Dong Wen

Dong Wen
University of Science and Technology Beijing | USTB · Institute of Artificial Intelligence

Doctor of Engineering

About

45
Publications
12,756
Reads
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339
Citations
Additional affiliations
January 2017 - present
University of California, San Diego
Position
  • Professor
Description
  • Diagnosis and rehabilitation methods based on EEG/fMRI data analysis for various types of cognitive impairment; Nonlinear dynamics and networks methods; Embedded systems based on BCI and VR
January 2015 - present
Yanshan University
Position
  • Professor (Associate)
Description
  • Diagnosis and rehabilitation methods based on DTI/EEG/fMRI/fNIRS/MEG/PET data analysis for various types of cognitive impairment; Nonlinear dynamics and networks methods; Health big data mining and system; Embedded systems based on BCI and VR
September 2010 - present
Beijing Normal University
Position
  • Researcher
Description
  • Nonlinear dynamics and networks methods

Publications

Publications (45)
Article
In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method for spatial cognitive ability assessment was proposed, aiming at achieving the binary classification of task-state EEG signals before and after spatial cognitive training. Besides, the multi-dimensional conditional mutual information method was used...
Article
Full-text available
This study aimed to find a good coupling feature extraction method to effectively analyze resting state EEG signals (rsEEG) of amnestic mild cognitive impairment(aMCI) with type 2 diabetes mellitus(T2DM) and normal control (NC) with T2DM. A method of EEG signal coupling feature extraction based on weight permutation conditional mutual information (...
Article
This study aims to explore an effective method to evaluate spatial cognitive ability, which can effectively extract and classify the feature of EEG signals collected from subjects participating in the virtual reality (VR) environment; and evaluate the training effect objectively and quantitatively to ensure the objectivity and accuracy of spatial c...
Article
Background The application of deep learning models to electroencephalogram (EEG) signal classification has recently become a popular research topic. Several deep learning models have been proposed to classify EEG signals in patients with various neurological diseases. However, no effective deep learning model for event-related potential (ERP) signa...
Article
Combing brain-computer interfaces (BCI) and virtual reality (VR) is a novel technique in the field of medical rehabilitation and game entertainment. However, the limitations of BCI such as a limited number of action commands and low accuracy hinder the widespread use of BCI-VR. Recent studies have used hybrid BCIs that combine multiple BCI paradigm...
Article
This study aims to find an effective method to evaluate the efficacy of cognitive training of spatial memory under a virtual reality environment, by classifying the EEG signals of subjects in the early and late stages of spatial cognitive training. This study proposes a new EEG signal analysis method based on Multivariate Permutation Conditional Mu...
Article
The convolutional neural network (CNN) model is an active research topic in the field of EEG signals analysis. However, the classification effect of CNN on EEG signals of amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) is not ideal. Even if EEG signals are transformed into multispectral images that are more closely ma...
Article
Background: The traditional rehabilitation for neurological diseases lacks the active participation of patients, its process is monotonous and tedious, and the effects need to be improved. Therefore, a new type of rehabilitation technology with more active participation combining brain-computer interface (BCI) with virtual reality (VR) has develop...
Article
Objective: The purpose of this study is to judge whether this combination method of multispectral image and convolutional neural network (CNN) method can be used to distinguish amnestic mild cognitive impairment (aMCI) with Type 2 diabetes mellitus (T2DM) and normal controls (NC) with T2DM effectively.. Methods: In this study, the authors first...
Article
Recently, combining feature extraction and classification method of electroencephalogram (EEG) signals has been widely used in identifying mild cognitive impairment. However, it remains unclear which feature of EEG signals is best effective in assessing amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) when combining on...
Chapter
This chapter introduces the research status of spatial complex brain networks from the perspective of graph theory and complex networks. Firstly, we review the theoretical concepts of graph theory and complex networks, and combined them with spatial complex brain networks. We focused on a variety of important network topological properties, and the...
Article
