The increase in theta/beta ratio on resting-state EEG in boys with attention-deficit/hyperactivity disorder is mediated by slow alpha peak frequency

The Impact of Neurofeedback Training on Cognitive Abilities Assessed by the Wechsler Intelligence Scale for Children-Revised in Children with Attention Deficit: A Randomized Single-Blind Sham-Controlled Study

Aim: This study aims to investigate the effects of a neurofeedback system on cognitive skills, as measured by the Wechsler Intelligence Scale for Children-Revised (WISC-R), in a cohort of 100 children aged 8 to 12 who were diagnosed with attention deficit.Materials and Methods: A randomized single-blind sham control group design was employed, with 50 participants assigned to the experimental group receiving neurofeedback training and 50 participants assigned to the sham group receiving simulated training. Participants were selected through random sampling from individuals seeking assistance at a specialized education center over the course of one year (May 2021-2022). Pre- and post-test WISC-R assessments were administered to both groups to evaluate participants' mental performance. The experimental group underwent a total of 60 sessions of quantitative electroencephalography-based infralow frequency neurofeedback training, with half-hour sessions conducted three days a week over a five-month period. The post-test WISC-R was administered at the end of the sixth month.Results: The results revealed significant differences between the pre- and post-training test scores, specifically in terms of verbal IQ, picture arrangement, performance IQ, and total IQ (p = 0.016, p = 0.001, p < 0.001, and p = 0.002, respectively), when comparing the differences between the two groups.Conclusion: These findings indicate a notable improvement in performance IQ, total IQ, and a reduction in attention deficits among the neurofeedback group based on the WISC-R assessments. Future studies should consider employing larger sample sizes, including appropriate control groups, and conducting long-term follow-ups to further elucidate the clinical significance of these results.

Role of visual brainwave entrainment on the resting state brainwaves of children with and without attention-deficit/hyperactivity disorder

The relationship between brainwave oscillations and Attention-Deficit/Hyperactivity Disorder (ADHD)-related cognitive challenges is a trending proposition in the field of Cognitive Neuroscience. Studies suggest the role of brainwave oscillations in the symptom expressions of ADHD-diagnosed children. Intervention studies have further suggested the scope of brain stimulation techniques in improving cognition. The current manuscript explored the effect of changes in the brainwaves post-sensory entrainment on cognitive performance of children. We calculated each participant's brainwave difference and ratios of theta, alpha, and beta power after the entrainment sessions. Further, we explored possible correlations between these values and the psychometric scores. The beta resting state showed the strongest association with selective attention performance of all participants. Theta-beta ratio (TBR) showed an inverse correlation with selective attention and working memory performances. The theta frequency was associated with decreased working performance in children without ADHD. Our findings also suggest a predominant role of TBR than the theta-alpha ratio in determining the cognitive performance of children with ADHD. The individual differences in the entrainment reception were attributed to the participant's age, IQ, and their innate baseline frequencies. The implications of our findings can initiate substantiating brainwave-based entrainment sessions as a therapeutic modality to improve cognition among children.

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.

Aperiodic and periodic components of oscillatory brain activity in relation to cognition and symptoms in pediatric ADHD

Children with attention-deficit/hyperactivity disorder show deficits in processing speed, as well as aberrant neural oscillations, including both periodic (oscillatory) and aperiodic (1/f-like) activity, reflecting the pattern of power across frequencies. Both components were suggested as underlying neural mechanisms of cognitive dysfunctions in attention-deficit/hyperactivity disorder. Here, we examined differences in processing speed and resting-state-Electroencephalogram neural oscillations and their associations between 6- and 12-year-old children with (n = 33) and without (n = 33) attention-deficit/hyperactivity disorder. Spectral analyses of the resting-state EEG signal using fast Fourier transform revealed increased power in fronto-central theta and beta oscillations for the attention-deficit/hyperactivity disorder group, but no differences in the theta/beta ratio. Using the parameterization method, we found a higher aperiodic exponent, which has been suggested to reflect lower neuronal excitation-inhibition, in the attention-deficit/hyperactivity disorder group. While fast Fourier transform-based theta power correlated with clinical symptoms for the attention-deficit/hyperactivity disorder group only, the aperiodic exponent was negatively correlated with processing speed across the entire sample. Finally, the aperiodic exponent was correlated with fast Fourier transform-based beta power. These results highlight the different and complementary contribution of periodic and aperiodic components of the neural spectrum as metrics for evaluation of processing speed in attention-deficit/hyperactivity disorder. Future studies should further clarify the roles of periodic and aperiodic components in additional cognitive functions and in relation to clinical status.

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.

Longitudinal Stability of Neural Correlates of Pediatric Attention Deficit Hyperactivity Disorder: A Pilot Study of Event Related Potentials and Electroencephalography

OBJECTIVE

Stability and developmental effects of electroencephalography (EEG) and event related potential (ERP) correlates of ADHD are understudied. This pilot study examined stability and developmental changes in ERP and EEG metrics of interest.

METHODS

Thirty-seven 7 to 11-year-old children with ADHD and 15 typically developing (TD) children completed EEG twice, 11 to 36 months apart. A series of mixed effects linear models were run to examine stability and developmental effects of EEG and ERP metrics.

RESULTS

Stability and developmental effects of EEG and ERP correlates of ADHD varied considerably across metrics. P3 amplitude was stable over time and showed diverging developmental trajectories across groups. Developmental differences were apparent in error related ERPs and resting aperiodic exponent. Theta-beta ratio was stable over time among all children.

CONCLUSIONS

Developmental trajectories of EEG and ERP correlates of ADHD are candidate diagnostic markers. Replication with larger samples is needed.

Aberrant oscillatory activity in neurofibromatosis type 1: an EEG study of resting state and working memory

BACKGROUND

Neurofibromatosis type 1 (NF1) is a genetic neurodevelopmental disorder commonly associated with impaired cognitive function. Despite the well-explored functional roles of neural oscillations in neurotypical populations, only a limited number of studies have investigated oscillatory activity in the NF1 population.

METHODS

We compared oscillatory spectral power and theta phase coherence in a paediatric sample with NF1 (N = 16; mean age: 13.03 years; female: n = 7) to an age/sex-matched typically developing control group (N = 16; mean age: 13.34 years; female: n = 7) using electroencephalography measured during rest and during working memory task performance.

RESULTS

Relative to typically developing children, the NF1 group displayed higher resting state slow wave power and a lower peak alpha frequency. Moreover, higher theta power and frontoparietal theta phase coherence were observed in the NF1 group during working memory task performance, but these differences disappeared when controlling for baseline (resting state) activity.

CONCLUSIONS

Overall, results suggest that NF1 is characterised by aberrant resting state oscillatory activity that may contribute towards the cognitive impairments experienced in this population.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03310996 (first posted: October 16, 2017).

