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Gestures and speech, as linked communicative expressions, form an integrated system. Previous functional magnetic resonance imaging studies have suggested that neural networks for gesture and spoken word production share similar brain regions consisting of fronto-temporo-parietal brain regions. However, information flow within the neural network may dynamically change during the planning of two communicative expressions and also differ between them. To investigate dynamic information flow in the neural network during the planning of gesture and spoken word generation in this study, participants were presented with spatial images and were required to plan the generation of gestures or spoken words to represent the same spatial situations. The evoked potentials in response to spatial images were recorded to analyze the effective connectivity within the neural network. An independent component analysis of the evoked potentials indicated 12 clusters of independent components, the dipoles of which were located in the bilateral fronto-temporo-parietal brain regions and on the medial wall of the frontal and parietal lobes. Comparison of effective connectivity indicated that information flow from the right middle cingulate gyrus (MCG) to the left supplementary motor area (SMA) and from the left SMA to the left precentral area increased during gesture planning compared with that of word planning. Furthermore, information flow from the right MCG to the left superior frontal gyrus also increased during gesture planning compared with that of word planning. These results suggest that information flow to the brain regions for hand praxis is more strongly activated during gesture planning than during word planning. [ABSTRACT FROM AUTHOR]

In order to achieve the accurate identifications of various electroencephalograms (EEGs) and electrocardiograms (ECGs), a unified framework of wavelet scattering transform (WST), bidirectional weighted two-directional two-dimensional principal component analysis (BW(2D)2PCA) and grey wolf optimization based kernel extreme learning machine (KELM) was put forward in this study. To extract more discriminating features in the WST domain, the BW(2D)2PCA was proposed based on original two-directional two-dimensional principal component analysis, by considering both the contribution of eigenvalue and the variation of two adjacent eigenvalues. Totally fifteen classification tasks of classifying normal vs interictal vs ictal EEGs, non-seizure vs seizure EEGs and normal vs congestive heart failure (CHF) ECGs were investigated. Applying patient non-specific strategy, the proposed scheme reported ACCs of no less than 99.300 ± 0.121 % for all the thirteen classification cases of Bonn dataset in classifying normal vs interictal vs ictal EEGs, MCC of 90.947 ± 0.128 % in distinguishing non-seizure vs seizure EEGs of CHB-MIT dataset, and MCC of 99.994 ± 0.001 % in identifying normal vs CHF ECGs of BBIH dataset. Experimental results indicate BW(2D)2PCA based framework outperforms (2D)2PCA based scheme, the high-performance results manifest the effectiveness of the proposed framework and our proposal is superior to most existing approaches. [ABSTRACT FROM AUTHOR]

Gestures and speech, as linked communicative expressions, form an integrated system. Previous functional magnetic resonance imaging studies have suggested that neural networks for gesture and spoken word production share similar brain regions consisting of fronto-temporo-parietal brain regions. However, information flow within the neural network may dynamically change during the planning of two communicative expressions and also differ between them. To investigate dynamic information flow in the neural network during the planning of gesture and spoken word generation in this study, participants were presented with spatial images and were required to plan the generation of gestures or spoken words to represent the same spatial situations. The evoked potentials in response to spatial images were recorded to analyze the effective connectivity within the neural network. An independent component analysis of the evoked potentials indicated 12 clusters of independent components, the dipoles of which were located in the bilateral fronto-temporo-parietal brain regions and on the medial wall of the frontal and parietal lobes. Comparison of effective connectivity indicated that information flow from the right middle cingulate gyrus (MCG) to the left supplementary motor area (SMA) and from the left SMA to the left precentral area increased during gesture planning compared with that of word planning. Furthermore, information flow from the right MCG to the left superior frontal gyrus also increased during gesture planning compared with that of word planning. These results suggest that information flow to the brain regions for hand praxis is more strongly activated during gesture planning than during word planning.

