Significant EEG Features Involved in Mathematical Reasoning: Evidence from Wavelet Analysis

Math Skills: a New Look from Functional Data Analysis

Mental calculations involve various areas of the brain. The frontal, parietal and temporal lobes of the left hemisphere have a principal role in the completion of this typology of tasks. Their level of activation varies based on the mathematical competence and attentiveness of the subject under examination and the perceived difficulty of the task. Recent literature often investigates patterns of cerebral activity through fMRI, which is an expensive technique. In this scenario, EEGs represent a more straightforward and cheaper way to collect information regarding brain activity. In this work, we propose an EEG based method to detect differences in the cerebral activation level of people characterized by different abilities in carrying out the same arithmetical task. Our approach consists in the extraction of the activation level of a given region starting from the EEG acquired during resting state and during the completion of a subtraction task. We then analyze these data through Functional Data Analysis, a statistical technique that allows operating on biomedical signals as if they were functions. The application of this technique allowed for the detection of distinct cerebral patterns among the two groups and, more specifically, highlighted the presence of higher levels of activation in the parietal lobe in the population characterized by a lower performance.

Infants Oscillatory Frequencies change during Free-Play

Social interactions are known to be an essential component of infant development. For this reason, exploring functional neural activity while infants are engaged in social interactions will enable a better understanding of the infant social brain. This in turn, will enable the beginning of disentangling the neural basis of social and non-social interactions as well as the influence that maternal engagement has on infant brain function. Maternal sensitivity serves as a model for socio-emotional development during infancy, which poses the question: do interactions between parents and their offspring present altered electrophysiological responses in comparison to the general population if said parents are at risk of mental health disorders? The current research aimed to observe the oscillatory activity of 6-month-old infants during spontaneous free-play interactions with their mother. A 5-minute unconstrained free-play session was recorded between infant-mother dyads with EEG recordings taken from the 6-month-old infants (n = 64). During the recording, social and non-social behaviours were coded and EEG assessed with these epochs. Results showed an increase in oscillatory activity both when an infant played independently or interacted with their mother and oscillatory power was greatest in the alpha and theta bands. In the present 6-month-old cohort, no hemispheric power differences were observed as oscillatory power in the corresponding neural regions (i.e. left and right temporal regions) appeared to mirror each other. Instead, temporal estimates were larger and different from all other regions, whilst the frontal and parietal regions bihemispherically displayed similar estimates, which were larger than those observed centrally, but smaller than those displayed in the temporal locations. The interactions observed between the behavioural events and frequency bands demonstrated a significant reduction in power comparative to the power observed in the gamma band during the baseline event. The present research sought to explore the obstacle of artificial play paradigms for neuroscience research, whereby researchers question how much these paradigms relate to reality. The present manuscript will discuss the strengths and limitations of taking an unconstrained free-play approach.

Technology-Based Innovations to Foster Personalized Healthy Lifestyles and Well-Being: A Targeted Review

BACKGROUND

New community-based arrangements and novel technologies can empower individuals to be active participants in their health maintenance, enabling people to control and self-regulate their health and wellness and make better health- and lifestyle-related decisions. Mobile sensing technology and health systems responsive to individual profiles combined with cloud computing can expand innovation for new types of interoperable services that are consumer-oriented and community-based. This could fuel a paradigm shift in the way health care can be, or should be, provided and received, while lessening the burden on exhausted health and social care systems.

OBJECTIVE

Our goal is to identify and discuss the main scientific and engineering challenges that need to be successfully addressed in delivering state-of-the-art, ubiquitous eHealth and mHealth services, including citizen-centered wellness management services, and reposition their role and potential within a broader context of diverse sociotechnical drivers, agents, and stakeholders.

METHODS

We review the state-of-the-art relevant to the development and implementation of eHealth and mHealth services in critical domains. We identify and discuss scientific, engineering, and implementation-related challenges that need to be overcome to move research, development, and the market forward.

