetry scores, but gender was not. Finally, EEG asymmetries in the other frequency bands were not associated with well-being, supporting the specific role of alpha asymmetries with the brain mechanisms underlying well-being levels. Interpretations, limitations, and recommendations for future studies are discussed. This paper presents novel methodological, experimental, and theoretical findings that help advance human neurophysiological monitoring techniques using wearable neurotechnologies and increase the feasibility of their implementation into real-world applications.

time-frequency analysis of scalp EEG with Hilbert-Huang transform and deep learning

Electroencephalography (EEG) is a brain imaging approach that has been widely used in neuroscience and clinical settings. The conventional EEG analyses usually require pre-defined frequency bands when characterizing neural oscillations and extracting features for classifying EEG signals. However, neural responses are naturally heterogeneous by showing variations in frequency bands of brainwaves and peak frequencies of oscillatory modes across individuals. Fail to account for such variations might result in information loss and classifiers with low accuracy but high variation across individuals. To address these issues, we present a systematic time-frequency analysis approach for analyzing scalp EEG signals. In particular, we propose a data-driven method to compute the subject-specific frequency bands for brain oscillations via Hilbert-Huang Transform, lifting the restriction of using fixed frequency bands for all subjects. Then, we propose two novel metrics to quantify the power and frequency aspects of brainwaves represented by sub-signals decomposed from the EEG signals. The effectiveness of the proposed metrics are tested on two scalp EEG datasets and compared with four commonly used features sets extracted from wavelet and Hilbert-Huang Transform. The validation results show that the proposed metrics are more discriminatory than other features leading to accuracies in the range of 94.93% to 99.84%. Besides classification, the proposed metrics show great potential in quantification of neural oscillations and serving as biomarkers in the neuroscience research.

The Sample Size Matters: To What Extent the Participant Reduction Affects the Outcomes of a Neuroscientific Research. A Case-Study in Neuromarketing Field

The sample size is a crucial concern in scientific research and even more in behavioural neurosciences, where besides the best practice it is not always possible to reach large experimental samples. In this study we investigated how the outcomes of research change in response to sample size reduction. Three indices computed during a task involving the observations of four videos were considered in the analysis, two related to the brain electroencephalographic (EEG) activity and one to autonomic physiological measures, i.e., heart rate and skin conductance. The modifications of these indices were investigated considering five subgroups of sample size (32, 28, 24, 20, 16), each subgroup consisting of 630 different combinations made by bootstrapping n (n = sample size) out of 36 subjects, with respect to the total population (i.e., 36 subjects). The correlation analysis, the mean squared error (MSE), and the standard deviation (STD) of the indexes were studied at the participant reduction and three factors of influence were considered in the analysis: the type of index, the task, and its duration (time length). The findings showed a significant decrease of the correlation associated to the participant reduction as well as a significant increase of MSE and STD (p < 0.05). A threshold of subjects for which the outcomes remained significant and comparable was pointed out. The effects were to some extents sensitive to all the investigated variables, but the main effect was due to the task length. Therefore, the minimum threshold of subjects for which the outcomes were comparable increased at the reduction of the spot duration.

EEG beta-modulations reflect age-specific motor resource allocation during dual-task walking

The parallel execution of two motor tasks can lead to performance decrements in either one or both of the tasks. Age-related declines can further magnify the underlying competition for cognitive resources. However, little is known about the neural dynamics underlying motor resource allocation during dual-task walking. To better understand motor resource conflicts, this study investigated sensorimotor brain rhythms in younger and older adults using a dual-task protocol. Time-frequency data from two independent component motor clusters were extracted from electroencephalography data during sitting and walking with an additional task requiring manual responses. Button press-related desynchronization in the alpha and beta frequency range were analyzed for the impact of age (< 35 years, ≥ 70 years) and motor task (sitting, walking). Button press-related desynchronization in the beta band was more pronounced for older participants and both age groups demonstrated less pronounced desynchronizations in both frequency bands during walking compared to sitting. Older participants revealed less power modulations between sitting and walking, and less pronounced changes in beta and alpha suppression were associated with greater slowing in walking speed. Our results indicate age-specific allocations strategies during dual-task walking as well as interdependencies of concurrently performed motor tasks reflected in modulations of sensorimotor rhythms.