The purpose of the present study is to explore how the emotionalized expression of news content in the posttruth era affects the cognitive processing of the audiences. One news that was text-written with two different expression types (emotional expression vs. neutral expression) was adopted as an experiment material in the study, and changes in cortical activity during news reporting reading tasks were examined with electroencephalograms, sampled from nine sites and four channels and analyzed with weighted phase lag index (wPLI) based on brain functional connectivity (FC) method. The results show that emotional discourses caused a stronger cortical brain activity and more robust brain FC (beta oscillations); besides, reading emotionalized expression consumed more attention resources but fewer cognitive resources, which may impede further rational thinking of the audiences.

The purpose of the present study is to explore how the emotionalized expression of news content in the posttruth era affects the cognitive processing of the audiences. One news that was text-written with two different expression types (emotional expression vs. neutral expression) was adopted as an experiment material in the study, and changes in cortical activity during news reporting reading tasks were examined with electroencephalograms, sampled from nine sites and four channels and analyzed with weighted phase lag index (wPLI) based on brain functional connectivity (FC) method. The results show that emotional discourses caused a stronger cortical brain activity and more robust brain FC (beta oscillations); besides, reading emotionalized expression consumed more attention resources but fewer cognitive resources, which may impede further rational thinking of the audiences. [ABSTRACT FROM AUTHOR]

There is a growing interest in human gamma‐band oscillatory activity due to its direct link to neuronal populations, its associations with many cognitive processes, and its positive relationship with fMRI BOLD signal. Visual gamma has been successfully detected using concurrent EEG‐fMRI recordings and linked to activity in the visual cortex using voxel‐wise regression analysis. As gamma‐band oscillations reflect predominantly feedforward projections between brain regions, its inclusion in functional connectivity analysis is highly recommended; however, very few studies have investigated this line of research. In the current study, we aimed to explore this gap by asking which fMRI brain network is related to gamma activity induced by the color discrimination task. Advanced denoising strategies and multitaper spectral decomposition were applied to EEG data to detect gamma oscillations, and group independent component analysis was performed on fMRI data to identify task‐related neural networks. Despite using only trials without motor response (50% of the trials), the two neural measures were successfully coupled. One of the six task‐related networks, the occipito‐parietal network, exhibited significant trial‐by‐trial covariations with gamma oscillations. In addition to the expected extrastriate visual cortex, the network encompasses extensive brain activations in the precuneus, bilateral intraparietal, and anterior insular cortices. We argue that the visual cortex is the source of gamma, whereas the remaining brain regions exhibit feedforward and feedback connections related to this oscillatory activity. Our findings provide evidence for the electrophysiological basis of the connectivity revealed by BOLD signal and impart novel insights into the neural mechanism of color discrimination. Our brain works in extremely complex ways to perform even the simplest cognitive tasks, such as discriminating a green dot from a red dot. Using fMRI, we identified several neural networks that were engaged in this task. Using simultaneously recorded EEG, we detected sustained gamma activity in response to this visual stimulus. A trial‐by‐trial coupling revealed that gamma activity was linked to a specific neural network—the occipito‐parietal network. Our findings impart novel insights into the electrophysiological basis of fMRI brain networks during cognitive tasks and the neural mechanism of color perception. [ABSTRACT FROM AUTHOR]

In this paper, we perform statistical analysis of time series deriving from four neurodegenerative disorders, namely epilepsy, amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), Huntington’s disease (HD). The time series are concerned with electroencephalograms (EEGs) of healthy and epileptic states, as well as gait dynamics (in particular stride intervals) of the ALS, PD and HDs. We study data concerning one subject for each neurodegenerative disorder and one healthy control. The analysis is based on Tsallis non-extensive statistical mechanics and in particular on the estimation of Tsallis q-triplet, namely {qstat, qsen, qrel}. The deviation of Tsallis q-triplet from unity indicates non-Gaussian statistics and long-range dependencies for all time series considered. In addition, the results reveal the efficiency of Tsallis statistics in capturing differences in brain dynamics between healthy and epileptic states, as well as differences between ALS, PD, HDs from healthy control subjects. The results indicate that estimations of Tsallis q-indices could be used as possible biomarkers, along with others, for improving classification and prediction of epileptic seizures, as well as for studying the gait complex dynamics of various diseases providing new insights into severity, medications and fall risk, improving therapeutic interventions.