Recently, coupling between groups of neurons or different brain regions has been widely studied to provide insights into underlying mechanisms of brain functions. To comprehensively understand the effect of such coupling, it is necessary to accurately extract the coupling strength information among multivariate neural signals from the whole brain....
Article
Full-text available
Multiscale entropy (MSE) analysis is a novel entropy-based analysis method for quantifying the complexity of dynamic neural signals and physiological systems across multiple temporal scales. This approach may assist in elucidating the pathophysiologic mechanisms of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). Using restin...
Article
Full-text available
We analyzed topology of brain functional networks in type 2 diabetes mellitus (T2DM) patients without mild cognitive impairment. We recruited T2DM patients without mild cognitive impairment (4 males and 8 females) and healthy control subjects (8 males and 16 females) to undergo cognitive testing and resting-state functional magnetic resonance imagi...
Article
Full-text available
At present, the sparse representation-based classification (SRC) methods of electroencephalograph (EEG) signal analysis have become an important approach for studying brain science. SRC methods mean that the target data is sparsely represented on the basis of a fixed dictionary or learned dictionary, and classified based on the reconstruction crite...
Article
Full-text available
Preclinical Alzheimer's disease (Pre-AD) has been regarded as the preclinical pathological state of mild cognitive impairment (MCI) and Alzheimer's disease (AD; Sperling et al., 2011), and becomes a popular research field currently. At present, many researchers studied the biomarkers of Pre-AD from several angles, including of neuropsychology scale...
Article
Full-text available
Objective: This study was meant to explore whether the coupling strength and direction of resting-state electroencephalogram (rsEEG) could be used as an indicator to distinguish the patients of Type 2 Diabetes Mellitus (T2DM) with or without amnestic Mild Cognitive Impairment (aMCI). Methods: Permutation conditional mutual information (PCMI) was us...
Article
Full-text available
At present, the clinical diagnosis of mild cognitive impairment (MCI) patients becomes the important approach of evaluating early Alzheimer's disease. The methods of EEG signal coupling and synchronization act as a key role in evaluating and diagnosing MCI patients. Recently, these coupling and synchronization methods were used to analyze the EEG s...
Article
During fracturing, high pressure pipe manifold can be easily damaged under the coupling effect of the internal high pressure and erosion. In order to reveal the damage mechanism, a new apparatus which can provide an axis tensile force for specimen has been developed and built to perform the tests. Based on the macroscopic and SEM analyses, it is de...
Article
Subjected to effect of complex working conditions, oil production well casing will produce different degrees of stress concentration during the long-term service process. This kind of stress concentration can cause casing damage accident very easily, inflicting huge economic losses. As the advantage of the magnetic memory testing technology in meta...
Article
Full-text available
Recently, the synchronization between neural signals has been widely used as a key indicator of brain function. To understand comprehensively the effect of synchronization on the brain function, accurate computation of the synchronization strength among multivariate neural series from the whole brain is necessary. In this study, we proposed a metho...
Article
Full-text available
Aim: Spiking activities and neuronal network oscillations in the theta frequency range have been found in many cortical areas during information processing. The aim of this study is to determine whether nicotinic acetylcholine receptors (nAChRs) mediate neuronal network activity in rat medial septum diagonal band Broca (MSDB) slices. Methods: Extr...
Article
Neuronal oscillations in the gamma frequency range and spikes have been reported in many cortical areas in the information processing, but the role of spikes play in cortical processing remains unclear. The aim of this study was to examine the role of kainate in the generation of oscillatory field activity at gamma frequency in the hippocampal slic...
Article
Neuronal oscillations in the theta frequency range and spikes have been reported in many cortical areas in the information processing, but the role of spikes play in cortical processing remains unclear. The aim of this study was to examine the role of DHPG in the generation of oscillatory field activity at theta frequency in the medical septal diag...
Article
Cloth simulation is an important research area in Computer Graphics worldwide at present, and the study, which detects collision, processes surface intersection and response, is a difficulty during the course of cloth simulation. Although there are some achievements, it is difficult for the researchers to improve the efficiency of collision detecti...