Transcranial random noise stimulation combined with cognitive training for treating ADHD: a randomized, sham-controlled clinical trial

Non-invasive brain stimulation has been suggested as a potential treatment for improving symptomology and cognitive deficits in Attention-Deficit/Hyperactivity Disorder (ADHD), the most common childhood neurodevelopmental disorder. Here, we examined whether a novel form of stimulation, high-frequency transcranial random noise stimulation (tRNS), applied with cognitive training (CT), may impact symptoms and neural oscillations in children with ADHD. We conducted a randomized, double-blind, sham-controlled trial in 23 unmedicated children with ADHD, who received either tRNS over the right inferior frontal gyrus (rIFG) and left dorsolateral prefrontal cortex (lDLPFC) or sham stimulation for 2 weeks, combined with CT. tRNS + CT yielded significant clinical improvements (reduced parent-reported ADHD rating-scale scores) following treatment, compared to the control intervention. These improvements did not change significantly at a 3-week follow-up. Moreover, resting state (RS)-EEG periodic beta bandwidth of the extracted peaks was reduced in the experimental compared to control group immediately following treatment, with further reduction at follow-up. A lower aperiodic exponent, which reflects a higher cortical excitation/inhibition (E/I) balance and has been related to cognitive improvement, was seen in the experimental compared to control group. This replicates previous tRNS findings in adults without ADHD but was significant only when using a directional hypothesis. The experimental group further exhibited longer sleep onset latencies and more wake-up times following treatment compared to the control group. No significant group differences were seen in executive functions, nor in reported adverse events. We conclude that tRNS + CT has a lasting clinical effect on ADHD symptoms and on beta activity. These results provide a preliminary direction towards a novel intervention in pediatric ADHD.

Human neuronal excitation/inhibition balance explains and predicts neurostimulation induced learning benefits

Previous research has highlighted the role of the excitation/inhibition (E/I) ratio for typical and atypical development, mental health, cognition, and learning. Other research has highlighted the benefits of high-frequency transcranial random noise stimulation (tRNS)-an excitatory form of neurostimulation-on learning. We examined the E/I as a potential mechanism and studied whether tRNS effect on learning depends on E/I as measured by the aperiodic exponent as its putative marker. In addition to manipulating E/I using tRNS, we also manipulated the level of learning (learning/overlearning) that has been shown to influence E/I. Participants (n = 102) received either sham stimulation or 20-minute tRNS over the dorsolateral prefrontal cortex (DLPFC) during a mathematical learning task. We showed that tRNS increased E/I, as reflected by the aperiodic exponent, and that lower E/I predicted greater benefit from tRNS specifically for the learning task. In contrast to previous magnetic resonance spectroscopy (MRS)-based E/I studies, we found no effect of the level of learning on E/I. A further analysis using a different data set suggest that both measures of E/I (EEG versus MRS) may reflect, at least partly, different biological mechanisms. Our results highlight the role of E/I as a marker for neurostimulation efficacy and learning. This mechanistic understanding provides better opportunities for augmented learning and personalized interventions.

Classification of children with ADHD through task-related EEG recordings via Swarm-Decomposition-based Phase Locking Value. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-5. [PMID: 38082916 DOI: 10.1109/embc40787.2023.10340329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Attention Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder mainly affecting children. ADHD children brain activity is reported to present alterations from neurotypically developed children, yet establishment of an EEG biomarker, which is of high importance in clinical practice and research, has not been achieved. In this work, task-related EEG recordings from 61 ADHD and 60 age-matched non-ADHD children are analyzed to examine the underlying Cross-Frequency Coupling phenomena. The proposed framework introduces personalized brain rhythm extraction in the form of oscillatory modes via Swarm Decomposition, allowing for the transition from sensor-level connectivity to source-level connectivity. Oscillatory modes are then subjected to a phase locking value-based feature extraction and the efficiency of the extracted features in separating ADHD from non-ADHD individuals is evaluated by means of a nested 5-fold cross validation scheme. The experimental results of the proposed framework (Area Under the Receiver Operating Characteristics Curve-AUROC: 0.9166) when benchmarked against the commonly used filter-based brain rhythm extraction (AUROC: 0.8361) underscore its efficiency and demonstrate its overall superiority over other state-of-the-art functional connectivity approaches in this classification task for this dataset.Clinical relevance-This framework provides novel insights about brain regions of interest that are involved in ADHD task-related function and holds promise in providing objective ADHD biomarkers by extending classic sensor-level connectivity to source-level.

Exploration of electroencephalogram response to MPH treatment in ADHD patients

Attention-deficit/hyperactivity disorder (ADHD) is known to be associated with several diagnostic resting-state electroencephalography (EEG) patterns, including the theta/beta ratio, but no objective predictive markers for each medication. In this study, we explored EEG markers with which the therapeutic efficacy of medications could be estimated at the 1st clinical visit. Thirty-two ADHD patients and thirty-one healthy subjects participated in this study. EEG was recorded during eyes-closed resting conditions, and ADHD symptoms were scored before and after the therapeutic intervention (8 ± 2 weeks). Although comparing EEG patterns between ADHD patients and healthy subjects showed significant differences, EEG dynamics, e.g., theta/beta ratio, in ADHD patients before and after MPH treatment were not significantly different despite improvements in ADHD symptoms. We demonstrated that MPH good responders and poor responders, defined by the efficacy of MPH, had significantly different theta band power in right temporal areas, alpha in left occipital and frontal areas, and beta in left frontal areas. Moreover, we showed that MPH good responders had significant improvements toward normalization in several coherence measures after MPH treatment. Our study implies the possibility of these EEG indices as predictive markers for ADHD therapeutic efficacy.

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.

Dorsolateral prefrontal cortex dysfunction caused by a go/no-go task in children with attention-deficit hyperactivity disorder: A functional near-infrared spectroscopy study

Background

Children with attention-deficit hyperactivity disorder (ADHD) exhibit executive function deficits, which can be attributed to a dysfunction in the prefrontal region of the brain. Our study aims to evaluate the alteration of brain activity in children with ADHD during the administration of a go/no-go task using functional near-infrared spectroscopy (fNIRS) in comparison to a control group containing typically developing (TD) children.

Methods

32 children with ADHD and 31 of their TD peers were recruited and asked to perform a go/no-go task while undergoing measurements, with the aim of detecting changes in average oxygenated hemoglobin signaling (Δavg oxy-Hb) via fNIRS in the prefrontal lobe.

Results

fNIRS data showed significant differences between the left and right dorsolateral prefrontal cortices, with a lower Δavg oxy-Hb change in the ADHD group compared to the TD group.

Conclusion

Our results indicate that brain dysfunction in children with ADHD is related to functional impairments in the dorsolateral prefrontal cortex. The go/no-go task paired with fNIRS represents a useful measurement tool to assess prefrontal brain dysfunction in children struggling with ADHD.

Comparing executive functions in children with attention deficit hyperactivity disorder with or without reading disability: A resting-state EEG study

INTRODUCTION

As numerous studies have shown, executive dysfunction is the main impairment in attention-deficit/hyperactivity disorder. According to recent neuroimaging studies, the frontoparietal coherence plays a key role in overall cognitive functions. Therefore, the aim of this study was to compare executive functions during resting-state EEG by monitoring brain connectivity (coherence) patterns in children with attention deficit hyperactivity disorder (ADHD) with or without reading disability (RD).

METHODS

The statistical sample of the study consisted of 32 children with ADHD aged between 8 and 12 years old with or without specific RD. Each group consisted of 11 boys and 5 girls that were matched on chronological age and gender. EEG was recorded during eyes-opened condition and brain connectivity within and between frontal and parietal regions was analyzed within theta, alpha, and beta bands.

RESULTS

The results revealed that across the frontal regions, the comorbid group showed a significant reduction in the left intrahemispheric coherence in the alpha and beta bands. The ADHD-alone group exhibited increased theta and decreased alpha and beta coherence in frontal regions. In the frontoparietal regions, children in the comorbid group showed lower coherence between frontal and parietal networks compared to children without comorbid RD.

CONCLUSION

The findings indicate that brain connectivity (coherence) patterns of children with ADHD with comorbid RD were more abnormal and lend support to more disrupted cortical connectivity in the comorbid group. Thus, these findings can be a useful marker for better recognizing ADHD and comorbid disabilities.

Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight

Pilots of aircraft face varying degrees of cognitive workload even during normal flight operations. Periods of low cognitive workload may be followed by periods of high cognitive workload and vice versa. During such changing demands, there exists potential for increased error on behalf of the pilots due to periods of boredom or excessive cognitive task demand. To further understand cognitive workload in aviation, the present study involved collection of electroencephalogram (EEG) data from ten (10) collegiate aviation students in a live-flight environment in a single-engine aircraft. Each pilot possessed a Federal Aviation Administration (FAA) commercial pilot certificate and either FAA class I or class II medical certificate. Each pilot flew a standardized flight profile representing an average instrument flight training sequence. For data analysis, we used four main sub-bands of the recorded EEG signals: delta, theta, alpha, and beta. Power spectral density (PSD) and log energy entropy of each sub-band across 20 electrodes were computed and subjected to two feature selection algorithms (recursive feature elimination (RFE) and lasso cross-validation (LassoCV), and a stacking ensemble machine learning algorithm composed of support vector machine, random forest, and logistic regression. Also, hyperparameter optimization and tenfold cross-validation were used to improve the model performance, reliability, and generalization. The feature selection step resulted in 15 features that can be considered an indicator of pilots' cognitive workload states. Then these features were applied to the stacking ensemble algorithm, and the highest results were achieved using the selected features by the RFE algorithm with an accuracy of 91.67% (± 0.11), a precision of 93.89% (± 0.09), recall of 91.67% (± 0.11), F-score of 91.22% (± 0.12), and the mean ROC-AUC of 0.93 (± 0.06). The achieved results indicated that the combination of PSD and log energy entropy, along with well-designed machine learning algorithms, suggest the potential for the use of EEG to discriminate periods of the low, medium, and high workload to augment aircraft system design, including flight automation features to improve aviation safety.

Higher theta-beta ratio during screen-based vs. printed paper is related to lower attention in children: An EEG study

Reading is considered a non-intuitive, cognitively demanding ability requiring synchronization between several neural networks supporting visual, language processing and higher-order abilities. With the involvement of technology in our everyday life, reading from a screen has become widely used. Several studies point to challenges in processing written materials from the screen due to changes in attention allocation when reading from a screen compared to reading from a printed paper. The current study examined the differences in brain activation when reading from a screen compared to reading from a printed paper focusing on spectral power related to attention in fifteen 6-8-year-old children. Using an electroencephalogram, children read two different age-appropriate texts, without illustrations, presented randomly on the screen and on a printed paper. Data were analyzed using spectral analyses in brain regions related to language, visual processing, and cognitive control, focusing on theta vs. beta waveforms. Results indicated that while reading from a printed paper was accompanied by higher energy in high-frequency bands (beta, gamma), reading from the screen was manifested by a higher power in the lower frequency bands (alpha, theta). Higher theta compared to the beta ratio, representing challenges in allocating attention to a given task, was found for the screen reading compared to the printed paper reading condition. Also, a significant negative correlation was found between differences in theta/beta ratio for screen vs paper reading and accuracy level in the age-normalized Sky-Search task measuring attention and a positive correlation with performance time. These results provide neurobiological support for the greater cognitive load and reduced focused attention during screen-based compared to print-based reading and suggest a different reliance on attention resources for the two conditions in children.

Neurofeedback training for children with ADHD using individual beta rhythm

Neurofeedback training (NFT) is a noninvasive neuromodulation method for children with attention-deficit/hyperactivity disorder (ADHD). Brain rhythms, the unique pattern in electroencephalogram (EEG), are widely used as the training target. Most of current studies used a fixed frequency division of brain rhythms, which ignores the individual developmental difference of each child. In this study, we validated the feasibility of NFT using individual beta rhythm. A total of 55 children with ADHD were divided into two groups using the relative power of individual or fixed beta rhythms as the training index. ADHD rating scale (ADHD-RS) was completed before and after NFT, and the EEG and behavioral features were extracted during the training process. After intervention, the attention ability of both groups was significantly improved, showing a significant increase in beta power, a decrease in scores of ADHD-RS and an improvement in behavioral and other EEG features. The training effect was significantly better with individualized beta training, showing more improvement in ADHD-RS scores. Furthermore, the distribution of brain rhythms moved towards high frequency after intervention. This study demonstrates the effectiveness of NFT based on individual beta rhythm for the intervention of children with ADHD. When designing a NFT protocol and the corresponding data analysis process, an individualized brain rhythm division should be applied to reflect the actual brain state and to accurately evaluate the effect of NFT.

Neurofeedback for the Education of Children with ADHD and Specific Learning Disorders: A Review

Neurofeedback (NF) is a type of biofeedback in which an individual’s brain activity is measured and presented to them to support self-regulation of ongoing brain oscillations and achieve specific behavioral and neurophysiological outcomes. NF training induces changes in neurophysiological circuits that are associated with behavioral changes. Recent evidence suggests that the NF technique can be used to train electrical brain activity and facilitate learning among children with learning disorders. Toward this aim, this review first presents a generalized model for NF systems, and then studies involving NF training for children with disorders such as dyslexia, attention-deficit/hyperactivity disorder (ADHD), and other specific learning disorders such as dyscalculia and dysgraphia are reviewed. The discussion elaborates on the potential for translational applications of NF in educational and learning settings with details. This review also addresses some issues concerning the role of NF in education, and it concludes with some solutions and future directions. In order to provide the best learning environment for children with ADHD and other learning disorders, it is critical to better understand the role of NF in educational settings. The review provides the potential challenges of the current systems to aid in highlighting the issues undermining the efficacy of current systems and identifying solutions to address them. The review focuses on the use of NF technology in education for the development of adaptive teaching methods and the best learning environment for children with learning disabilities.

Premenstrual syndrome is associated with an altered spontaneous electroencephalographic delta/beta power ratio across the menstrual cycle

Premenstrual syndrome is associated with altered spontaneous brain activity in the late luteal phase, but the fluctuation patterns of brain activity throughout the menstrual cycle have not been revealed. Furthermore, it is also unknown whether the altered spontaneous brain activity during the whole menstrual cycle is further associated with their habitual use of maladaptive emotion regulation strategies. Based on the two reasons, electroencephalogram data and cognitive emotion regulation questionnaire from 32 women with high premenstrual symptoms (HPMS) and 33 women with low premenstrual symptoms (LPMS) were measured in the late luteal and follicular phases. Delta power, theta power, beta power, and the slow/fast wave ratios (SW/FW, including theta/beta power ratio [TBR] and delta/beta power ratio [DBR]) were calculated using both fixed frequency bands and individually adjusted frequency bands (based on the individual alpha peak frequency). The results showed that for the frontal and central DBR, as assessed both with fixed and individualized frequency bands, there was no difference between the two phases of the LPMS group, whereas there was a difference between the two phases of the HPMS group with a higher DBR in the late luteal phase than in the follicular phase. Further correlation results revealed that for women with HPMS in the late luteal phase, the frontal and central DBR values, as assessed both with fixed and individualized frequency bands, were positively correlated with self-blame and rumination. Consequently, HPMS was characterized by a fluctuation across the menstrual cycle in the DBR, which was further associated with maladaptive emotion regulation.

Incremental Validity of Multi-Method and Multi-Informant Evaluations in the Clinical Diagnosis of Preschool ADHD

OBJECTIVES

This study investigated the discriminative validity of various single or combined measurements of electroencephalogram (EEG) data, Conners' Kiddie Continuous Performance Test (K-CPT), and Disruptive Behavior Disorder Rating Scale (DBDRS) to differentiate preschool children with ADHD from those with typical development (TD).