As social robots become more and more intelligent and autonomous in operation, it is extremely important to ensure that such robots act in socially acceptable manner. More specifically, if such an autonomous robot is capable of generating and expressing emotions of its own, it should also have an ability to reason if it is ethical to exhibit a particular emotional state in response to a surrounding event. Most existing computational models of emotion for social robots have focused on achieving a certain level of believability of the emotions expressed. We argue that believability of a robot’s emotions, although crucially necessary, is not a sufficient quality to elicit socially acceptable emotions. Thus, we stress on the need of higher level of cognition in emotion processing mechanism which empowers social robots with an ability to decide if it is socially appropriate to express a particular emotion in a given context or it is better to inhibit such an experience. In this paper, we present the detailed mathematical explanation of the ethical reasoning mechanism in our computational model, EEGS, that helps a social robot to reach to the most socially acceptable emotional state when more than one emotions are elicited by an event. Experimental results show that ethical reasoning in EEGS helps in the generation of believable as well as socially acceptable emotions. [ABSTRACT FROM AUTHOR]

Evidence from behavioral studies has suggested a capacity existed in working memory. As the concept of functional connectivity has been introduced into neuroscience research in the recent years, the aim of this study is to investigate the functional connectivity in the brain when working memory load reaches the capacity. 32-channel electroencephalographs (EEGs) were recorded for 16 healthy subjects, while they performed a visual working memory task with load 1–6. Individual working memory capacity was calculated according to behavioral results. Short-time Fourier transform was used to determine the principal frequency band (theta band) related to working memory. The functional connectivity among EEGs was measured by the directed transform function (DTF) via spectral Granger causal analysis. The capacity was 4 calculated from the behavioral results. The power was focused in the frontal midline region. The strongest connectivity strengths of EEG theta components from load 1 to 6 distributed in the frontal midline region. The curve of DTF values vs load numbers showed that DTF increased from load 1 to 4, peaked at load 4, then decreased after load 4. This study finds that the functional connectivity between EEGs, described quantitatively by DTF, became less strong when working memory load exceeded the capacity. [ABSTRACT FROM AUTHOR]

Previous studies have reported that multiple brain regions are activated during spatial navigation, but it remains unclear how this activation is converted to motor commands for navigation. This study was aimed to investigate synchronization across different brain regions and between cortical areas and muscles during spatial navigation. This synchronization has been suggested to be essential for integrating activity in the multiple brain areas to support higher cognitive functions and for conversion of cortical activity to motor commands. In the present study, the subjects were required to sequentially trace ten checkpoints in a virtual town by manipulating a joystick and to perform this three times while electroencephalograms and electromyograms from the right arm were monitored. Time spent on the task in the third trial was significantly lesser than that in the first trial indicating an improvement in task performance. This repeated learning was associated with an increase in alpha power at the electrodes over the contralateral sensorimotor region and in theta power at the electrodes over the bilateral premotor and frontotemporal regions. Alpha- and theta-range corticocortical coherences between these regions and other brain areas were also increased in the third trial compared to the first trial. Furthermore, alpha- and theta-range corticomuscular coherence was significantly increased in the second and third trials compared to the first trial. These results suggest that alpha- and theta-range synchronous activity across multiple systems is essential for the integrated brain activity required in spatial navigation and for the conversion of this activity to motor commands. [ABSTRACT FROM AUTHOR]