RESULTS

Several important advances have been identified in the fields of systems for personalized health monitoring, such as smartphone platforms and intelligent ubiquitous services. Sensors embedded in smartphones and clothes are making the unobtrusive recognition of physical activity, behavior, and lifestyle possible, and thus the deployment of platforms for health assistance and citizen empowerment. Similarly, significant advances are observed in the domain of infrastructure supporting services. Still, many technical problems remain to be solved, combined with no less challenging issues related to security, privacy, trust, and organizational dynamics.

CONCLUSIONS

Delivering innovative ubiquitous eHealth and mHealth services, including citizen-centered wellness and lifestyle management services, goes well beyond the development of technical solutions. For the large-scale information and communication technology-supported adoption of healthier lifestyles to take place, crucial innovations are needed in the process of making and deploying usable empowering end-user services that are trusted and user-acceptable. Such innovations require multidomain, multilevel, transdisciplinary work, grounded in theory but driven by citizens' and health care professionals' needs, expectations, and capabilities and matched by business ability to bring innovation to the market.

Electroencephalogram associations to cognitive performance in clinically active nurses

Cognitive impairment is traditionally identified via cognitive screening tools that have limited ability in detecting early or transitional stages of impairment. The dynamic nature of physiological variables such as the electroencephalogram (EEG) may provide alternate means for detecting these transitions. However, previous research examining EEG and cognitive performance is largely confined to samples with diagnosed cognitive impairments, and research examining non-impaired, and occupation specific samples, is limited. The present study aimed to investigate the associations between frontal pole and central EEG and cognitive performance in a sample of male and female nurses, and to determine the significance of these associations. Fifty seven nurses participated in the study, in which two lead bipolar EEG was recorded at positions Fp1 (frontal polar), Fp2, C3 (central) and C4 during a baseline and an active phase involving the common neuropsychological Stroop test. Participants' cognitive performance was assessed using the mini-mental state exam (MMSE) and Cognistat screening tools. Significant correlations between EEG beta activity and the outcome of MMSE and Cognistat were revealed, where an increased beta activity was associated to an increased global cognitive performance. Additionally, domain specific cognitive performance was also significantly associated to various EEG variables. The study identified potential EEG biomarkers for global and domain specific cognitive performance, and provides initial groundwork for the development of future EEG based biomarkers for detection of cognitive pathologies.

Eating breakfast enhances the efficiency of neural networks engaged during mental arithmetic in school-aged children

To determine the influence of a morning meal on complex mental functions in children (8-11 y), time-frequency analyses were applied to electroencephalographic (EEG) activity recorded while children solved simple addition problems after an overnight fast and again after having either eaten or skipped breakfast. Power of low frequency EEG activity [2 Hertz (Hz) bands in the 2-12 Hz range] was determined from recordings over frontal and parietal brain regions associated with mathematical thinking during mental calculation of correctly answered problems. Analyses were adjusted for background variables known to influence or reflect the development of mathematical skills, i.e., age and measures of math competence and math fluency. Relative to fed children, those who continued to fast showed greater power increases in upper theta (6-8 Hz) and both alpha bands (8-10 Hz; 10-12 Hz) across sites. Increased theta suggests greater demands on working memory. Increased alpha may facilitate task-essential activity by suppressing non-task-essential activity. Fasting children also had greater delta (2-4 Hz) and greater lower-theta (4-6 Hz) power in left frontal recordings-indicating a region-specific emphasis on both working memory for mental calculation (theta) and activation of processes that suppress interfering activity (delta). Fed children also showed a significant increase in correct responses while children who continued to fast did not. Taken together the findings suggest that neural network activity involved in processing numerical information is functionally enhanced and performance is improved in children who have eaten breakfast, whereas greater mental effort is required for this mathematical thinking in children who skip breakfast.

Applied strategies towards EEG/MEG biomarker identification in clinical and cognitive research

As the underlying causes of several neuronal disorders and neurodegenerative diseases still remain, to some extent, unknown and no accurate diagnostic tests are available, the identification of prognostic and predictive neurophysiological biomarkers has attracted tremendous interest. The continuous advancement of neuroscience methods applied in EEG and magnetoencephalography has been successful in capturing brain processes and identifying persistent cognitive deficits. In this article, the most promising approaches of this rapidly evolving field, along with some indicative clinical applications in major neuropathophysiological disorders, are reviewed. Such strategies for biomarker identification will lead the way to future clinical applications even if, currently, EEG biomarkers are in a premature state.