Calibrating rhythmic stimulation parameters to individual EEG markers: the consistency of individual alpha frequency in practical lab settings

Rhythmic stimulation can be applied to modulate neuronal oscillations. Such 'entrainment' is optimized when stimulation frequency is individually-calibrated based on magneto/encephalography markers. It remains unknown how consistent such individual markers are across days/sessions, within a session, or across cognitive states, hemispheres, and estimation methods, especially in a realistic, practical, lab setting. We here estimated individual alpha frequency (IAF) repeatedly from short EEG measurements at rest or during an attention task (cognitive state), using single parieto-occipital electrodes in 24 participants on four days (between-sessions), with multiple measurements over an hour on one day (within-session). First, we introduce an algorithm to automatically reject power spectra without a sufficiently clear peak to ensure unbiased IAF estimations. Then we estimated IAF via the traditional 'maximum' method and a 'Gaussian fit' method. IAF was reliable within- and between-sessions for both cognitive states and hemispheres, though task-IAF estimates tended to be more variable. Overall, the 'Gaussian fit' method was more reliable than the 'maximum' method. Furthermore, we evaluated how far from an approximated 'true' task-related IAF the selected 'stimulation frequency' was, when calibrating this frequency based on a short rest-EEG, a short task-EEG, or simply selecting 10Hertz for all participants. For the 'maximum' method, rest-EEG calibration was best, followed by task-EEG, and then 10 Hertz. For the 'Gaussian fit' method, rest-EEG and task-EEG-based calibration were similarly accurate, and better than 10 Hertz. These results lead to concrete recommendations about valid, and automated, estimation of individual oscillation markers in experimental and clinical settings.

Modulation of Peak Alpha Frequency Oscillations During Working Memory Is Greater in Females Than Males

Peak alpha frequency is known to vary not just between individuals, but also within an individual over time. While variance in this metric between individuals has been tied to working memory performance, less understood are how short timescale modulations of peak alpha frequency during task performance may facilitate behavior. This gap in understanding may be bridged by consideration of a key difference between individuals: sex. Inconsistent findings in the literature regarding the relationship between peak alpha frequency and cognitive performance, as well as known sex-related-differences in peak alpha frequency and its modulation motivated our hypothesis that cognitive and neural processes underlying working memory-modulation of peak alpha frequency in particular-may differ based upon sex. Targeting sex as a predictive factor, we analyzed the EEG data of participants recorded while they performed four versions of a visual spatial working memory task. A significant difference between groups was present: females modulated peak alpha frequency more than males. Task performance did not differ by sex, yet a relationship between accuracy and peak alpha frequency was present in males, but not in females. These findings highlight the importance of considering sex as a factor in the study of oscillatory activity, particularly to further understanding of the neural mechanisms that underlie working memory.

Neural Correlates of Motor Skill Learning Are Dependent on Both Age and Task Difficulty

Although a general age-related decline in neural plasticity is evident, the effects of age on neural plasticity after motor practice are inconclusive. Inconsistencies in the literature may be related to between-study differences in task difficulty. Therefore, we aimed to determine the effects of age and task difficulty on motor learning and associated brain activity. We used task-related electroencephalography (EEG) power in the alpha (8–12 Hz) and beta (13–30 Hz) frequency bands to assess neural plasticity before, immediately after, and 24-h after practice of a mirror star tracing task at one of three difficulty levels in healthy younger (19–24 yr) and older (65–86 yr) adults. Results showed an age-related deterioration in motor performance that was more pronounced with increasing task difficulty and was accompanied by a more bilateral activity pattern for older vs. younger adults. Task difficulty affected motor skill retention and neural plasticity specifically in older adults. Older adults that practiced at the low or medium, but not the high, difficulty levels were able to maintain improvements in accuracy at retention and showed modulation of alpha TR-Power after practice. Together, these data indicate that both age and task difficulty affect motor learning, as well as the associated neural plasticity.

The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence

Real-world experience is typically multimodal. Evidence indicates that the facilitation in the detection of multisensory stimuli is modulated by the perceptual load, the amount of information involved in the processing of the stimuli. Here, we used a realistic virtual reality environment while concomitantly acquiring Electroencephalography (EEG) and Galvanic Skin Response (GSR) to investigate how multisensory signals impact target detection in two conditions, high and low perceptual load. Different multimodal stimuli (auditory and vibrotactile) were presented, alone or in combination with the visual target. Results showed that only in the high load condition, multisensory stimuli significantly improve performance, compared to visual stimulation alone. Multisensory stimulation also decreases the EEG-based workload. Instead, the perceived workload, according to the "NASA Task Load Index" questionnaire, was reduced only by the trimodal condition (i.e., visual, auditory, tactile). This trimodal stimulation was more effective in enhancing the sense of presence, that is the feeling of being in the virtual environment, compared to the bimodal or unimodal stimulation. Also, we show that in the high load task, the GSR components are higher compared to the low load condition. Finally, the multimodal stimulation (Visual-Audio-Tactile-VAT and Visual-Audio-VA) induced a significant decrease in latency, and a significant increase in the amplitude of the P300 potentials with respect to the unimodal (visual) and visual and tactile bimodal stimulation, suggesting a faster and more effective processing and detection of stimuli if auditory stimulation is included. Overall, these findings provide insights into the relationship between multisensory integration and human behavior and cognition.