METHOD

We recruited 70 preschoolers, of whom 38 were diagnosed with ADHD and 32 exhibited TD; all participants underwent the K-CPT and wireless EEG recording in different conditions (rest, slow-rate, and fast-rate task).

RESULTS

Slow-rate task-related central parietal delta (1-4 Hz) and central alpha (8-13 Hz) and beta (13-30 Hz) powers between groups with ADHD and TD were significantly distinct (p < .05). A combination of DBDRS, K-CPT, and specific EEG data provided the best probability scores (area under curve = 0.926, p < .001) and discriminative validity to identify preschool children with ADHD (overall correct classification rate = 85.71%).

CONCLUSIONS

Multi-method and multi-informant evaluations should be emphasized in clinical diagnosis of preschool ADHD.

Transfreq: A Python package for computing the theta-to-alpha transition frequency from resting state electroencephalographic data

A classic approach to estimate individual theta-to-alpha transition frequency (TF) requires two electroencephalographic (EEG) recordings, one acquired in a resting state condition and one showing alpha desynchronisation due, for example, to task execution. This translates into long recording sessions that may be cumbersome in studies involving patients. Moreover, an incomplete desynchronisation of the alpha rhythm may compromise TF estimates. Here we present transfreq, a publicly available Python library that allows TF computation from resting state data by clustering the spectral profiles associated to the EEG channels based on their content in alpha and theta bands. A detailed overview of transfreq core algorithm and software architecture is provided. Its effectiveness and robustness across different experimental setups are demonstrated on a publicly available EEG data set and on in-house recordings, including scenarios where the classic approach fails to estimate TF. We conclude with a proof of concept of the predictive power of transfreq TF as a clinical marker. Specifically, we present a scenario where transfreq TF shows a stronger correlation with the mini mental state examination score than other widely used EEG features, including individual alpha peak and median/mean frequency. The documentation of transfreq and the codes for reproducing the analysis of the article with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/. Motivated by the results showed in this article, we believe our method will provide a robust tool for discovering markers of neurodegenerative diseases.

Methodological considerations for studying neural oscillations

Neural oscillations are ubiquitous across recording methodologies and species, broadly associated with cognitive tasks, and amenable to computational modelling that investigates neural circuit generating mechanisms and neural population dynamics. Because of this, neural oscillations offer an exciting potential opportunity for linking theory, physiology and mechanisms of cognition. However, despite their prevalence, there are many concerns-new and old-about how our analysis assumptions are violated by known properties of field potential data. For investigations of neural oscillations to be properly interpreted, and ultimately developed into mechanistic theories, it is necessary to carefully consider the underlying assumptions of the methods we employ. Here, we discuss seven methodological considerations for analysing neural oscillations. The considerations are to (1) verify the presence of oscillations, as they may be absent; (2) validate oscillation band definitions, to address variable peak frequencies; (3) account for concurrent non-oscillatory aperiodic activity, which might otherwise confound measures; measure and account for (4) temporal variability and (5) waveform shape of neural oscillations, which are often bursty and/or nonsinusoidal, potentially leading to spurious results; (6) separate spatially overlapping rhythms, which may interfere with each other; and (7) consider the required signal-to-noise ratio for obtaining reliable estimates. For each topic, we provide relevant examples, demonstrate potential errors of interpretation, and offer suggestions to address these issues. We primarily focus on univariate measures, such as power and phase estimates, though we discuss how these issues can propagate to multivariate measures. These considerations and recommendations offer a helpful guide for measuring and interpreting neural oscillations.

Task-Rate-Related Neural Dynamics Using Wireless EEG to Assist Diagnosis and Intervention Planning for Preschoolers with ADHD Exhibiting Heterogeneous Cognitive Proficiency

This study used a wireless EEG system to investigate neural dynamics in preschoolers with ADHD who exhibited varying cognitive proficiency pertaining to working memory and processing speed abilities. Preschoolers with ADHD exhibiting high cognitive proficiency (ADHD-H, n = 24), those with ADHD exhibiting low cognitive proficiency (ADHD-L, n = 18), and preschoolers with typical development (TD, n = 31) underwent the Conners’ Kiddie Continuous Performance Test and wireless EEG recording under different conditions (rest, slow-rate, and fast-rate task). In the slow-rate task condition, compared with the TD group, the ADHD-H group manifested higher delta and lower beta power in the central region, while the ADHD-L group manifested higher parietal delta power. In the fast-rate task condition, in the parietal region, ADHD-L manifested higher delta power than those in the other two groups (ADHD-H and TD); additionally, ADHD-L manifested higher theta as well as lower alpha and beta power than those with ADHD-H. Unlike those in the TD group, the delta power of both ADHD groups was enhanced in shifting from rest to task conditions. These findings suggest that task-rate-related neural dynamics contain specific neural biomarkers to assist clinical planning for ADHD in preschoolers with heterogeneous cognitive proficiency. The novel wireless EEG system used was convenient and highly suitable for clinical application.

Investigating the Potential Use of EEG for the Objective Measurement of Auditory Presence

Presence is the sense of being in a virtual environment when physically situated in another place. It is one of the key components of the overall virtual reality (VR) experience, as well as other immersive audio applications. However, there is no standardized method for measuring presence. In our previous study, we explored the possibility of using electroencephalography (EEG) to measure presence by using questionnaires as a reference. It was found that an increase in the subjective presence level was correlated with an increase in the theta/beta ratio (an index derived from EEG). In the present study, we re-analyzed the original data and found that the peak alpha frequency (PAF), another EEG index, may also have the potential to reflect the change in the subjective presence level. Specifically, an increase in the subjective presence level was found to be correlated with a decrease in PAF. Together with our previous study, these results indicate the potential use of EEG for the objective measurement of presence in the future.

Neural Effects of Physical Activity and Movement Interventions in Individuals With Developmental Disabilities-A Systematic Review

Individuals with developmental disabilities present with perceptuo-motor, social communication, and cognitive impairments that often relate to underlying atypical brain structure and functioning. Physical activity/movement interventions improve behavioral performance of individuals with and without developmental disabilities. Majority of the evidence on potential neural mechanisms explaining the impact of physical activity/movement interventions is based on studies in individuals with typical development; there is a dearth of systematic reviews synthesizing the neural effects of physical activity/movement interventions in individuals with developmental disabilities. In this systematic review, we have gathered evidence on the neural effects of physical activity/movement interventions from 32 papers reporting substantial neural effects and behavioral improvements in individuals with developmental disabilities. Chronic intervention effects (multiple sessions) were greater than acute intervention effects (single session). Specifically, using electroencephalogram, functional magnetic resonance imaging, diffusion tensor imaging, and functional near-infrared spectroscopy, studies found physical activity/movement intervention-related changes in neural activity, indicating normalization of cortical arousal in individuals with attention-deficit /hyperactivity disorder (ADHD), increased social brain connectivity in individuals with autism spectrum disorder (ASD), and more efficient executive functioning processes in individuals with a wide range of other developmental disabilities. Despite promising results, more research is clearly needed in this area with larger sample sizes, using standardized neuroimaging tools/variables, and across multiple diagnoses to further explore the neural mechanisms underlying physical activity/movement interventions and to replicate findings from the present review.

Neurological state changes indicative of ADHD in children learned via EEG-based LSTM networks

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that pervasively interferes with the lives of individuals starting in childhood.

