Task prioritization modulates alpha, theta and beta EEG dynamics reflecting proactive cognitive control

Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI

Brain-computer interfaces use signals from the brain, such as EEG, to determine brain states, which in turn can be used to issue commands, for example, to control industrial machinery. While Cloud computing can aid in the creation and operation of industrial multi-user BCI systems, the vast amount of data generated from EEG signals can lead to slow response time and bandwidth problems. Fog computing reduces latency in high-demand computation networks. Hence, this paper introduces a fog computing solution for BCI processing. The solution consists in using fog nodes that incorporate machine learning algorithms to convert EEG signals into commands to control a cyber-physical system. The machine learning module uses a deep learning encoder to generate feature images from EEG signals that are subsequently classified into commands by a random forest. The classification scheme is compared using various classifiers, being the random forest the one that obtained the best performance. Additionally, a comparison was made between the fog computing approach and using only cloud computing through the use of a fog computing simulator. The results indicate that the fog computing method resulted in less latency compared to the solely cloud computing approach.

Intimate Relationships Weaken Female Competition: Evidence from Phase-amplitude Coupling and Event-Related Potentials

Competition, an essential component of social interaction, frequently occurs in daily life, and the impact of intimate relationships on women's competition has not yet been revealed. In this study, the visual target paradigm was used to explore the neural mechanisms underlying the regulation of female competitiveness by intimate relationships using event-related potential (ERP) data, time-frequency analysis, and brain functional connectivity. The research results indicate that, the P1, the N4, and the LPP were sensitive to the impact of intimate relationships on competition. Compared to competition between unfamiliar opposite-gender dyads, the average amplitudes of the N4 and LPP were higher in the late stage of competition between romantic partners. Compared to competition with strangers of the opposite gender, alpha band power was significantly higher when female individuals competed with their romantic partners. In addition, there was a positive correlation between the synchronization of activity in the frontal, parietal, and right temporal lobes of a female's brain and their degree of female engagement in competition. When a female individuals focused on the competition, activity synchronization was higher. The results indicate that competition with unfamiliar individuals of the opposite gender can make female focus on the competitive task, causing synchronous activation of corresponding brain regions. When competing with a romantic partner, women's focus decreases, their willingness to compete decreases, and the synchrony of brain functional connectivity decreases. This study suggests that intimate relationship weakens women's competitiveness, which is of significant importance for understanding high-quality intimate relationship and promoting the development of healthy competition.

Characterizing neurocognitive impairments in Parkinson's disease with mobile EEG when walking and stepping over obstacles

The neural correlates that help us understand the challenges that Parkinson's patients face when negotiating their environment remain under-researched. This deficit in knowledge reflects the methodological constraints of traditional neuroimaging techniques, which include the need to remain still. As a result, much of our understanding of motor disorders is still based on animal models. Daily life challenges such as tripping and falling over obstacles represent one of the main causes of hospitalization for individuals with Parkinson's disease. Here, we report the neural correlates of naturalistic ambulatory obstacle avoidance in Parkinson's disease patients using mobile EEG. We examined 14 medicated patients with Parkinson's disease and 17 neurotypical control participants. Brain activity was recorded while participants walked freely, and while they walked and adjusted their gait to step over expected obstacles (preset adjustment) or unexpected obstacles (online adjustment) displayed on the floor. EEG analysis revealed attenuated cortical activity in Parkinson's patients compared to neurotypical participants in theta (4-7 Hz) and beta (13-35 Hz) frequency bands. The theta power increase when planning an online adjustment to step over unexpected obstacles was reduced in Parkinson's patients compared to neurotypical participants, indicating impaired proactive cognitive control of walking that updates the online action plan when unexpected changes occur in the environment. Impaired action planning processes were further evident in Parkinson's disease patients' diminished beta power suppression when preparing motor adaptation to step over obstacles, regardless of the expectation manipulation, compared to when walking freely. In addition, deficits in reactive control mechanisms in Parkinson's disease compared to neurotypical participants were evident from an attenuated beta rebound signal after crossing an obstacle. Reduced modulation in the theta frequency band in the resetting phase across conditions also suggests a deficit in the evaluation of action outcomes in Parkinson's disease. Taken together, the neural markers of cognitive control of walking observed in Parkinson's disease reveal a pervasive deficit of motor-cognitive control, involving impairments in the proactive and reactive strategies used to avoid obstacles while walking. As such, this study identified neural markers of the motor deficits in Parkinson's disease and revealed patients' difficulties in adapting movements both before and after avoiding obstacles in their path.

Self-Awareness of Goals Task (SAGT) and Planning Skills: The Neuroscience of Decision Making

A goal's self-awareness and the planning to achieve it drive decision makers. Through a neuroscientific approach, this study explores the self-awareness of goals by analyzing the explicit and implicit processes linked to the ability to self-represent goals and sort them via an implicit dominant key. Thirty-five professionals performed a novel and ecological decision-making task, the Self-Awareness of Goals Task (SAGT), aimed at exploring the (i) self-representation of the decision-making goals of a typical working day; (ii) self-representation of how these goals were performed in order of priority; (iii) temporal sequence; and (iv) in terms of their efficacy. Electrophysiological (i.e., alpha, beta, and gamma band), autonomic, behavioral, and self-report data (General Decision Making Style and Big Five Inventory) are collected. Higher self-awareness of goals by time as well as efficacy and the greater activation of alpha, beta, and gamma bands in the temporoparietal brain area were found. Correlations reported positive associations between the self-awareness of goals via a time and dependent decision-making style and a conscientious personality, but also between the self-awareness of goals via an efficacy and rational decision-making style. The results obtained in this study suggest that the SAGT could activate recursive thinking in the examinee and grasp individual differences in self-representation and aware identification of decision-making goals.

Mentalizing and self-other distinction in visual perspective taking: the analysis of temporal neural processing using high-density EEG

This high density EEG report dissects the neural processing in the visual perspective taking using four experimental comparisons (Arrow, Avatar and Self, Other). Early activation differences occurred between the Avatar and the Arrow condition in primary visual pathways concomitantly with alpha and beta phase locked responses predominant in the Avatar condition. In later time points, brain activation was stronger for the Avatar condition in paracentral lobule of frontal lobe. When taking the other's perspective, there was an increased recruitment of generators in the occipital and temporal lobes and later on in mentalizing and salience networks bilaterally before spreading to right frontal lobe subdivisions. Microstate analysis further supported late recruitment of the medial frontal gyrus and precentral lobule in this condition. Other perspective for the Avatar only showed a strong beta response located first in left occipito-temporal and right parietal areas, and later on in frontal lobes. Our EEG data support distinct brain processes for the Avatar condition with an increased recruitment of brain generators that progresses from primary visual areas to the anterior brain. Taking the other's perspective needs an early recruitment of neural processors in posterior areas involved in theory of mind with later involvement of additional frontal generators.