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.

EEG Correlates of Learning From Speech Presented in Environmental Noise

How the human brain retains relevant vocal information while suppressing irrelevant sounds is one of the ongoing challenges in cognitive neuroscience. Knowledge of the underlying mechanisms of this ability can be used to identify whether a person is distracted during listening to a target speech, especially in a learning context. This paper investigates the neural correlates of learning from the speech presented in a noisy environment using an ecologically valid learning context and electroencephalography (EEG). To this end, the following listening tasks were performed while 64-channel EEG signals were recorded: (1) attentive listening to the lectures in background sound, (2) attentive listening to the background sound presented alone, and (3) inattentive listening to the background sound. For the first task, 13 lectures of 5 min in length embedded in different types of realistic background noise were presented to participants who were asked to focus on the lectures. As background noise, multi-talker babble, continuous highway, and fluctuating traffic sounds were used. After the second task, a written exam was taken to quantify the amount of information that participants have acquired and retained from the lectures. In addition to various power spectrum-based EEG features in different frequency bands, the peak frequency and long-range temporal correlations (LRTC) of alpha-band activity were estimated. To reduce these dimensions, a principal component analysis (PCA) was applied to the different listening conditions resulting in the feature combinations that discriminate most between listening conditions and persons. Linear mixed-effect modeling was used to explain the origin of extracted principal components, showing their dependence on listening condition and type of background sound. Following this unsupervised step, a supervised analysis was performed to explain the link between the exam results and the EEG principal component scores using both linear fixed and mixed-effect modeling. Results suggest that the ability to learn from the speech presented in environmental noise can be predicted by the several components over the specific brain regions better than by knowing the background noise type. These components were linked to deterioration in attention, speech envelope following, decreased focusing during listening, cognitive prediction error, and specific inhibition mechanisms.

Inter- and Intra-individual Variability in Brain Oscillations During Sports Motor Imagery

The aim of this work was to re-evaluate electrophysiological data from a previous study on motor imagery (MI) with a special focus on observed inter- and intra-individual differences. More concretely, we investigated event-related desynchronization/synchronization patterns during sports MI (playing tennis) compared with simple MI (squeezing a ball) and discovered high variability across participants. Thirty healthy volunteers were divided in two groups; the experimental group (EG) performed a physical exercise between two imagery sessions, and the control group (CG) watched a landscape movie without physical activity. We computed inter-individual differences by assessing the dissimilarities among subjects for each group, condition, time period, and frequency band. In the alpha band, we observe some clustering in the ranking of the subjects, therefore showing smaller distances than others. Moreover, in our statistical evaluation, we observed a consistency in ranking across time periods both for the EG and for the CG. For the latter, we also observed similar rankings across conditions. On the contrary, in the beta band, the ranking of the subjects was more similar for the EG across conditions and time periods than for the subjects of the CG. With this study, we would like to draw attention to variability measures instead of primarily focusing on the identification of common patterns across participants, which often do not reflect the whole neurophysiological reality.

Investigating brain electrical activity and functional connectivity in adolescents with clinically elevated levels of ADHD symptoms in alpha frequency band

EEG measures such as power and connectivity have been widely used to investigate the neuronal underpinnings of ADHD. Traditionally, the fixed band analysis, in which a single frequency band is applied to all the subjects, has been used to estimate these EEG measures. However, there are important interindividual differences in the predominant frequency of alpha-band oscillations. In this study, we present an individualized estimate of EEG in the alpha band and compared the results with traditional fixed band analysis. We also examined the EEG profile separately in lower and upper alpha bands. We further examined the association between EEG measures and ADHD symptoms. Eyes closed resting EEG was collected from 21 adolescents with clinically elevated levels of ADHD and 21 age and gender matched control subjects. Spectral power and connectivity were computed in lower and upper alpha bands. Results revealed a dissociation between upper and lower alpha band power and connectivity in ADHD. The ADHD group showed reduced power and connectivity in the lower alpha band and an elevation of upper alpha power compared to the Control group. EEG power in the lower alpha band was negatively associated with ADHD severity. Our results, however, did not provide conclusive evidence for IAF as an overall greater measure of EEG compared to the traditional fixed band method.