Article
For the characteristics of time consuming, complicated steps and lack of automatic of the traditional water quality monitoring, an automated real-time online water quality monitoring program based on wireless sensor network (WSN) is presented in this paper. This system combined micro-UV-Vis Fiber Optic Spectrometer with ZigBee Wireless Communicatio...
Article
Most algorithms for mining frequent patterns in data streams are based on structures like FP-tree, complex mining method makes time and storage space large compared to the bit vector expression. In this paper, an algorithm based on Horizontal Bit vectors for mining Frequent Patterns in data Streams HB-FPS is proposed. HB-FPS is divided into two pha...
Article
A novel potential function-based approach is presented to investigate the line formation for multi-agent systems. The control objective for these agents is to reach a final desired line formation. The line formation is achieved by using a leaderfollower strategy, in which one agent is regarded as the leader and the others as followers. Moreover, ea...
Conference Paper
The principles and advantages of a new type of non-destructive testing technique—Metal Magnetic Memory (MMM) technique—is briefly introduced, and the Intense Magnetic Memory (IMM) technique under conditions of high intense remanence is put forward. As application in the field of pipeline inspection, a new intelligent pipeline inspection system base...
Conference Paper
In this paper a new method has been proposed based on the combination of principal component analysis(PCA) and independent component analysis (ICA) for face recognition. In the process of face feature subspace extraction, the dimension of the date set has been reduced by the principal component analysis (PCA), and then the principal components are...
Conference Paper
Drill pipe is used in well drilling in oil field. Under the force of tubular columns and pressure of drilling fluids, the drill pipe usually fails in long-term service, which consequently leads to accidents, such as drill pipe twisting off, and causes stagnation of production. So it’s especially necessary to detect defects in drill pipe in time. Tr...
Conference Paper
Corrosion-induced pits will disturb the original stress distribution of casing and appear local high stress area. Through 3-D finite element analysis on casing with spherical and cylindrical corrosion cavity, the stress concentration degree and the influences of cavity shape, size and orifice diameter on stress concentration factor are determined a...
Conference Paper
Large diameter and high pressure is the typical feature of long distance gas transmission pipelines. Pipelines are subjected to corrosion, cracks and other kinds of deformations. Ensuring pipeline integrity and the associated utilization of in-line inspection (ILI) tools have become primary concern of pipeline operators. A novel nondestructive tech...
Conference Paper
The neural networks identification model is developed on the basis of the force analysis of magnetic bearing spindle, which reflects the nonlinear delay character between inputs and outputs system. This network is able to converge quickly in 5 training steps. The mean square error value reduces to 3.495e-006 in 50 steps. Inspection shows that the n...
Conference Paper
Cloth simulation is an important research area in computer graphics worldwide at present, and the study, which processes surface intersection during the course of cloth collision, is a difficulty in cloth simulation. We present a global optimization algorithm close to the reality and minimizing intersection contour lines, in order to improve authen...
Conference Paper
In this paper, a new kind of instrument used specially for testing the worn surface of casing was introduced, its structure and testing principle was further elaborated. Meanwhile, the testing data were also used for the 3D surface reconstruction of the worn area of casing. The comparative study with SEM pictures could tell that the instrument perf...
Article
Casing wear was investigated on the DCWT-1000 casing wear tester, the influence of impact-slide of deep and ultra-deep well on the wear behavior of casing was studied. Microstructure and surface topography of the wear surface of casing under different loads were investigated by means of 3D surface topography measurement apparatus, microscope, and S...
Article
A ground monitoring system was developed to diagnose casing and tooljoint wear in a well during drilling by combining the analysis of wear debris with detection of the tooljoints. The result shows that the wear debris concentration in the circulating drilling mud in a well reflects the total wear rate of the triboelements in the well, while the wea...

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