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.

A meta-analysis of tight versus conventional glycemia control in critically ill brain-injured adults

Objective: To evaluate the benefits and risks of tight glycemia control (TGC) versus conventional glucose control (CGC) in critically ill brain-injured adults. Methods: We performed meta-analysis by systematically searching PubMed, EMBASE, OVID, ScienceDirect, Web of Science, CNKI, Wanfang Data, and CQVIP databases to retrieve RCTs in any languages. We used Review Manager to perform meta-analysis. Odds ratios (ORs) or weighted mean differences (WMDs) with 95% confidence intervals (CIs) were calculated in analyses. Results: Twenty-six RCTs with a total of 3,759 participants were included in this meta-analysis. In-hospital mortality showed significant dissimilarity between TGC and CGC groups with OR of 0.76 (95%CI 0.58, 0.99). However, in terms of overall mortality and long-term neurological severity outcome, it didn't show differences with ORs of 0.93 (95%CI 0.79, 1.10) and 1.15 (95%CI 0.96, 1.37). There were also discrepancies in infection rate and ICU length of stay with OR of 0.51 (95%CI 0.42, 0.62) and WMD of -2.37 (95%CI -2.99, -1.74). Significances were observed in hypoglycemia events with ORs of 6.24 (95%CI 4.83, 8.07) and 2.73 (95%CI 2.56, 2.91) using two methods. Conclusion: In critically ill brain injury, TGC did not show beneficial effects on reducing overall mortality and long term neurological outcome, but it increased the risk of hypoglycemia.

Controlling Chaos in Neuron Based on Lasalle Invariance Principle

A new control law is proposed to asymptotically stabilize the chaotic neuron system based on LaSalleinvariant principle.The control technique does not require analytical knowledge of the system dynamics and operateswithout an explicit knowledge of the desired steady-state position.The well-known modified Hodgkin-Huxley (MHH)and Hindmarsh-Rose (HR) model neurons are taken as examples to verify the implementation of our method.Simulationresults show the proposed control law is effective.The outcome of this study is significant since it is helpful to understandthe learning process of a human brain towards the information processing,memory and abnormal discharge of the brainneurons.

A Connectome Computation System for discovery science of brain

Much like genomics, brain connectomics has rapidly become a core component of most national brain projects around the world. Beyond the ambitious aims of these projects, a fundamental challenge is the need for an efficient, robust, reliable and easy-to-use pipeline to mine such large neuroscience datasets. Here, we introduce a computational pipeline--namely the Connectome Compu- tation System （CCS）-for discovery science of human brain connectomes at the macroscale with multimodal magnetic resonance imaging technologies. The CCS is designed with a three-level hierarchical structure that includes data cleaning and preprocessing, individual connectome mapping andconnectome mining, and knowledge discovery. Several functional modules are embedded into this hierarchy to implement quality control procedures, reliability analysis and connectome visualization. We demonstrate the utility of the CCS based upon a publicly available dataset, the NKI- Rockland Sample, to delineate the normative trajectories of well-known large-scale neural networks across the natural life span （6-85 years of age）. The CCS has been made freely available to the public via GitHub （https：//github.com/ zuoxinian/CCS） and our laboratory＇s Web site （http：//lfcd. psych.ac.cn/ccs.html） to facilitate progress in discovery science in the field of human brain connectomics.