Effect of Self-Controlled Practice on Neuro-Cortical Dynamics During the Processing of Visual Performance Feedback

Evidence has accumulated that learners participating in self-controlled practice can both acquire skills and process task-relevant information more effectively than those participating in externally controlled practice. However, the impact of self-controlled practice on neuro-cognitive information processing during visual performance-related feedback has received limited investigation. We expected that individuals participating in self-controlled practice would exhibit elevated neuro-cognitive information processing, as assessed via electroencephalography (EEG), compared with those engaged with externally controlled practice. Participants practiced a golf-putting task under self-controlled or externally controlled (yoked) conditions while EEG data were recorded. Results indicated that EEG theta power was maintained at an elevated level during the feedback period in the self-controlled group relative to the yoked group. The yoked group did not display increases in theta power until the time at which the ball stopped. Both groups displayed similar improvement over the course of the experiment. Correlational analyses revealed that performance improvement within each group was related differently to EEG theta power. Specifically, the self-controlled group displayed positive relationships between theta power and performance improvement, while the yoked group displayed negative relationships. These results have implications regarding the relative effectiveness of self-controlled and externally controlled practice and the instances in which they may provide the most benefit.

Bilingual Language Experience Shapes Resting-State Brain Rhythms

An increasing body of research has investigated how bilingual language experience changes brain structure and function, including changes to task-free, or "resting-state" brain connectivity. Such findings provide important evidence about how the brain continues to be shaped by different language experiences throughout the lifespan. The neural effects of bilingual language experience can provide evidence about the additional processing demands placed on the linguistic and/or executive systems by dual-language use. While considerable research has used MRI to examine where these changes occur, such methods cannot reveal the temporal dynamics of functioning brain networks at rest. The current study used data from task-free EEGS to disentangle how the linguistic and cognitive demands of bilingual language use impact brain functioning. Data analyzed from 106 bilinguals and 91 monolinguals revealed that bilinguals had greater alpha power, and significantly greater and broader coherence in the alpha and beta frequency ranges than monolinguals. Follow-up analyses showed that higher alpha was related to language control: more second-language use, higher native-language proficiency, and earlier age of second-language acquisition. Bilateral beta power was related to native-language proficiency, whereas theta was related to native-language proficiency only in left-hemisphere electrodes. The results contribute to our understanding of how the linguistic and cognitive requirements of dual-language use shape intrinsic brain activity, and what the broader implications for information processing may be.

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.

On the (in)efficacy of motor imagery training without feedback and event-related desynchronizations considerations

Motor imagery (MI) constitutes a recurrent strategy for signals generation in brain-computer interfaces (BCIs) - systems that aim to control external devices by directly associating brain responses to distinct commands. Although great improvement has been achieved in MI-BCIs performance over recent years, they still suffer from inter- and intra-subject variability issues. As an attempt to cope with this, some studies have suggested that MI training should aid users to appropriately modulate their response for BCI usage: generally, this training is performed based on the sensorimotor rhythms' modulation over the primary sensorimotor cortex (PMC), with the signal being feedbacked to the user. Nonetheless, recent studies have revisited the actual involvement of the PMC into MI, and little to no attention has been devoted to understanding the participation of other cortical areas into training protocols. Therefore, in this work, our aim was to analyze the response induced by hands MI of 10 healthy subjects in the form of event-related desynchronizations (ERDs) and to assess whether features from beyond the PMC might be useful for hands MI classification. We investigated how this response occurs for distinct frequency intervals between 7-30 Hz, and ex0plored changes in their evocation pattern across 12 MI training sessions without feedback. Overall, we found that ERD patterns occur differently for the frequencies encompassed by the μ and β bands, with its evocation being favored for the first band. Over time, the no-feedback approach was inefficient to aid in enhancing ERD evocation (EO). Moreover, to some extent, EO tends to decrease over blocks within a given run, and runs within an MI session, but remains stable within an MI block. We also found that the C3/C4 pair is not necessarily optimal for data classification, and both spectral and spatial subjects' specificities should be considered when designing training protocols.

Understanding the interplay of sleep and aging: Methodological challenges

In quest of new avenues to explain, predict, and treat pathophysiological conditions during aging, research on sleep and aging has flourished. Despite the great scientific potential to pinpoint mechanistic pathways between sleep, aging, and pathology, only little attention has been paid to the suitability of analytic procedures applied to study these interrelations. On the basis of electrophysiological sleep and structural brain data of healthy younger and older adults, we identify, illustrate, and resolve methodological core challenges in the study of sleep and aging. We demonstrate potential biases in common analytic approaches when applied to older populations. We argue that uncovering age-dependent alterations in the physiology of sleep requires the development of adjusted and individualized analytic procedures that filter out age-independent interindividual differences. Age-adapted methodological approaches are thus required to foster the development of valid and reliable biomarkers of age-associated cognitive pathologies.