OBJECTIVE

To address the subjectivity of current diagnostic approaches, many studies have been dedicated to efforts to identify the differences between ADHD and neurotypical (NT) individuals using EEG and continuous performance tests (CPT).

APPROACH

In this study, we proposed EEG-based long short-term memory (LSTM) networks that utilize deep learning techniques with learning the cognitive state transition to discriminate between ADHD and NT children via EEG signal processing. A total of thirty neurotypical children and thirty ADHD children participated in CPT tests while being monitored with EEG. Several architectures of deep and machine learning were applied to three EEG data segments including resting state, cognitive execution, and a period containing a fusion of those.

MAIN RESULTS

The experimental results indicated that EEG-based LSTM networks produced the best performance with an average accuracy of 90.50 ± 0.81 % in comparison with the deep neural networks, the convolutional neural networks, and the support vector machines with learning the cognitive state transition of EEG data. Novel observations of individual neural markers showed that the beta power activity of the O1 and O2 sites contributed the most to the classifications, subjects exhibited decreased beta power in the ADHD group, and had larger decreases during cognitive execution.

SIGNIFICANCE

These findings showed that the proposed EEG-based LSTM networks are capable of extracting the varied temporal characteristics of high-resolution electrophysiological signals to differentiate between ADHD and NT children, and brought a new insight to facilitate the diagnosis of ADHD. The registration numbers of the institutional review boards are 16MMHIS021 and EC1070401-F.

Reduced Power in Fronto-Parietal Theta EEG Linked to Impaired Attention-Sampling in Adult ADHD

Attention-deficit/hyperactivity disorder (ADHD) in adults is understudied, especially regarding neural mechanisms such as oscillatory control of attention sampling. We report an electroencephalography (EEG) study of such cortical mechanisms, in ADHD-diagnosed adults during administration of Test of Variables of Attention (TOVA), a gold-standard continuous performance test for ADHD that measures the ability to sustain attention and inhibit impulsivity. We recorded 53 adults (28 female, 25 male, aged 18-60), and 18 matched healthy controls, using 128-channel EEG. We analyzed sensor-space features established as neural correlates of attention: timing-sensitivity and phase-synchrony of response activations, and event-related (de)synchronization (ERS/D) of α and θ frequency band activity; in frontal and parietal scalp regions. TOVA test performance significantly distinguished ADHD adults from neurotypical controls, in commission errors, response time variability (RTV) and d' (response sensitivity). The ADHD group showed significantly weaker target-locked and response-locked amplitudes, that were strongly right-lateralized at the N2 wave, and weaker phase synchrony (longer reset poststimulus). They also manifested significantly less parietal prestimulus 8-Hz θ ERS, less frontal and parietal poststimulus 4-Hz θ ERS, and more frontal and parietal prestimulus α ERS during correct trials. These differences may reflect excessive modulation of endogenous activity by strong entrainment to stimulus (α), combined with deficient modulation by neural entrainment to task (θ), which in TOVA involves monitoring stimulus spatial location (not predicted occurrence onset which is regular and task-irrelevant). Building on the hypotheses of θ coding for relational structure and rhythmic attention sampling, our results suggest that ADHD adults have impaired attention sampling in relational categorization tasks.

Neurofeedback Training Protocol Based on Selecting Distinctive Features to Treat or Reduce ADHD Symptoms

Recent research has shown that electroencephalography (EEG) theta/beta ratio (TBR) in cases with attention deficit hyperactivity disorder (ADHD) has thus far been reported lower than that in healthy individuals. Accordingly, utilizing EEG-TBR as a biomarker to diagnose ADHD has been called into question. Besides, employing known protocol to reduce EEG-TBR in the vertex (Cz) channel to treat ADHD via neurofeedback (NFB) has been doubted. The present study was to propose a new NFB treatment protocol to manage ADHD using EEG signals from 30 healthy controls and 30 children with ADHD through an attention-based task and to calculate relative power in their different frequency bands. Then, the most significant distinguishing features of EEG signals from both groups were determined via a genetic algorithm (GA). The results revealed that EEG-TBR values in children with ADHD were lower compared with those in healthy peers; however, such a difference was not statistically significant. Likewise, inhibiting alpha band activity and enhancing delta one in F7 or T5 channels was proposed as a new NFB treatment protocol for ADHD. No significant increase in EEG-TBR in the Cz channel among children with ADHD casts doubt on the effectiveness of using EEG-TBR inhibitory protocols in the Cz channel. Consequently, it was proposed to apply the new protocol along with reinforced beta-band activity to treat or reduce ADHD symptoms.

Background Activity Findings in End-Stage Renal Disease With and Without Comorbid Diabetes: An Electroencephalogram Study

Renal failure and diabetes can induce cerebral complications, including encephalopathy, for which attentional and cognitive impairment are common symptoms. It is possible that renal failure with comorbid diabetes may induce more severe encephalopathy due to multiple pathogenic mechanisms. This concept was supported by the main findings of this study, which showed that EEG background activity between end-stage renal disease with and without comorbid diabetes was significantly different in relative power of delta in the eyes-open condition in frontoparietal regions; theta in the eyes-closed condition in all regions; beta in the parieto-occipital regions in both eye conditions; the delta/theta ratio in both eye conditions in frontoparietal regions; and the theta/beta ratio in all regions in the eyes-closed condition. These findings may increase awareness of comorbid cerebral complications in clinical practice. Moreover, the delta/theta ratio is recommended as an optimal feature to possibly determine the severity of encephalopathy.

Characterization of Mayer-wave oscillations in functional near-infrared spectroscopy using a physiologically informed model of the neural power spectra

Significance: Mayer waves are spontaneous oscillations in arterial blood pressure that can mask cortical hemodynamic responses associated with neural activity of interest. Aim: We aim to characterize the properties of oscillations in the functional near-infrared spectroscopy (fNIRS) signal generated by Mayer waves in a large sample of fNIRS recordings. Further, we aim to determine the impact of short-channel correction for the attenuation of these unwanted signal components. Approach: Mayer-wave oscillation parameters were extracted from 310 fNIRS measurements using the fitting oscillations and one-over-f method to compute normative values. The effect of short-channel correction on Mayer-wave oscillation power was quantified on 222 measurements. The practical benefit of the short-channel correction approach for reducing Mayer waves and improving response detection was also evaluated on a subgroup of 17 fNIRS measurements collected during a passive auditory speech detection experiment. Results: Mayer-wave oscillations had a mean frequency of 0.108 Hz, bandwidth of 0.04 Hz, and power of 3.5    μ M 2 / Hz . The distribution of oscillation signal power was positively skewed, with some measurements containing large Mayer waves. Short-channel correction significantly reduced the amplitude of these undesired signals; greater attenuation was observed for measurements containing larger Mayer-wave oscillations. Conclusions: A robust method for quantifying Mayer-wave oscillations in the fNIRS signal spectrum was presented and used to provide normative parameterization. Short-channel correction is recommended as an approach for attenuating Mayer waves, particularly in participants with large oscillations.

DSM-5 Adult Attention-Deficit/Hyperactivity Disorder: Sex Differences in EEG Activity

This study examined sex differences in the EEG of adults diagnosed with Attention-Deficit/Hyperactivity Disorder (AD/HD) according to DSM-5 criteria. Sixteen females and 16 males with AD/HD, and age- and sex-matched control groups, had an eyes-closed resting EEG recorded from 19 electrode sites. EEGs were Fast Fourier transformed and estimates for total power, absolute and relative power in the delta, theta, alpha, beta and gamma bands, and the theta/beta ratio, were analysed across nine cortical regions. Males with AD/HD, compared with male controls, had globally reduced absolute beta, globally elevated relative theta, and a larger theta/beta ratio. In contrast, no global effects emerged between females with and without AD/HD. Significant group interactions indicated that globally elevated relative theta and elevated frontal-midline theta/beta ratio noted in males with AD/HD differed significantly from results in females. There are statistically significant EEG differences in relative theta and the theta/beta ratio between males and females with and without AD/HD. These results indicate that AD/HD affects the EEG activity of males and females differently. This study helps confirm the need for further independent examination of AD/HD within female populations.