A decision support framework for the discrimination of children with controlled epilepsy based on EEG analysis

BACKGROUND

In this work we consider hidden signs (biomarkers) in ongoing EEG activity expressing epileptic tendency, for otherwise normal brain operation. More specifically, this study considers children with controlled epilepsy where only a few seizures without complications were noted before starting medication and who showed no clinical or electrophysiological signs of brain dysfunction. We compare EEG recordings from controlled epileptic children with age-matched control children under two different operations, an eyes closed rest condition and a mathematical task. The aim of this study is to develop reliable techniques for the extraction of biomarkers from EEG that indicate the presence of minor neurophysiological signs in cases where no clinical or significant EEG abnormalities are observed.

METHODS

We compare two different approaches for localizing activity differences and retrieving relevant information for classifying the two groups. The first approach focuses on power spectrum analysis whereas the second approach analyzes the functional coupling of cortical assemblies using linear synchronization techniques.

RESULTS

Differences could be detected during the control (rest) task, but not on the more demanding mathematical task. The spectral markers provide better diagnostic ability than their synchronization counterparts, even though a combination (or fusion) of both is needed for efficient classification of subjects.

CONCLUSIONS

Based on these differences, the study proposes concrete biomarkers that can be used in a decision support system for clinical validation. Fusion of selected biomarkers in the Theta and Alpha bands resulted in an increase of the classification score up to 80% during the rest condition. No significant discrimination was achieved during the performance of a mathematical subtraction task.

Assessment of neural dynamic coupling and causal interactions between independent EEG components from cognitive tasks using linear and nonlinear methods

Over the past few years there has been an increased interest in studying the underlying neural mechanism of cognitive brain activity. In this direction, we study the brain activity based on its independent components instead of the EEG signal itself. Both linear and nonlinear synchronization measures are applied to EEG components, which are free of volume conduction effects and background noise. More specifically, a robust nonlinear state-space generalized synchronization assessment method and the recently introduced partial directed coherence are investigated in a working memory paradigm, during mental rehearsal of pictures. The latter is a linear method able to assess not only the independence of the brain regions, but also the direction of the statistically significant relationships. The results are in accordance with previous psychophysiology studies suggesting increased synchrony between prefrontal and parietal components during the rehearsal process, most prominently in gamma (ca. 40 Hz) band. This study indicates that functional connectivity during cognitive processes may be successfully assessed using independent components, which reflect distinct spatial patterns of activity.

Optimal brain network synchrony visualization: application in an alcoholism paradigm

Although Electroencephalographic (EEG) signal synchronization studies have been a topic of increasing interest lately, there is no similar effort in the visualization of such measures. In this direction a graph-theoretic approach devised to study and stress the coupling dynamics of task-performing dynamical networks is proposed. Both linear and nonlinear interdependence measures are investigated in an alcoholism paradigm during mental rehearsal of pictures, which is known to reflect synchronization impairment. More specifically, the widely used magnitude squared coherence; phase synchronization and a robust nonlinear state-space generalized synchronization assessment method are investigated. This paper mostly focuses on a signal-based technique of selecting the optimal visualization threshold using surrogate datasets to correctly identify the most significant correlation patterns. Furthermore, a graph statistical parameter attempts to capture and quantify collective motifs present in the functional brain network. The results are in accordance with previous psychophysiology studies suggesting that an alcoholic subject has impaired synchronization of brain activity and loss of lateralization during the rehearsal process, most prominently in alpha (8-12 Hz) band, as compared to a control subject. Lower beta (13-30 Hz) synchronization was also evident in the alcoholic subject.

Parametric and nonparametric EEG analysis for the evaluation of EEG activity in young children with controlled epilepsy

There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform) and a parametric, signal modeling technique (ARMA) are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest) task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.