Empirical Frequency Band Analysis of Nonstationary Time Series

The time-varying power spectrum of a time series process is a bivariate function that quantifies the magnitude of oscillations at different frequencies and times. To obtain low-dimensional, parsimonious measures from this functional parameter, applied researchers consider collapsed measures of power within local bands that partition the frequency space. Frequency bands commonly used in the scientific literature were historically derived, but they are not guaranteed to be optimal or justified for adequately summarizing information from a given time series process under current study. There is a dearth of methods for empirically constructing statistically optimal bands for a given signal. The goal of this article is to provide a standardized, unifying approach for deriving and analyzing customized frequency bands. A consistent, frequency-domain, iterative cumulative sum based scanning procedure is formulated to identify frequency bands that best preserve nonstationary information. A formal hypothesis testing procedure is also developed to test which, if any, frequency bands remain stationary. The proposed method is used to analyze heart rate variability of a patient during sleep and uncovers a refined partition of frequency bands that best summarize the time-varying power spectrum.

The Evaluation of Creative Ideas in Older and Younger Adults

Abstract. The ability to solve problems of divergent type is one of the most intact functions in successful aging. However, neurophysiologic mechanisms that support the efficiency of creative thinking remain largely unknown. This study was aimed to investigate age-related difference in localized induced electroencephalogram (EEG) changes during creative idea evaluation stage of divergent problem-solving (Alternate Uses Task), using standardized low-resolution brain electromagnetic tomography. Younger (45 women, 44 men, Mage = 22.1 years, age range: 18–30 years) and older adults (46 women, 43 men, Mage = 64.9 years, age range: 55–75 years) participated in the study. Higher synchronization in individually adjusted theta frequency band [from (individual alpha peak frequency −6 Hz) to (individual alpha peak frequency −4 Hz)] in anterior areas with the maximum values in anterior cingulate gyrus was revealed in older as compared with younger participants by group contrast. Higher desynchronization in wide beta range [from (individual alpha peak frequency +2 Hz) to 30 Hz] was localized in posterior brain regions with the highest values in posterior cingulate gyrus, precuneus, and parietal lobule in older adults. Induced beta 2 synchronization was positively correlated with originality (as measured by the mean frequency of ideas) in younger and years of education in older subjects. Based on the data, it was supposed that controlling the decision-making processes is more important for older adults while maintenance of the internal image of elements’ recombination may play essential role for younger subjects.

Multimodal alterations of directed connectivity profiles in patients with attention-deficit/hyperactivity disorders

Functional and effective connectivity measures for tracking brain region interactions that have been investigated using both electroencephalography (EEG) and magnetoencephalography (MEG) bringing up new insights into clinical research. However, the differences between these connectivity methods, especially at the source level, have not yet been systematically studied. The dynamic characterization of coherent sources and temporal partial directed coherence, as measures of functional and effective connectivity, were applied to multimodal resting EEG and MEG data obtained from 11 young patients (mean age 13.2 ± 1.5 years) with attention-deficit/hyperactivity disorder (ADHD) and age-matched healthy subjects. Additionally, machine-learning algorithms were applied to the extracted connectivity features to identify biomarkers differentiating the two groups. An altered thalamo-cortical connectivity profile was attested in patients with ADHD who showed solely information outflow from cortical regions in comparison to healthy controls who exhibited bidirectional interregional connectivity in alpha, beta, and gamma frequency bands. We achieved an accuracy of 98% by combining features from all five studied frequency bands. Our findings suggest that both types of connectivity as extracted from EEG or MEG are sensitive methods to investigate neuronal network features in neuropsychiatric disorders. The connectivity features investigated here can be further tested as biomarkers of ADHD.

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

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

EEG cross-frequency correlations as a marker of predisposition to affective disorders

EEG cross-frequency amplitude-amplitude correlation (CF-AAC) has been considered as a potential marker of social anxiety and other affective disturbances. Functional significance of this phenomenon remains unclear, partly because the majority of studies used channel-level analysis, which precluded the spatial localization of observed effects. It is not also clear whether CF-AAC may serve as a marker of specific pathological conditions and specific states, or a more general predisposition to affective disturbances. We used source-level analysis of EEG data obtained in resting conditions in a nonclinical sample and patients with major depressive disorder (MDD) and investigated associations of CF-AAC measures with a broad range of known risk factors for affective disorders, including age, gender, genotype, stress exposure, personality, and self-reported ‘neurotic’ symptomatology. A consistent pattern of associations showed that all investigated risk factors were associated with an enhancement of CF-AAC in cortical regions associated with emotional and self-referential processing. It could be concluded that CF-AAC is a promising candidate marker of a general predisposition to affective disorders at preclinical stages.

