The analysis of EEG coherence reflects middle childhood differences in mathematical achievement

Psychophysiological evaluation of the Smartick method in children with reading and mathematical difficulties

Introduction

Assistive technologies for learning are aimed at promoting academic skills, such as reading and mathematics. These technologies mainly embrace mobile and web apps addressed to children with learning difficulties. Nevertheless, most applications lack pedagogical foundation. Additionally, the task of selecting suitable technology for educational purposes becomes challenging. Hence, this protocol posits the psychophysiological assessment of an online method for learning (OML) named Smartick. This platform comprises reading and math activities for learning training. In this protocol, individual monitoring of each child is proposed to determine the progress in learning caused by Smartick.

Methods and analysis

One hundred and twelve children aged between 8 and 12 who present reading or math difficulty after a rigorous psychometric evaluation will be recruited. The study comprises four sessions. In sessions 1 and 2, collective and individual psychometric evaluations will be performed, respectively. Reading and mathematical proficiency will be assessed, as well as attentional levels and intellectual quotient. Subsequently, each child will be semi-randomly assigned to either the experimental or control groups. Afterward, a first EEG will be collected for all children in session 3. Then, experimental groups will use Smartick for 3 months, in addition to their traditional learning method. In contrast, control groups will only continue with their traditional learning method. Finally, session 4 will consist of performing a second psychometric evaluation and another EEG, so that psychophysiological parameters can be encountered that indicate learning improvements due to the OML, regardless of the traditional learning method at hand.

Discussion

Currently, few studies have validated learning improvement due to assistive technologies for learning. However, this proposal presents a psychophysiological evaluation addressed to children with reading or math difficulties who will be trained with an OML.

Effective connectivity analysis of brain networks of mathematically gifted adolescents using transfer entropy

Using functional neuroimaging, electrophysiological techniques and neural data processing techniques, neuroscientists have found that mathematically gifted adolescents exhibit unusual neurocognitive features in the activation of task-related brain regions. Hemispheric information interaction, functional reorganization of networks, and utilization of task-related brain regions are beneficial to rapid and efficient task processing. Based on Granger causality channel selection, the transfer entropy (TE) value between effective channels was computed, and the information flow patterns in the directed functional brain networks derived from electroencephalography (EEG) data during deductive reasoning tasks were explored. We evaluated the workspace configuration patterns of the brain network and the global integration characteristics of separated brain regions using node strength, motif, directed clustering coefficient and characteristic path length in the brain networks of mathematically gifted adolescents with effective connectivity. The empirical results demonstrated that a more integrated functional network at the global level and a more efficient clique at the local level support a pattern of workspace configuration in the mathematically gifted brain that is more conducive to task-related information processing.

Early neural markers for individual difference in mathematical achievement determined from rational number processing

The neural markers for individual differences in mathematical achievement have been studied extensively using magnetic resonance imaging; however, high temporal resolution electrophysiological evidence for individual differences in mathematical achievement require further elucidation. This study evaluated the event-related potential (ERP) when 48 college students with high or low mathematical achievement (HA vs. LA) matched non-symbolic and symbolic rational numbers. Behavioral results indicated that HA students had better performance in the discretized non-symbolic matching, although the two groups showed similar performances in the continuous matching. ERP data revealed that even before non-symbolic stimulus presentation, HA students had greater Bereitschaftspotential (BP) amplitudes over posterior central electrodes. After the presentation of non-symbolic numbers, HA students had larger N1 amplitudes at 160 ms post-stimulus, over left-lateralized parieto-occipital electrodes. After the presentation of symbolic numbers, HA students displayed more profound P1 amplitudes at 100 ms post-stimulus, over left parietal electrodes. Furthermore, larger BP and N1 amplitudes were associated with the shorter reaction times, and larger P1 amplitudes corresponded to lower error rates. The BP effect could indicate preparation processing, and early left-lateralized N1 and P1 effects could reflect the non-symbolic and symbolic number processing along the dorsal neural pathways. These results suggest that the left-lateralized P1 and N1 components elicited by matching non-symbolic and symbolic rational numbers can be considered as neurocognitive markers for individual differences in mathematical achievement.