Abstract: Native Americans have some of the highest rates of marijuana and alcohol use and abuse, yet neurobiological measures associated with dependence on these substances in this population remain unknown. The present investigation evaluated the heritability of spectral characteristics of the electroencephalogram (EEG) and their correlation with marijuana and alcohol dependence in an American Indian community. Participants (n =626) were evaluated for marijuana (MJ) and alcohol (ALC) dependence, as well as other psychiatric disorders. EEGs were collected from six cortical sites and spectral power determined in five frequency bands (delta 1.0–4.0Hz, theta 4.0–7.5Hz, alpha 7.5–12.0Hz, low beta 12.0–20.0Hz and high beta/gamma 20–50Hz). The estimated heritability (h 2) of the EEG phenotypes was calculated using SOLAR, and ranged from 0.16 to 0.67. Stepwise linear regression was used to detect correlations between MJ and ALC dependence and the spectral characteristics of the EEG using a model that took into account: age, gender, Native American Heritage (NAH) and a lifetime diagnosis of antisocial personality and/or conduct disorder (ASPD/CD). Increases in spectral power in the delta frequency range, were significantly correlated with gender (p <0.001) and marijuana dependence (p <0.003). Gender, age, NAH and ASPD/CD were all significantly (p <0.001) correlated with theta, alpha and beta band power, whereas alcohol dependence (p <0.01), gender (p <0.001), and ASPD/CD (p <0.001) were all correlated with high beta/gamma band power. These data suggest that the traits of EEG delta and high beta/gamma activity are correlated with MJ dependence and alcohol dependence, respectively, in this community sample of Native Americans. [Copyright &y& Elsevier]

Objectives: The quantification of subtle patterns in sequential data, and their changes, has considerable potential utility throughout psychiatry, including the analyses of mood ratings, heart rate, respiratory, and electroencephalographic recordings. Methods: Approximate entropy (ApEn), a relatively recently developed statistic quantifying serial irregularity, has been applied in numerous studies throughout mathematics and other fields of study, especially biology. Results: We discussed applications of ApEn, both extant and potential, of most relevance to psychiatrists. We provided a mechanistic interpretation of lowered ApEn values, and discusses the relationship between ApEn and other (both classical and complexity) measures of serial dynamics. We also briefly discussed cross-ApEn, a thematically similar quantification of two-variable asynchrony that can aid in uncovering subtle disruptions in complicated network dynamics. Conclusions: ApEn and cross-ApEn have significant potential to consequentially enhance present statistical methodologies of analysis of psychiatric data, in both clinical and in research settings. [ABSTRACT FROM AUTHOR]

Abstract: Various time–frequency methods have been used to study time-varying properties of non-stationary neurophysiological signals. In the present study, a time–frequency coherence estimate using continuous wavelet transform (CWT) together with its confidence intervals are proposed to evaluate the correlation between two non-stationary processes. The approach is based on averaging over repeat trials. A systematic comparison between approaches using CWT and short-time Fourier transform (STFT) is carried out. Simulated data are generated to test the performance of these methods when estimating time–frequency based coherence. In contrast to some recent studies, we find that CWT based coherence estimates do not supersede STFT based estimates. We suggest that a combination of STFT and CWT would be most suitable for analysing non-stationary neural data. Tests are presented to investigate the time and frequency discrimination capabilities of the two approaches. The methods are applied to two experimental data sets: electroencephalogram (EEG) and surface electromyogram (EMG) during wrist movements in a healthy subject, and local field potential (LFP) and surface EMG recordings during resting tremor in a Parkinsonian patient. Supporting software is available at http://www.dcs.warwick.ac.ukffeng/software/COD and http://www.neurospec.org. [Copyright &y& Elsevier]

The effects of cocaine and pseudococaine on the EEGs, heart and respiratory rates, and self-administration behavior were studied in rhesus monkeys. An intravenous injection of cocaine (2.5 and 4.0 mg/kg) in the monkey produced low-voltage fast waves (LVFWs) in the EEGs and behavioral hyperexcitation accompanied by marked increases in the heart and respiratory rates with mydriasis and excessive salivation. In contrast, pseudococaine produced high-voltage slow waves (HVSWs) in the EEGs and behavioral depression accompanied by the same symptoms of the autonomic functions as those produced by cocaine. Both isomers were self-administered by the monkeys. During cocaine self-administration sessions, the animals showed hyperexcitation in their overall behavior, while with pseudococaine thay showed almost normal behavioral responses. These results suggest that cocaine produced excitatory effects and pseudococaine inhibitory effects on the EEGs and behavior. Both isomers stimulate the heart and respiratory rates, and were self-administered by the monkeys. [ABSTRACT FROM AUTHOR]