Self-Controlled Practice to Achieve Neuro-Cognitive Engagement: Underlying Brain Processes to Enhance Cognitive-Motor Learning and Performance

While self-controlled practice has been shown to be an effective practice methodology, the neuro-cognitive correlates of its effectiveness are unclear. We investigated whether learners participating in self-controlled practice exhibit increased neuro-cognitive engagement compared to externally controlled practice. Two groups (self-controlled and yoked) of 16 participants practiced and performed a golf putting task over 3 days. Working memory engagement, central executive activity, and cortical activation were assessed via electroencephalography as indicators of neuro-cognitive engagement. The self-controlled group exhibited more consistent working memory engagement, and greater central executive activity, compared to the yoked group during practice. Relationships were also observed between neuro-cognitive engagement during self-controlled practice and performance improvement, indicating that self-controlled practice uniquely benefitted from increased neuro-cognitive engagement.

Brain-Computer Interface-Based Adaptive Automation to Prevent Out-Of-The-Loop Phenomenon in Air Traffic Controllers Dealing With Highly Automated Systems

Increasing the level of automation in air traffic management is seen as a measure to increase the performance of the service to satisfy the predicted future demand. This is expected to result in new roles for the human operator: he will mainly monitor highly automated systems and seldom intervene. Therefore, air traffic controllers (ATCos) would often work in a supervisory or control mode rather than in a direct operating mode. However, it has been demonstrated how human operators in such a role are affected by human performance issues, known as Out-Of-The-Loop (OOTL) phenomenon, consisting in lack of attention, loss of situational awareness and de-skilling. A countermeasure to this phenomenon has been identified in the adaptive automation (AA), i.e., a system able to allocate the operative tasks to the machine or to the operator depending on their needs. In this context, psychophysiological measures have been highlighted as powerful tool to provide a reliable, unobtrusive and real-time assessment of the ATCo's mental state to be used as control logic for AA-based systems. In this paper, it is presented the so-called "Vigilance and Attention Controller", a system based on electroencephalography (EEG) and eye-tracking (ET) techniques, aimed to assess in real time the vigilance level of an ATCo dealing with a highly automated human-machine interface and to use this measure to adapt the level of automation of the interface itself. The system has been tested on 14 professional ATCos performing two highly realistic scenarios, one with the system disabled and one with the system enabled. The results confirmed that (i) long high automated tasks induce vigilance decreasing and OOTL-related phenomena; (ii) EEG measures are sensitive to these kinds of mental impairments; and (iii) AA was able to counteract this negative effect by keeping the ATCo more involved within the operative task. The results were confirmed by EEG and ET measures as well as by performance and subjective ones, providing a clear example of potential applications and related benefits of AA.

THE POWER OF NEURAL OSCILLATIONS TO INFORM SENTENCE COMPREHENSION: A LINGUISTIC PERSPECTIVE

The field of psycholinguistics is currently experiencing an explosion of interest in the analysis of neural oscillations - rhythmic brain activity synchronized at different temporal and spatial levels. Given that language comprehension relies on a myriad of processes, which are carried out in parallel in distributed brain networks, there is hope that this methodology might bring the field closer to understanding some of the more basic (spatially and temporally distributed, yet at the same time often overlapping) neural computations that support language function. In this review we discuss existing proposals linking oscillatory dynamics in different frequency bands to basic neural computations, and review relevant theories suggesting associations between band-specific oscillations and higher-level cognitive processes. More or less consistent patterns of oscillatory activity related to certain types of linguistic processing can already be derived from the evidence that has accumulated over the past few decades. The centerpiece of the current review is a synthesis of such patterns grouped by linguistic phenomenon. We restrict our review to evidence linking measures of oscillatory power to the comprehension of sentences, as well as linguistically (and/or pragmatically) more complex structures. For each grouping, we provide a brief summary and a table of associated oscillatory signatures that a psycholinguist might expect to find when employing a particular linguistic task. Summarizing across different paradigms, we conclude that a handful of basic neural oscillatory mechanisms are likely recruited in different ways and at different times for carrying out a variety of linguistic computations.

Hilbert spectral analysis of EEG data reveals spectral dynamics associated with microstates

BACKGROUND

This study addresses an ongoing debate, i.e. whether microstates have a relation to specific oscillations or frequency bands. The previous literature on this has been inconclusive. Due to stochastic calculation of microstates it is important to address this issue because instead of providing further insights, it might lead us to ambiguous interpretations.

NEW METHOD

Here we propose a new method that allows to remove the time-frequency trade-off, which hampered previous works, using Empirical Mode Decomposition (EMD) and the AM-FM model. The method is applied to two resting-state EEG datasets.

