Magnetic stimulation at acupoints relieves mental fatigue: An Event Related Potential (P300) study

Advances in Non-Invasive Neuromodulation Techniques for Improving Cognitive Function: A Review

Non-invasive neuromodulation techniques are widely utilized to study and improve cognitive function, with the aim of modulating different cognitive processes. For workers performing high-intensity mental and physical tasks, extreme fatigue may not only affect their working efficiency but may also lead to cognitive decline or cognitive impairment, which, in turn, poses a serious threat to their physical health. The use of non-invasive neuromodulation techniques has important research value for improving and enhancing cognitive function. In this paper, we review the research status, existing problems, and future prospects of transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial magnetic stimulation (TMS), and transcutaneous acupoint stimulation (TAS), which are the most studied physical methods in non-invasive neuromodulation techniques to improve and enhance cognition. The findings presented in this paper will be of great reference value for the in-depth study of non-invasive neuromodulation techniques in the field of cognition.

How to Tackle Mental Fatigue: A Systematic Review of Potential Countermeasures and Their Underlying Mechanisms

INTRODUCTION

Mental fatigue (MF) is a psychobiological state that impairs cognitive as well as physical performance in different settings. Recently, numerous studies have sought ways to counteract these negative effects of MF. An overview of the explored countermeasures for MF is, however, lacking.

OBJECTIVES

The objective of this review is to provide an overview of the different MF countermeasures currently explored in literature. Countermeasures were classified by the timing of application (before, during or after the moment of MF) and type of intervention (behavioural, physiological and psychological).

METHODS

The databases of PubMed (MEDLINE), Web of Science and PsycINFO were searched until March 7, 2022. Studies were eligible when MF was induced using a task with a duration of at least 30 min, when they assessed MF markers in at least two out of the three areas wherein MF markers have been defined (i.e., behavioural, subjective and/or [neuro]physiological) and used a placebo or control group for the countermeasure.

RESULTS

A total of 33 studies investigated one or more countermeasures against MF. Of these, eight studies assessed a behavioural countermeasure, 22 a physiological one, one a psychological countermeasure and two a combination of a behavioural and psychological countermeasure. The general finding was that a vast majority of the countermeasures induced a positive effect on behavioural (e.g., task or sport performance) and/or subjective MF markers (e.g., visual analogue scale for MF or alertness). No definitive conclusion could be drawn regarding the effect of the employed countermeasures on (neuro)physiological markers of MF as only 19 of the included studies investigated these measures, and within these a large heterogeneity in the evaluated (neuro)physiological markers was present.

DISCUSSION

Within the physiological countermeasures it seems that the use of odours during a MF task or caffeine before the MF task are the most promising interventions in combating MF. Promising behavioural (e.g., listening to music) and psychological (e.g., extrinsic motivation) countermeasures of MF have also been reported. The most assumed mechanism through which these countermeasures operate is the dopaminergic system. However, this mechanism remains speculative as (neuro)physiological markers of MF have been scarcely evaluated to date.

CONCLUSION

The present systematic review reveals that a wide range of countermeasures have been found to successfully counteract MF on a subjective, (neuro)physiological and/or behavioural level. Of these, caffeine, odours, music and extrinsic motivation are the most evidenced for countering MF. To provide in-detail practical guidelines for the real-life application of MF countermeasures, more research must be performed into the underlying mechanisms and into the optimal dosage and time of application/intake.

Mental Fatigue Has Great Impact on the Fractal Dimension of Brain Functional Network

Mental fatigue has serious negative impacts on the brain cognitive functions and has been widely explored by the means of brain functional networks with the neuroimaging technique of electroencephalogram (EEG). Recently, several researchers reported that brain functional network constructed from EEG signals has fractal feature, raising an important question: what are the effects of mental fatigue on the fractal dimension of brain functional network? In the present study, the EEG data of alpha1 rhythm (8-10 Hz) at task state obtained by a mental fatigue model were chosen to construct brain functional networks. A modified greedy colouring algorithm was proposed for fractal dimension calculation in both binary and weighted brain functional networks. The results indicate that brain functional networks still maintain fractal structures even when the brain is at fatigue state; fractal dimension presented an increasing trend along with the deepening of mental fatigue fractal dimension of the weighted network was more sensitive to mental fatigue than that of binary network. Our current results suggested that mental fatigue has great regular impacts on the fractal dimension in both binary and weighted brain functional networks.

Multivariate weighted recurrence network analysis of EEG signals from ERP-based smart home system

Smart home has been widely used to improve the living quality of people. Recently, the brain-computer interface (BCI) contributes greatly to the smart home system. We design a BCI-based smart home system, in which the event-related potentials (ERP) are induced by the image interface based on the oddball paradigm. Then, we investigate the influence of mental fatigue on the ERP classification by the Fisher linear discriminant analysis. The results indicate that the classification accuracy of ERP decreases as the brain evolves from the normal stage to the mental fatigue stage. In order to probe into the difference of the brain, cognitive process between mental fatigue and normal states, we construct multivariate weighted recurrence networks and analyze the variation of the weighted clustering coefficient and weighted global efficiency corresponding to these two brain states. The findings suggest that these two network metrics allow distinguishing normal and mental fatigue states and yield novel insights into the brain fatigue behavior resulting from a long use of the ERP-based smart home system. These properties render the multivariate recurrence network, particularly useful for analyzing electroencephalographic recordings from the ERP-based smart home system.