Neural Dynamics for Facilitating ADHD Diagnosis in Preschoolers: Central and Parietal Delta Synchronization in the Kiddie Continuous Performance Test

The present study aimed to characterize children at risk of attention-deficit/hyperactivity disorder (ADHD) during preschool age and provide early intervention. The continuous performance test (CPT) and electroencephalography (EEG) can contribute additional valuable information to facilitate diagnosis. This study measured brain dynamics at slow and fast task rates in the CPT using a wireless wearable EEG and identified correlations between the EEG and CPT data in preschool children with ADHD. Forty-nine preschool children participated in this study, of which 29 were diagnosed with ADHD and 20 exhibited typical development (TD). The Conners Kiddie Continuous Performance Test (K-CPT) and wireless wearable EEG recordings were employed simultaneously. Significant differences were observed between the groups with ADHD and TD in task-related EEG spectral powers (central as well as parietal delta, P < 0.01), which were distinct only in the slow-rate task condition. A shift from resting to the CPT task condition induced overall alpha powers decrease in the ADHD group. In the task condition, the delta powers were positively correlated with the CPT perseveration scores, whereas the alpha powers were negatively correlated with specific CPT scores mainly on perseveration and detectability (P < 0.05). These results, which complement the findings of other sparse studies that have investigated within-task-related brain dynamics, particularly in preschool children, can assist specialists working in early intervention to plan training and educational programs for preschoolers with ADHD.

Relationship between individual alpha peak frequency and attentional performance in a multiple object tracking task among ice-hockey players

Individual alpha peak frequency (IAPF), the discrete frequency with the highest power value in the alpha oscillation range of the electroencephalogram, is a stable neurophysiological marker and is closely associated with various cognitive functions, including aspects of attention and working memory. However, the relationship between IAPF and attentional performance as well as the effects of engaging attention on IAPF are unknow. Here, we examined whether IAPF values were associated with attentional performance by evaluating accuracy during the performance of a multiple object tracking (MOT) task, a well-established paradigm for investigating goal-driven attention in dynamic environments, and whether engagement in the task affected IAPF values. In total, 18 elite players and 20 intermediate players completed the study. Resting electroencephalogram recordings were obtained for 120 s while players kept their eyes open and an additional 120 s while players’ eyes were closed, before and again after performing the MOT task. Tracking accuracy in the MOT task and IAPF values before and after the MOT task were analyzed. As expected, tracking accuracies were higher in elite players than in intermediate-level players. Baseline IAPF values were significantly and positively correlated with the accuracy of target tracking in the MOT task. IAPF values were higher in elite than intermediate players in both the eyes open and closed conditions and both before and after MOT task performance. Interindividual IAPF values did not differ before and after the MOT task. These findings indicate that IAPF is a stable marker, without intraindividual changes associated with engagement in the MOT task. Elite players had higher IAPF values and exhibited more accurate MOT performance than intermediate-level players; thus, baseline IAPF values may be useful to predict attentional performance in the MOT task among athletes.

An EEG investigation of alpha and beta activity during resting states in adults with Williams syndrome

Background

Williams syndrome (WS) is neurodevelopmental disorder characterised by executive deficits of attention and inhibitory processing. The current study examined the neural mechanisms during resting states in adults with WS in order to investigate how this subserves the attention and inhibitory deficits associated with the syndrome.

Method

Adopting electroencephalography (EEG) methodology, cortical electrical activity was recorded from eleven adults with WS aged 35 + years during Eyes Closed (EC) and Eyes Open (EO) resting states, and compared to that of thirteen typically developing adults matched for chronological age (CA) and ten typically developing children matched for verbal mental ability (MA). Using mixed-design analyses of variance (ANOVA), analyses focused on the full alpha (8–12.5 Hz), low-alpha (8–10 Hz), upper-alpha (10–12.5 Hz), and beta (13–29.5 Hz) bands, as these are thought to have functional significance with attentional and inhibitory processes.

Results

No significant difference in alpha power were found between the WS and CA groups across all analyses, however a trend for numerically lower alpha power was observed in the WS group, consistent with other developmental disorders characterised by attentional/inhibitory deficits such as Attention Deficit Hyperactivity Disorder (ADHD). In contrast, comparable beta power between the WS and CA groups during both EC/EO conditions suggests that their baseline EEG signature is commensurate with successful attentional processing, though this needs to be interpreted with caution due to the small sample size. Analyses also revealed an unusual trend for low variability in the EEG signature of the WS group, which contradicts the heterogeneity typically observed behaviourally.

Conclusions

This novel finding of low variability in the EEG spectra in the WS group has been previously associated with poor behavioural performance in ADHD and is highly informative, highlighting future research needs to also consider how the role of low variability in the EEG profile of WS manifests in relation to their behavioural and cognitive profiles.

Neurophysiological aspects of the completely locked-in syndrome in patients with advanced amyotrophic lateral sclerosis

OBJECTIVE

Amyotrophic lateral sclerosis (ALS) patients in completely locked-in syndrome (CLIS) are incapable of expressing themselves, and their state of consciousness and awareness is difficult to evaluate. Due to the complete paralysis included paralysis of eye muscles, any assessment of the perceptual and psychophysiological state can only be implemented in passive experimental paradigms with neurophysiological recordings.

METHODS

Four patients in CLIS were investigated in several experiments including resting state, visual stimulation (eyes open vs eyes closed), auditory stimulation (modified local-global paradigm), somatosensory stimulation (electrical stimulation of the median nerve), and during sleep.

RESULTS

All patients showed altered neurophysiological metrics, but a unique and common pattern could not be found between patients. However, slowing of the electroencephalography (EEG) and attenuation or absence of alpha wave activity was common in all patients. In two of the four patients, a slow dominant frequency emerged at 4 Hz with synchronized EEG at all channels. In the other two patients slowing of EEG appears less synchronized. EEGs between eyes open and eyes closed were significantly different in all patients. The dominant slow frequency during the day changes during slow-wave sleep (supposedly sleep stage 3) to even slower frequencies below 2 Hz. Somatosensory evoked potentials (SEPs) were absent or significantly altered in comparison to healthy subjects, similarly for auditory evoked potentials (AEPs).

CONCLUSIONS

The heterogeneity of the results underscores the fact that no single neurophysiological index is available to assess psychophysiological states in unresponsive ALS patients in CLIS. This caveat may also be valid for the assessment of cognitive processes; a functioning BCI can be the solution.

SIGNIFICANCE

Most of the studies of the neurophysiology of ALS patients focused on the early stage of the disease, and there are very few studies on the late stage when patients are completely paralyzed with no means of communication (i.e., CLIS). This study provides quantitative metrics of different neurophysiological aspects of these patients.