RESULTS

First, our analysis confirmed that, indeed, when overlooking time-dependence in frequency domain, the results are inconclusive and consequently, highlighted the importance of preserving time-information in the spectral domain. Second, it is confirmed using synthetic data that the local peaks in global field potential (GFP) waveform are influenced by spectral powers present in composite signals. Based on synthetic results, it is inferred that in our dataset, an average frequency range of 10-15 Hz dominates the formation and the temporal dynamics of microstates. Third, it is shown that multiple overlapping patterns of synchronized activities described by a single meta-process in full band microstate studies can be identified using the proposed frequency-band subdivision. The results are consistent across both datasets.

CONCLUSION

This study opens several new ventures to be explored in the future: e.g. analysis of temporally overlapping patterns described so far by single topographic patterns, which we show to be spectrally differentiable via band-wise topographic segmentation proposed in the present study.

Neural anticipatory mechanisms predict faster reaction times and higher fluid intelligence

Higher cognitive ability is reliably linked to better performance on chronometric tasks (i.e., faster reaction times, RT), yet the neural basis of these effects remains unclear. Anticipatory processes represent compelling yet understudied potential mechanisms of these effects, which may facilitate performance through reducing the uncertainty surrounding the temporal onset of stimuli (temporal uncertainty) and/or facilitating motor readiness despite uncertainty about impending target locations (target uncertainty). Specifically, the contingent negative variation (CNV) represents a compelling candidate mechanism of anticipatory motor planning, while the alpha oscillation is thought to be sensitive to temporal contingencies in perceptual systems. The current study undertook a secondary analysis of a large data set (n = 91) containing choice RT, cognitive ability, and EEG measurements to help clarify these issues. Single-trial EEG analysis in conjunction with mixed-effects modeling revealed that higher fluid intelligence corresponded to faster RT on average. When considered together, temporal and target uncertainty moderated the RT-ability relationship, with higher ability being associated with greater resilience to both types of uncertainty. Target uncertainty attenuated the amplitude of the CNV for all participants, but higher ability individuals were more resilient to this effect. Similarly, only higher ability individuals showed increased prestimulus alpha power (at left-lateralized sites) during longer, more easily anticipated interstimulus intervals. Collectively, these findings emphasize top-down anticipatory processes as likely contributors to chronometry-ability correlations.

Cross-Frequency Coupling in Developmental Perspective

It is generally assumed that different electroencephalogram (EEG) frequency bands are somehow related to different computational modes in the brain. Integration of these computational modes is reflected in the phenomenon of cross-frequency coupling (CFC). On slow temporal scales, CFC may reflect trait-like properties, which posits a question of its developmental trends. This is the first study that explored source-level CFC measures in a developmental perspective using both cross-sectional and longitudinal designs. CFC measures demonstrated good test-retest stability and proved to be higher in adults in cortical areas participating in sensory-motor integration, response inhibition, and attentional control. In children, greater CFC was observed in parietal regions involved in self-centered cognition. Over the period from 7 to 10 years, CFC demonstrated nonlinear growth trajectories. Introversion was associated with higher CFC in cortical areas related to emotion, attention, and social cognition, implying that the association between introversion and CFC appears early in the development.

The Dry Revolution: Evaluation of Three Different EEG Dry Electrode Types in Terms of Signal Spectral Features, Mental States Classification and Usability

One century after the first recording of human electroencephalographic (EEG) signals, EEG has become one of the most used neuroimaging techniques. The medical devices industry is now able to produce small and reliable EEG systems, enabling a wide variety of applications also with no-clinical aims, providing a powerful tool to neuroscientific research. However, these systems still suffer from a critical limitation, consisting in the use of wet electrodes, that are uncomfortable and require expertise to install and time from the user. In this context, dozens of different concepts of EEG dry electrodes have been recently developed, and there is the common opinion that they are reaching traditional wet electrodes quality standards. However, although many papers have tried to validate them in terms of signal quality and usability, a comprehensive comparison of different dry electrode types from multiple points of view is still missing. The present work proposes a comparison of three different dry electrode types, selected among the main solutions at present, against wet electrodes, taking into account several aspects, both in terms of signal quality and usability. In particular, the three types consisted in gold-coated single pin, multiple pins and solid-gel electrodes. The results confirmed the great standards achieved by dry electrode industry, since it was possible to obtain results comparable to wet electrodes in terms of signals spectra and mental states classification, but at the same time drastically reducing the time of montage and enhancing the comfort. In particular, multiple-pins and solid-gel electrodes overcome gold-coated single-pin-based ones in terms of comfort.