Detrending Moving Average, Power Spectral Density, and Coherence: Three EEG-Based Methods to Assess Emotion Irradiation during Facial Perception

Understanding brain reactions to facial expressions can help in explaining emotion-processing and memory mechanisms. The purpose of this research is to examine the dynamics of electrical brain activity caused by visual emotional stimuli. The focus is on detecting changes in cognitive mechanisms produced by negative, positive, and neutral expressions on human faces. Three methods were used to study brain reactions: power spectral density, detrending moving average (DMA), and coherence analysis. Using electroencephalogram (EEG) recordings from 48 subjects while presenting facial image stimuli from the International Affective Picture System, the topographic representation of the evoked responses was acquired and evaluated to disclose the specific EEG-based activity patterns in the cortex. The theta and beta systems are two key cognitive systems of the brain that are activated differently on the basis of gender. The obtained results also demonstrate that the DMA method can provide information about the cortical networks’ functioning stability, so it can be coupled with more prevalent methods of EEG analysis.

Power Spectrum and Connectivity Analysis in EEG Recording during Attention and Creativity Performance in Children

The present research aims at examining the power spectrum and exploring functional brain connectivity/disconnectivity during concentration performance, as measured by the d2 test of attention and creativity as measured by the CREA test in typically developing children. To this end, we examined brain connectivity by using phase synchrony (i.e., phase locking index (PLI) over the EEG signals acquired by the Emotiv EPOC neuroheadset in 15 children aged 9- to 12-years. Besides, as a complement, a power spectrum analysis of the acquired signals was performed. Our results indicated that, during d2 Test performance there was an increase in global gamma phase synchronization and there was a global alpha and theta band desynchronization. Conversely, during CREA task, power spectrum analysis showed a significant increase in the delta, beta, theta, and gamma bands. Connectivity analysis revealed marked synchronization in theta, alpha, and gamma. These findings are consistent with other neuroscience research indicating that multiple brain mechanisms are indeed involved in creativity. In addition, these results have important implications for the assessment of attention functions and creativity in clinical and research settings, as well as for neurofeedback interventions in children with typical and atypical development.

Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold

The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu’s version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.

Resting State EEG Related to Mathematical Improvement After Spatial Training in Children

Spatial cognitive abilities, including mental rotation (MR) and visuo-spatial working memory (vsWM) are correlated with mathematical performance, and several studies have shown that training of these abilities can enhance mathematical performance. Here, we investigated the behavioral and neural correlates of MR and vsWM training combined with number line (NL) training. Fifty-seven children, aged 6–7, performed 25 days of NL training combined with either vsWM or MR and participated in an Electroencephalography (EEG)-session in school to measure resting state activity and steady-state visual evoked potentials during a vsWM task before and after training. Fifty children, aged 6–7, received usual teaching and acted as a control group. Compared to the control group, both training groups improved on a combined measure of mathematics. Cognitive improvement was specific to the training. Significant pre-post changes in resting state-EEG (rs-EEG), common to both training groups, were found for power as well as for coherence, with no significant differences in rs-EEG-changes between the vsWM and MR groups. Two of the common rs-EEG changes were correlated with mathematical improvement: (1) an increase in coherence between the central frontal lobe and the right parietal lobe in frequencies ranging from 16 to 25 Hz, and (2) an increase in coherence between the left frontal lobe and the right parietal lobe ranging from 23 to 25 Hz. These results indicate that changes in fronto-parietal coherence are related to an increase in mathematical performance, which thus might be a useful measure in further investigations of mathematical interventions in children.