We studied precise spectra of evoked EEGs in the word recognition analyzed with multiple band-pass filters (MBFA). The filter was infinite impulse response in 2nd order. Firstly, a signal swept from 5 to 100 Hz sine wave was tested with MBFA, fast Fourier transform and Wavelet transform. Ten evoked EEGs with word visual stimuli were recorded from three volunteers. The EEGs were sampled at 1 kHz in 14 bits for 1 s, and were analyzed with MBFA to make spectra from 5 to 100 Hz, and the spectra were averaged 10 times for observation. Evoked powers were noted at alpha band over the occipital area and suppressed powers were noted at beta and alpha bands afterward. Gamma bands were noted to be evoked over the posterior central area in the normalized Event-related spectra. [Copyright &y& Elsevier]

An increasing body of research has investigated how bilingual language experience changes brain structure and function, including changes to task-free, or "resting-state" brain connectivity. Such findings provide important evidence about how the brain continues to be shaped by different language experiences throughout the lifespan. The neural effects of bilingual language experience can provide evidence about the additional processing demands placed on the linguistic and/or executive systems by dual-language use. While considerable research has used MRI to examine where these changes occur, such methods cannot reveal the temporal dynamics of functioning brain networks at rest. The current study used data from task-free EEGS to disentangle how the linguistic and cognitive demands of bilingual language use impact brain functioning. Data analyzed from 106 bilinguals and 91 monolinguals revealed that bilinguals had greater alpha power, and significantly greater and broader coherence in the alpha and beta frequency ranges than monolinguals. Follow-up analyses showed that higher alpha was related to language control: more second-language use, higher native-language proficiency, and earlier age of second-language acquisition. Bilateral beta power was related to native-language proficiency, whereas theta was related to native-language proficiency only in left-hemisphere electrodes. The results contribute to our understanding of how the linguistic and cognitive requirements of dual-language use shape intrinsic brain activity, and what the broader implications for information processing may be., Competing Interests: Competing Interests: The authors have declared that no competing interests exist., (© 2020 Massachusetts Institute of Technology.)

Subliminal electromagnetic fields (EMFs) triggered nonlinear evoked potentials in awake but not anesthetized animals, and increased glucose metabolism in the hindbrain. Field detection occurred somewhere in the head and possibly was an unrecognized function of sensory neurons in facial skin, which synapse in the trigeminal nucleus and project to the thalamus via glutamate-dependent pathways. If so, anesthetic agents that antagonize glutamate neurotransmission would be expected to degrade EMF-evoked potentials (EEPs) to a greater extent than agents having different pharmacological effects. We tested the hypothesis using ketamine which blocks N-methyl-d-aspartate (NMDA) receptors (NMDARs), and xylazine which is an α₂-adrenoreceptor agonist. Electroencephalograms (EEGs) of rats were examined using recurrence analysis to observe EEPs in the presence and absence of ketamine and/or xylazine anesthesia. Auditory evoked potentials (AEPs) served as positive controls. The frequency of observation of evoked potentials in a given condition (wake or anesthesia) was compared with that due to chance using the Fisher's exact test. EEPs were observed in awake rats but not while they were under anesthesia produced using a cocktail of xylazine and ketamine. In another experiment each rat was measured while awake and while under anesthesia produced using either xylazine or ketamine. EEPs and AEPs were detected during wake and under xylazine (P<0.05 in each of the four measurements). In contrast, neither EEPs nor AEPs were observed when anesthesia was produced partly or wholly using ketamine. The duration and latency of the EEPs was unaltered by xylazine anesthesia. The afferent signal triggered by the transduction of weak EMFs was likely mediated by NMDAR-mediated glutamate neurotransmission., (Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.)