Effects of mirror-box therapy on modulation of sensorimotor EEG oscillatory rhythms: a single-case longitudinal study

We provide direct electrophysiological evidence that mirror therapy (MT) can change brain activity and aid in the recovery of motor function after stroke. In this longitudinal single-case study, the subject was a 58-yr-old man with right-hand hemiplegia due to ischemic stroke. Over a 9-mo period we treated him with MT twice a week and measured electroencephalograms (EEG) before, during, and after each therapy session. Using advanced signal processing methods, we identified five distinct movement-related oscillatory EEG components: one slow component designated as mu rhythm and four faster components designated as sensorimotor rhythms. Results show that MT produced long-term changes of two oscillatory EEG components including the mu rhythm, which is a well-documented correlate of voluntary movement in the frequency range of 7.5-12 Hz. Specifically, MT was significantly associated with an increase in the power of mu rhythm recorded over both hemispheres and a decrease in the power of one sensorimotor component recorded over the affected hemisphere. To obtain robust, repeatable individual measures of EEG components suitable for longitudinal study, we used irregular-resampling autospectral analysis to separate fractal and oscillatory components in the EEG power spectrum and three-way parallel factor analysis to isolate oscillatory EEG components and track their activations over time. The rhythms were identified over individual days of MT training and were clearly related to the periods of event-related desynchronization and synchronization (rest, observe, and move) during MT. Our results are consistent with a model in which MT promotes recovery of motor function by altering neural activity associated with voluntary movement. NEW & NOTEWORTHY We provide novel evidence that mirror therapy (MT), which helps in the recovery of motor function after a stroke, is also associated with long-lasting changes in brain electrical activity. Using precise measurements of oscillatory EEG components over a 9-mo period in a victim of ischemic stroke, we showed that MT produced long-term increases in the mu rhythm recorded over both hemispheres and a decrease in a sensorimotor EEG component recorded over the affected hemisphere.

Frequency Specific Cortical Dynamics During Motor Imagery Are Influenced by Prior Physical Activity

Motor imagery is often used inducing changes in electroencephalographic (EEG) signals for imagery-based brain-computer interfacing (BCI). A BCI is a device translating brain signals into control signals providing severely motor-impaired persons with an additional, non-muscular channel for communication and control. In the last years, there is increasing interest using BCIs also for healthy people in terms of enhancement or gaming. Most studies focusing on improving signal processing feature extraction and classification methods, but the performance of a BCI can also be improved by optimizing the user's control strategies, e.g., using more vivid and engaging mental tasks for control. We used multichannel EEG to investigate neural correlates of a sports imagery task (playing tennis) compared to a simple motor imagery task (squeezing a ball). To enhance the vividness of both tasks participants performed a short physical exercise between two imagery sessions. EEG was recorded from 60 closely spaced electrodes placed over frontal, central, and parietal areas of 30 healthy volunteers divided in two groups. Whereas Group 1 (EG) performed a physical exercise between the two imagery sessions, Group 2 (CG) watched a landscape movie without physical activity. Spatiotemporal event-related desynchronization (ERD) and event-related synchronization (ERS) patterns during motor imagery (MI) tasks were evaluated. The results of the EG showed significant stronger ERD patterns in the alpha frequency band (8-13 Hz) during MI of tennis after training. Our results are in evidence with previous findings that MI in combination with motor execution has beneficial effects. We conclude that sports MI combined with an interactive game environment could be a future promising task in motor learning and rehabilitation improving motor functions in late therapy processes or support neuroplasticity.

Continuous Wavelet Transform for Decoding Finger Movements From Single-Channel EEG

Human body movements can be reflected in brain signals and collected noninvasively with electroencephalography (EEG). Motor-related signals include sensory motor rhythms (also known as the Mu wave) in the upper-alpha band of 8-13 Hz and slow cortical potentials (SCPs) in the low frequency range of 0.1-5 Hz. This study compares the two signals for decoding finger movements. Human subjects were asked to individually lift each of the five digits of their right hand, at the rate of one every 10 s. EEG was recorded using a bipolar montage between ipsilateral and contralateral motor cortices. Electromyograms were obtained for identifying movement onsets. Linear discriminant analysis (LDA) generated significant performance with SCPs but not with Mu. Meanwhile, continuous wavelet transform (CWT) was applied to SCPs or Mu to create a spectrogram for each finger, showing distinctive amplitude and phase patterns. A dprime-based weighting algorithm was used to extract time-frequency features. With a template-matching paradigm, both SCP and Mu spectrograms yielded significant classification accuracies for multiple subjects, with the highest being >50% (chance = 20%). Interestingly, the index finger was better distinguished with Mu for most of the subjects, whereas the ring finger was better distinguished with SCPs. The CWT algorithm outperformed LDA by better decoding the thumb. This study suggests that the time-frequency characteristics of a single-channel EEG, when phase is preserved, contain critical information on finger movements. SCPs and Mu seem to work in an independent but complementary manner.