Hand preference and Mathematical Learning Difficulties: New data from Greece, the United Kingdom, and Germany and two meta-analyses of the literature

Increased rates of atypical handedness are observed in neurotypical individuals who are low-performing in mathematical tasks as well as in individuals with special educational needs, such as dyslexia. This is the first investigation of handedness in individuals with Mathematical Learning Difficulties (MLD). We report three new studies (N = 134; N = 1,893; N = 153) and two sets of meta-analyses (22 studies; N = 3,667). No difference in atypical hand preference between MLD and Typically Achieving (TA) individuals was found when handedness was assessed with self-report questionnaires, but weak evidence of a difference was found when writing hand was the handedness criterion in Study 1 (p = .049). Similarly, when combining data meta-analytically, no hand preference differences were detected. We suggest that: (i) potential handedness effects require larger samples, (ii) direction of hand preference is not a sensitive enough measure of handedness in this context, or that (iii) increased rates of atypical hand preference are not associated with MLD. The latter scenario would suggest that handedness is specifically linked to language-related conditions rather than conditions related to cognitive abilities at large. Future studies need to consider hand skill and degree of hand preference in MLD.

The neural differences of arithmetic verification performance depend on math skill: Evidence from event-related potential

AIM

Math skill is a basic need for an individual, as a career prospect. However, little is known about early brain processes of arithmetic between individuals with different math skill. Therefore, we questioned the modulation of the amplitude of an early negative component by math skill level in an arithmetic verification paradigm using event-related potential (ERP).

METHODS

Thirty-six right-handed participants were assigned in two groups of high- and low-performing students. Their electroencephalogram was recorded while they completed an arithmetic verification task. Simple arithmetic operands were made by random digits from 1 to 9. Addition and subtraction operations were equally used in correct and incorrect responses. The accuracy scores, reaction times, and peak amplitude of the negativity in 200-400 ms time window were analyzed.

RESULTS

The high-performing group showed significantly higher response speeds, and they were more accurate than the low-performing group. The group × region interaction effect was significant. The high-performing group showed a significantly greater negativity, particularly in parietal region, while the low-performing group showed a significantly deeper negativity in frontal and prefrontal region. In the low-performing group, there were significant peak amplitude differences between the anterior and posterior areas. However, such differences were not detected in the high-performing group.

CONCLUSION

Students with different mathematical performance showed distinct patterns in early processing of arithmetic verification, as reflected by differences in negativity at 200-400 ms at anterior and posterior. This suggests that ERPs could be used to differentiate math mastery at neural level which is beneficial in educational and clinical contexts.

QEEG coherence patterns related to mathematics ability in children

The current study investigates the utility of resting-state EEG coherence values in predicting standardized math scores in children. Quantitative EEG and standardized academic achievement analyses were performed on 60 school-aged children. Analyses assessing intrahemispheric coherence at rest were conducted across the sample of participants and several coherence networks were extracted and compared to standardized math achievement values. Specifically, networks that included Brodmann area 40 (a brain region involved in the cognitive processes responsible for mathematics performance) and whose coherence values were significantly correlated with standardized math scores were examined. Results indicate a total of four coherence networks, two in each hemisphere, that have utility in predicting general math skills in children. In addition to BA 40, these coherence networks include BAs in the right temporal lobe, right frontoparietal lobe, left superior temporal lobe, and the left medial prefrontal cortex. These findings address the current dearth of research on the neurological connectivity patterns that are foundational for mathematics abilities in children. Further, these results lay a foundation for the supplementary use of EEG in the assessment and identification practices surrounding math learning disabilities in children and additionally provide a neurocognitive framework upon which intervention research may be targeted.

A brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics

Recent studies aiming to facilitate mathematical skill development in primary school children have explored the electrophysiological characteristics associated with different levels of arithmetic achievement. The present work introduces an alternative EEG signal characterization using graph metrics and, based on such features, a classification analysis using a decision tree model. This proposal aims to identify group differences in brain connectivity networks with respect to mathematical skills in elementary school children. The methods of analysis utilized were signal-processing (EEG artifact removal, Laplacian filtering, and magnitude square coherence measurement) and the characterization (Graph metrics) and classification (Decision Tree) of EEG signals recorded during performance of a numerical comparison task. Our results suggest that the analysis of quantitative EEG frequency-band parameters can be used successfully to discriminate several levels of arithmetic achievement. Specifically, the most significant results showed an accuracy of 80.00% (α band), 78.33% (δ band), and 76.67% (θ band) in differentiating high-skilled participants from low-skilled ones, averaged-skilled subjects from all others, and averaged-skilled participants from low-skilled ones, respectively. The use of a decision tree tool during the classification stage allows the identification of several brain areas that seem to be more specialized in numerical processing.