Standardization and Personalized Medicine Using Quantitative EEG in Clinical Settings

Two major trends have been dominant in health care in recent years. First, there is a growing consensus that standardization of health care procedures and methods can result in improved effectiveness and safety of treatments. Second, there is increased interest in "personalized medicine," which refers to the tailoring of treatments to individual patients. Here I discuss how these trends apply to the field of quantitative EEG (qEEG), where de-artifacted resting state EEGs of individuals are compared with a normative database in order to assess clinically meaningful deviations, which can be used for diagnostic procedures, to guide personalized treatment protocols, and to assess treatment effectiveness. Standardized and automated de-artifacting procedures are increasingly being used in scientific research and in clinical practice. The advantages of these procedures over manual de-artifacting will be discussed. The results of a systematic comparison between 2 commonly used qEEG databases show that these databases produce very comparable results, illustrating not only the validity and reliability of both databases but also the opportunity to move forward to a standardized use of qEEG in clinical practice. Finally, the standardization of qEEG interpretation as both a diagnostic and treatment selection tool provides an example of how qEEG can merge both personalized medicine and standardization in the treatment of psychological disorders.

Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity

Band ratio measures, computed as the ratio of power between two frequency bands, are a common analysis measure in neuroelectrophysiological recordings. Band ratio measures are typically interpreted as reflecting quantitative measures of periodic, or oscillatory, activity. This assumes that the measure reflects relative powers of distinct periodic components that are well captured by predefined frequency ranges. However, electrophysiological signals contain periodic components and a 1/f-like aperiodic component, the latter of which contributes power across all frequencies. Here, we investigate whether band ratio measures truly reflect oscillatory power differences, and/or to what extent ratios may instead reflect other periodic changes, such as in center frequency or bandwidth, and/or aperiodic activity. In simulation, we investigate how band ratio measures relate to changes in multiple spectral features, and show how multiple periodic and aperiodic features influence band ratio measures. We validate these findings in human electroencephalography (EEG) data, comparing band ratio measures to parameterizations of power spectral features and find that multiple disparate features influence ratio measures. For example, the commonly applied θ/β ratio is most reflective of differences in aperiodic activity, and not oscillatory θ or β power. Collectively, we show that periodic and aperiodic features can create the same observed changes in band ratio measures, and that this is inconsistent with their typical interpretations as measures of periodic power. We conclude that band ratio measures are a non-specific measure, conflating multiple possible underlying spectral changes, and recommend explicit parameterization of neural power spectra as a more specific approach.

Parameterizing neural power spectra into periodic and aperiodic components

Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic oscillations have been linked to numerous physiological, cognitive, behavioral and disease states. Emerging evidence demonstrates that the aperiodic component has putative physiological interpretations and that it dynamically changes with age, task demands and cognitive states. Electrophysiological neural activity is typically analyzed using canonically defined frequency bands, without consideration of the aperiodic (1/f-like) component. We show that standard analytic approaches can conflate periodic parameters (center frequency, power, bandwidth) with aperiodic ones (offset, exponent), compromising physiological interpretations. To overcome these limitations, we introduce an algorithm to parameterize neural power spectra as a combination of an aperiodic component and putative periodic oscillatory peaks. This algorithm requires no a priori specification of frequency bands. We validate this algorithm on simulated data, and demonstrate how it can be used in applications ranging from analyzing age-related changes in working memory to large-scale data exploration and analysis.

Individual Differences in Negative Emotion Differentiation Predict Resting-State Spontaneous Emotional Regulatory Processes

Negative emotion differentiation facilitates emotion regulation. However, whether individual differences in negative emotion differentiation is associated with resting-state spontaneous emotion regulation remains unclear. This study aimed to explore the effect of individual differences in negative emotion differentiation on spontaneous emotional regulatory processes as indexed by resting electroencephalogram (EEG) indicators (e.g., frontal alpha asymmetry and theta/beta ratio). Participants (n = 40, M_age = 21.74 years, 62% women) completed a negative emotion differentiation task. Afterward, 4 min of resting EEG data were recorded. Multiple regression results showed that negative emotion differentiation significantly predicted the alpha asymmetry at electrode pairs (F4–F3 and FP2–FP1) and the theta/beta ratio at the F3 and FZ electrode sites. Individuals with high negative emotion differentiation presented more left-lateralized activations and a lower theta/beta ratio. Taken together, these results suggest that individuals with high negative emotion differentiation show enhanced spontaneous emotional regulatory functioning. Thus, we provided the first resting-state neural evidence on emotion differentiation of spontaneous emotional regulatory functioning.

Initial study on changes in activity of brain waves during audio stimulation using noninvasive brain–computer interfaces: choosing the appropriate filtering method

Objectives

In this paper series of experiments were carried out in order to check the influence of various sounds on human concentration during visually stimulated tasks performance.

Methods

The obtained data was filtered. For the study purposes various smoothing filters were tested, including Median and Savitzky–Golay Filters; however, median filter only was applied. Implementation of this filter made the obtained data more legible and useful for potential diagnostics purposes. The tests were carried out with the implementation of the Emotiv Flex EEG headset.

Results

The obtained results were promising and complied with the initial assumptions, which stated that the “relax”-phase, despite relaxing sounds stimuli, is strongly affected with the “focus”-phase with distracting sounds, which is clearly visible in the shape of the recorded EEG data.

Conclusions

Further investigations with broader range of subjects is being currently carried out in order to confirm the already obtained results.

Towards a Pragmatic Approach to a Psychophysiological Unit of Analysis for Mental and Brain Disorders: An EEG-Copeia for Neurofeedback

This article proposes what we call an "EEG-Copeia" for neurofeedback, like the "Pharmacopeia" for psychopharmacology. This paper proposes to define an "EEG-Copeia" as an organized list of scientifically validated EEG markers, characterized by a specific association with an identified cognitive process, that define a psychophysiological unit of analysis useful for mental or brain disorder evaluation and treatment. A characteristic of EEG neurofeedback for mental and brain disorders is that it targets a EEG markers related to a supposed cognitive process, whereas conventional treatments target clinical manifestations. This could explain why EEG neurofeedback studies encounter difficulty in achieving reproducibility and validation. The present paper suggests that a first step to optimize EEG neurofeedback protocols and future research is to target a valid EEG marker. The specificity of the cognitive skills trained and learned during real time feedback of the EEG marker could be enhanced and both the reliability of neurofeedback training and the therapeutic impact optimized. However, several of the most well-known EEG markers have seldom been applied for neurofeedback. Moreover, we lack a reliable and valid EEG targets library for further RCT to evaluate the efficacy of neurofeedback in mental and brain disorders. With the present manuscript, our aim is to foster dialogues between cognitive neuroscience and EEG neurofeedback according to a psychophysiological perspective. The primary objective of this review was to identify the most robust EEG target. EEG markers linked with one or several clearly identified cognitive-related processes will be identified. The secondary objective was to organize these EEG markers and related cognitive process in a psychophysiological unit of analysis matrix inspired by the Research Domain Criteria (RDoC) project.

EEG oscillation evidences of altered resting-state brain activity in children orphaned by parental HIV/AIDS

Early life adverse events in children orphaned by parental HIV/AIDS can have long-lasting effects on brain function. However, data are limited regarding the brain activation during resting state in these children. The current study aims to investigate the brain oscillation at rest in children orphaned by HIV/AIDS using resting-state EEG data. Data were derived from a larger neurodevelopmental study in which 90 children aged 9-17 years orphaned by AIDS and 66 matched controls were recruited through the local communities and school system. Children (63 orphans and 65 controls) who had resting-state EEG data and completed the Teacher-Child Rating Scale (T-CRS) were included in the current analysis. EEG results showed that children orphaned by HIV/AIDS had increased theta activity in the midline region, decreased beta activity in left hemisphere and overall increased theta/beta power ratio. Furthermore, the theta/beta ratio is positively correlated with learning problem and acting out scores, and negatively correlated with task orientation and peer social skill activities. Findings support that children orphaned by HIV/AIDS demonstrate different brain activity compared to non-orphan controls and also suggest that the EEG activities in resting state may serve as useful indicators of children's behavioral problems.

