Yanshan University

About: Yanshan University is a education organization based out in Qinhuangdao, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 19544 authors who have published 16904 publications receiving 184378 citations. The organization is also known as: Yānshān dàxué.

Fast and Scalable Multi-Way Analysis of Massive Neural Data

Abstract: Analysis of neural data with multiple modes and high density has recently become a trend with the advances in neuroscience research and practices There exists a pressing need for an approach to accurately and uniquely capture the features without loss or destruction of the interactions amongst the modes (typically) of space, time, and frequency Moreover, the approach must be able to quickly analyze the neural data of exponentially growing scales and sizes, in tens or even hundreds of channels, so that timely conclusions and decisions may be made A salient approach to multi-way data analysis is the parallel factor analysis (PARAFAC) that manifests its effectiveness in the decomposition of the electroencephalography (EEG) However, the conventional PARAFAC is only suited for offline data analysis due to the high complexity, which computes to be $O(n^{2})$ with the increasing data size In this study, a large-scale PARAFAC method has been developed, which is supported by general-purpose computing on the graphics processing unit (GPGPU) Comparing to the PARAFAC running on conventional CPU-based platform, the new approach dramatically excels by ${>}360$ times in run-time performance, and effectively scales by ${>}400$ times in all dimensions Moreover, the proposed approach forms the basis of a model for the analysis of electrocochleography (ECoG) recordings obtained from epilepsy patients, which proves to be effective in the epilepsy state detection The time evolutions of the proposed model are well correlated with the clinical observations Moreover, the frequency signature is stable and high in the ictal phase Furthermore, the spatial signature explicitly identifies the propagation of neural activities among various brain regions The model supports real-time analysis of ECoG in ${>}1{,}000$ channels on an inexpensive and available cyber-infrastructure

Relative power and coherence of EEG series are related to amnestic mild cognitive impairment in diabetes

Abstract: Objective: Diabetes is a risk factor for dementia and mild cognitive impairment. The aim of this study was to investigate whether some features of resting-state EEG (rsEEG) could be applied as a biomarker to distinguish the subjects with amnestic mild cognitive impairment (aMCI) from normal cognitive function in type 2 diabetes. Materials and Methods: In this study, 28 patients with type 2 diabetes (16 aMCI patients and 12 controls) were investigated. Recording of the rsEEG series and neuropsychological assessments were performed. The rsEEG signal was first decomposed into delta, theta, alpha, beta, gamma frequency bands. The relative power of each given band/sum of power and the coherence of waves from different brain areas were calculated. The extracted features from rsEEG and neuropsychological assessments were analyzed as well. Results: The main findings of this study were that: 1) compared with the control group, the ratios of power in theta band (P(theta)) versus power in alpha band (P(alpha)) (P(theta)/P(alpha)) in the frontal region and left temporal region were significantly higher for aMCI, and 2) for aMCI, the alpha coherences in posterior, fronto-right temporal, fronto-posterior, right temporo-posterior were decreased; the theta coherences in left central-right central (LC-RC) and left posterior-right posterior (LP-RP) regions were also decreased; but the delta coherences in left temporal-right temporal (LT-RT) region were increased. Conclusion: The proposed indexes from rsEEG recordings could be employed to track cognitive function of diabetic patients and also to help in the diagnosis of those who develop aMCI.