Detrended Fluctuation, Coherence, and Spectral Power Analysis of Activation Rearrangement in EEG Dynamics During Cognitive Workload

In the study of human cognitive activity using electroencephalogram (EEG), the brain dynamics parameters and characteristics play a crucial role. They allow to investigate the changes in functionality depending on the environment and task performance process, and also to access the intensity of the brain activity in various locations of the cortex and its dependencies. Usually, the dynamics of activation of different brain areas during the cognitive tasks are being studied by spectral analysis based on power spectral density (PSD) estimation, and coherence analysis, which are de facto standard tools in quantitative characterization of brain activity. PSD and coherence reflect the strength of oscillations and similarity of the emergence of these oscillations in the brain, respectively, while the concept of stability of brain activity over time is not well defined and less formalized. We propose to employ the detrended fluctuation analysis (DFA) as a measure of the EEG persistence over time, and use the DFA scaling exponent as its quantitative characteristics. We applied DFA to the study of the changes in activation in brain dynamics during mental calculations and united it with PSD and coherence estimation. In the experiment, EEGs during resting state and mental serial subtraction from 36 subjects were recorded and analyzed in four frequency ranges: θ1 (4.1-5.8 Hz), θ2 (5.9-7.4 Hz), β1 (13-19.9 Hz), and β2 (20-25 Hz). PSD maps to access the intensity of cortex activation and coherence to quantify the connections between different brain areas were calculated, the distribution of DFA scaling exponent over the head surface was exploited to measure the time characteristics of the dynamics of brain activity. Obtained arrangements of DFA scaling exponent suggest that normal functioning of the brain is characterized by long-term temporal correlations in the cortex. Topographical distribution of the DFA scaling exponent was comparable for θ and β frequency bands, demonstrating the largest values of DFA scaling exponent during cognitive activation. The study shows that the long-term temporal correlations evaluated by DFA can be of great interest for diagnosis of the variety of brain dysfunctions of different etiology in the future.

Electroencephalograms during Mental Arithmetic Task Performance

This work has been carried out to support the investigation of the electroencephalogram (EEG) Fourier power spectral, coherence, and detrended fluctuation characteristics during performance of mental tasks. To this aim, the presented dataset contains International 10/20 system EEG recordings from subjects under mental cognitive workload (performing mental serial subtraction) and the corresponding reference background EEGs. Based on the subtraction task performance (number of subtractions and accuracy of the result), the subjects were divided into good counters and bad counters (for whom the mental task required excessive efforts). The data was recorded from 36 healthy volunteers of matched age, all of whom are students of Educational and Scientific Centre “Institute of Biology and Medicine”, National Taras Shevchenko University of Kyiv (Ukraine); the recordings are available through Physiobank platform. The dataset can be used by the neuroscience research community studying brain dynamics during cognitive workload.

Dataset on the EEG time-frequency representation in children with different levels of mathematical achievement

This article presents the data related to the research paper entitled “The analysis of EEG coherence reflects middle childhood differences in mathematical achievement” (González-Garrido et al., 2018). The dataset is derived from the electroencephalographic (EEG) records registered from a total of 60 8–9-years-old children with different math skill levels (High: HA, Average: AA, and Low Achievement: LA) while performing a symbolic magnitude comparison task. The average brain patterns are shown through Time-Frequency Representations (TFR) for each group, and also grand-mean amplitudes within specific EEG epochs in a 19-electrode array are provided. Making this information publicly available for further analyses could significantly contribute to a better understanding on how math achievement in children associates with cognitive processing strategies.