Individualized Neurofeedback Training May Help Achieve Long-Term Improvement of Working Memory in Children With ADHD

Background. Children with attention deficit hyperactivity disorder (ADHD) may suffer from working memory deficits, which can adversely affect their academic performance. Neurofeedback training may enhance working memory and provide a solution to this problem. Aim. To investigate the effect of frequency-neurofeedback on working memory in children with ADHD and to check if the effect is long-lasting. Method. Forty-eight children with ADHD (aged 6-12 years) were randomly assigned either to a neurofeedback with training parameters chosen to take into account each child's peak alpha frequency (PAF) or to a waiting list control group. Each trained child underwent 19-channel electroencephalography (EEG). All children had average intelligence and none were receiving treatment, such as medications, for ADHD. Prior to the training, MOXO and n-back tests were performed. Next, neurofeedback training sessions with frequency bands for theta and beta ranges determined using each child's PAF were carried out for 10 weeks. Training parameters were set to increase amplitudes in the low beta range and to decrease amplitudes in the theta and high beta frequency ranges. The n-back test was performed again right after the training and then a year later. Results. During the first n-back test, children from both groups responded correctly to more than 43% of the stimuli. During the second test, children from the waiting list responded correctly to an average of 49% of the stimuli, while children who underwent the neurofeedback training were correct, on average, 69% of the time (significant difference, P < .001). During the third n-back test a year later, children from the waiting list responded correctly to 53% of the stimuli, while those who underwent the neurofeedback training responded correctly to nearly 71%. Conclusion. This study found a statistically significant improvement in a measure of working memory in children who did 10 to 12 sessions of neurofeedback training with training frequency ranges for theta and beta defined according to each child's PAF. The beneficial effects were still present a year after training.

Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments

Background: Although many electroencephalographic (EEG) indicators have been proposed in the literature, it is unclear which of the power bands and various indices are best as indicators of mental workload. Spectral powers (Theta, Alpha, and Beta) and ratios (Beta/(Alpha + Theta), Theta/Alpha, Theta/Beta) were identified in the literature as prominent indicators of cognitive workload.

Objective: The aim of the present study is to identify a set of EEG indicators that can be used for the objective assessment of cognitive workload in a multitasking setting and as a foundational step toward a human-autonomy augmented cognition system.

Methods: The participants' perceived workload was modulated during a teleoperation task involving an unmanned aerial vehicle (UAV) shepherding a swarm of unmanned ground vehicles (UGVs). Three sources of data were recorded from sixteen participants (n = 16): heart rate (HR), EEG, and subjective indicators of the perceived workload using the Air Traffic Workload Input Technique (ATWIT).

Results: The HR data predicted the scores from ATWIT. Nineteen common EEG features offered a discriminatory power of the four workload setups with high classification accuracy (82.23%), exhibiting a higher sensitivity than ATWIT and HR.

Conclusion: The identified set of features represents EEG indicators for the objective assessment of cognitive workload across subjects. These common indicators could be used for augmented intelligence in human-autonomy teaming scenarios, and form the basis for our work on designing a closed-loop augmented cognition system for human-swarm teaming.

Prosocial Virtual Reality, Empathy, and EEG Measures: A Pilot Study Aimed at Monitoring Emotional Processes in Intergroup Helping Behaviors

During a non-invasive procedure, participants both helped and helped by a confederate with features that create social distance (membership in an ethnic outgroup or another social group). For this purpose, we created a set of virtual scenarios in which the confederate’s ethnicity (white vs. black) and appearance (business man vs. beggar, with casual dress as a control condition) were crossed. The study aimed to explore how the emotional reactions of participants changed according to the confederate’s status signals as well as signals that they belong to the same or a different ethnic group. Participants’ alertness, calmness, and engagement were monitored using electroencephalogram (EEG) during the original virtual reality (VR) video sessions. Participants’ distress and empathy when exposed to helping interactions were self-assessed after the VR video sessions. The results pointed out that, irrespective of whether they helped the confederate or were helped by him/her, white participants showed higher levels of alertness when exposed to helping interactions involving a white beggar or a black businessman, and their emotional calmness and engagement were higher when interacting with a black beggar or a white businessman. The results for self-assessed distress and empathy followed the same tendency, indicating how physiological and self-assessed measures can both contribute to a better understanding of the emotional processes in virtual intergroup helping situations. Based on the presented results, the methodological and practical implications of VR in terms of enhancing self-reflective capacities in intergroup helping processes are discussed.

EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD

Adults with attention-deficit/hyperactivity disorder (ADHD) have been described as having altered resting-state electroencephalographic (EEG) spectral power and theta/beta ratio (TBR). However, a recent review (Pulini et al. 2018) identified methodological errors in neuroimaging, including EEG, ADHD classification studies. Therefore, the specific EEG neuromarkers of adult ADHD remain to be identified, as do the EEG characteristics that mediate between genes and behaviour (mediational endophenotypes). Resting-state eyes-open and eyes-closed EEG was measured from 38 adults with ADHD, 45 first-degree relatives of people with ADHD and 51 unrelated controls. A machine learning classification analysis using penalized logistic regression (Elastic Net) examined if EEG spectral power (1-45 Hz) and TBR could classify participants into ADHD, first-degree relatives and/or control groups. Random-label permutation was used to quantify any bias in the analysis. Eyes-open absolute and relative EEG power distinguished ADHD from control participants (area under receiver operating characteristic = 0.71-0.77). The best predictors of ADHD status were increased power in delta, theta and low-alpha over centro-parietal regions, and in frontal low-beta and parietal mid-beta. TBR did not successfully classify ADHD status. Elevated eyes-open power in delta, theta, low-alpha and low-beta distinguished first-degree relatives from controls (area under receiver operating characteristic = 0.68-0.72), suggesting that these features may be a mediational endophenotype for adult ADHD. Resting-state EEG spectral power may be a neuromarker and mediational endophenotype of adult ADHD. These results did not support TBR as a diagnostic neuromarker for ADHD. It is possible that TBR is a characteristic of childhood ADHD.

Frequency coupling of low and high frequencies in the EEG of ADHD children and adolescents in closed and open eyes conditions

The present report examines the possible differences in absolute Power Spectral Density (PSD), the topography of brain rhythms, and low frequency (delta and theta) vs. beta PSD when attention deficit disorder (ADHD) children and controls are compared. These results would potentially be useful to test the validity of the developmental lag and differential developmental models for ADHD. The EEG resting state under the experimental conditions of open and closed eyes were recorded in samples of control subjects and children with ADHD (6-17 years old). The PSD from 0 to 46 Hz was calculated and ANOVAs were performed to compare the groups of subjects in the two experimental conditions. To observe differences in the co-maturation of the brain rhythms between the groups of subjects, correlations of the PSD of all frequency ranges were computed. These results showed an increase in delta power in children with ADHD compared to control subjects. The topographies of the different brain rhythms were similar in children with ADHD and controls. The maturational power-to-power frequency-coupling between low frequencies and beta rhythms was lower in children with ADHD. The increased delta PSD in ADHD and the similar brain rhythms topographies in children with ADHD and controls support the developmental lag model, whereas the decreased co-maturation of low frequencies vs. beta PSD in children with ADHD suggests a differential maturation rate for low and beta frequencies in children with ADHD compared to controls.