EEG processing and its application in brain-computer interface

Electroencephalogram (EEG) is an efficient tool in exploring human brains. It plays a very important role in diagnosis of disorders related to epilepsy and development of new interaction techniques between machines and human beings,namely,brain-computer interface (BCI). The purpose of this review is to illustrate the recent researches in EEG processing and EEG-based BCI. First,we outline several methods in removing artifacts from EEGs,and classical algorithms for fatigue detection are discussed. Then,two BCI paradigms including motor imagery and steady-state motion visual evoked potentials (SSMVEP) produced by oscillating Newton's rings are introduced. Finally,BCI systems including wheelchair controlling and electronic car navigation are elaborated. As a new technique to control equipments,BCI has promising potential in rehabilitation of disorders in central nervous system,such as stroke and spinal cord injury,treatment of attention deficit hyperactivity disorder (ADHD) in children and development of novel games such as brain-controlled auto racings.

Research progress on interleukin-33 and its roles in the central nervous system

Interleukin-33 （IL-33）, a newly recognized IL-1 family member, is expressed by various tissues and cells. Since it can combine with chromosomes, IL-33 is regarded as an intracellular transcription repressor. Upon proinflammatory stimulation, it is released as an extracellular cytokine to function as an alarmin to dangerous signals. The IL-33 receptor is a heterodimer complex composed of ST2 and the IL-1 receptor accessory protein, the latter being conserved in other IL-1 family members. The IL-33/ST2 signaling pathway plays critical roles in inflammatory and immune diseases, as well as in central nervous system （CNS） diseases. Recently, there has been an increasing focus on IL-33, particularly on its production and functions in the CNS. The present review mainly focuses on progress in research on IL-33, especially its roles in the CNS.

Leptin in normal physiology and leptin resistance

Since the discovery of leptin as an adipokine in 1994, much progress has been made in the research about leptin. Circulating leptin binds to leptin receptor, activates STAT3-dependent and STAT3-independent signaling pathways, and plays an effective role in energy home- ostasis, neuroendocrine function and metabolism mainly through acting on the central nervous system, especially the hypothalamus. Leptin resistance is considered as a key risk factor for obesity. Various mechanisms have been formu- lated in order to explain leptin resistance, including impairment in leptin transport, attenuation in leptin sig- naling, ER stress, inflammation and deficiency in autop- hagy. Here, we review our current knowledge about leptin action, leptin signaling and leptin resistance, hoping to provide new ideas for the battle against obesity.

Central monoaminergic systems are a site of convergence of signals conveying the experience of exercise to brain circuits involved in cognition and emotional behavior

Physical activity can enhance cognitive function and increase resistance against deleterious effects of stress on mental health. Enhanced cognitive function and stress resistance produced by exercise are conserved among vertebrates, suggesting that ubiquitous mechanisms may underlie beneficial ef- fects of exercise. In the current review, we summarize the beneficial effects of exercise on cognitive function and stress resistance and discuss central and peripheral signaling factors that may be critical for conferring the effects of physical activity to brain circuits involved in cognitive function and stress. Additionally, it is suggested that norepinephrine and serotonin, highly conserved monoamines that are sensitive to exercise and able to modulate behavior in multiple species, could represent a conver- gence between peripheral and central exercise signals that mediate the beneficial effects of exercise. Finally, we offer the novel hypothesis that thermoregulation during exercise could contribute to the emotional effects of exercise by activating a subset of temperature-sensitive serotonergic neurons in the dorsal raphe nucleus that convey anxiolytic and stress-protective signals to forebrain regions. Throughout the review, we discuss limitations to current approaches and offer strategies for future re- search in exercise neuroscience.

A Robust Adaptive Dynamic Programming Principle for Sensorimotor Control with Signal-Dependent Noise

As human beings,people coordinate movements and interact with the environment through sensory information and motor adaptation in the daily lives.Many characteristics of these interactions can be studied using optimization-based models,which assume that the precise knowledge of both the sensorimotor system and its interactive environment is available for the central nervous system(CNS).However,both static and dynamic uncertainties occur inevitably in the daily movements.When these uncertainties are taken into consideration,the previously developed models based on optimization theory may fail to explain how the CNS can still coordinate human movements which are also robust with respect to the uncertainties.In order to address this problem,this paper presents a novel computational mechanism for sensorimotor control from a perspective of robust adaptive dynamic programming(RADP).Sharing some essential features of reinforcement learning,which was originally observed from mammals,the RADP model for sensorimotor control suggests that,instead of identifying the system dynamics of both the motor system and the environment,the CNS computes iteratively a robust optimal control policy using the real-time sensory data.An online learning algorithm is provided in this paper,with rigorous convergence and stability analysis.Then,it is applied to simulate several experiments reported from the past literature.By comparing the proposed numerical results with these experimentally observed data,the authors show that the proposed model can reproduce movement trajectories which are consistent with experimental observations.In addition,the RADP theory provides a unified framework that connects optimality and robustness properties in the sensorimotor system.