Qinglin Zhao

Qinglin Zhao
Lanzhou University | LZU · School of Information Science and Engineering

About

36
Publications
3,217
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401
Citations

Publications

Publications (36)
Article
Full-text available
According to the WHO, the number of mental disorder patients, especially depression patients, has overgrown and become a leading contributor to the global burden of disease. With the rising of tools such as artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applicat...
Article
Objective: The excellent Signal-to-Noise Ratio (SNR) is the premise of Electroencephalogram (EEG) research and applications. This study aims to use innovative method to swiftly remove the Ocular Artifacts (OAs) from multichannel EEG to enhance the SNR. Methods: The moment matching method which is prevalently used to removing stripe noise from hy...
Article
Drug addicts are characterized by difficulty neglecting monetary reward, but its underlying neural mechanisms remain unclear. The current study aimed to investigate the behavioral and electrophysiological signatures of abnormal attentional bias based on different amounts of reward in abstinent heroin addicts (AHAs). We used a simplified attentional...
Data
We present a multi-model open dataset for mental-disorder analysis. For now, the dataset includes data mainly from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. At this stage, only electroencephalogram (EEG) and speech recording data are...
Data
We present a multi-model open dataset for mental-disorder analysis. For now, the dataset includes data mainly from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. At this stage, only electroencephalogram (EEG) and speech recording data are...
Preprint
Full-text available
In universal environment, a patient-friendly inexpensive method is needed to realize the early diagnosis of depression, which is believed to be an effective way to reduce the mortality of depression. The purpose of this study is only to collect EEG signal from three electrodes Fp1, Fpz and Fp2, then the linear and nonlinear features of EEG used to...
Chapter
Clinically, polysomnography (PSG) is used to assess sleep quality by monitoring various parameters, such as Electroencephalogram (EEG), electrocardiogram (ECG), Electrooculography (EOG), Electromyography (EMG), pulse, oxygen saturation, and respiratory rate. However, in order to assess these parameters, PSG requires a variety of sensors that must m...
Article
Full-text available
The attention of drug-dependent persons tends to be captured by stimuli associated with drug consumption. This involuntary cognitive process is considered as attentional bias (AB). AB has been hypothesized to have causal effects on drug abuse and drug relapse, but its underlying neural mechanisms are still unclear. This study investigated the neura...
Article
Full-text available
Abnormal decision making is a behavioral characteristic of drug addiction. Indeed, drug addicts prefer immediate rewards at the expense of future interests. Assessing the neurocognitive basis of decision-making related to drug dependence, combining event-related potential (ERP) analysis and source localization techniques, may provide new insights i...
Article
Full-text available
It has been reported that chronic heroin intake induces both structural and functional changes in human brain; however, few studies have investigated the carry-over adverse effects on brain after heroin withdrawal. In this paper, we examined the neurophysiological differences between the abstinent heroin addicts (AHAs) and healthy controls (HCs) us...
Conference Paper
Heroin addiction is usually associated with decision-making deficits that they are more likely to accept risk. Understanding the neurocognitive mechanisms underlying risky decision-making in heroin addicts therefore is important not only for interpreting the behavioral or functional impairments for heroin addicts, but also for assessing the treatme...
Article
Electroencephalogram (EEG) plays an important role in E-healthcare systems, especially in the mental healthcare area, where constant and unobtrusive monitoring is desirable. In the context of OPTIMI project, a novel, low cost, and light weight wearable EEG sensor has been designed and produced. In order to improve the performance and reliability of...
Conference Paper
This paper presents a method to remove ocular artifacts from electroencephalograms (EEGs) which can be used in biomedical analysis in portable environment. An important problem in EEG analysis is how to remove the ocular artifacts which wreak havoc among analyzing EEG signals. In this paper, we propose a combination of Wavelet Transform with effect...
Conference Paper
Modeling and prediction of Electroencephalogram (EEG) signals is very important for Portable applications; EEG signals are however widely regarded as being chaotic in nature. An adaptive modeling technique that combines Discrete Wavelet Transformation (DWT) to predict contaminated EEG signals for removal of ocular artifacts (OAs) from EEG records i...
Conference Paper
Despite clear evidence of connections between chronic stress, brain patterns, age and gender, few studies have explored stressor differences in stress detection. This paper presents a stressor-specific evaluation model conducted between stress levels and electroencephalogram(EEG) features. The overall complexity, chaos of EEG signals, and spectrum...
Conference Paper
Full-text available
Schizophrenia is a mental disorder that may include delusions, loss of personality, confusion, social withdrawal, psychosis, and bizarre behavior. In this study, we use Electroencephalogram (EEG) signals of the Alpha band to detect the differences between nonlinear EEG features of schizophrenic patients and non-psychiatric controls. EEG signals fro...
Conference Paper
Full-text available
Mild cognitive impairment (MCI) is a brain-function syndrome involving the onset and evolution of cognitive impairments which are not significant enough to interfere with daily activities. In this study, we used resting state functional magnetic resonance imaging (fMRI) to detect the whole brain fractional amplitude of low-frequency fluctuations (f...
Conference Paper
Autism spectrum disorder is a developmental disorder affecting 60 out of 10,000 individuals. Children with autism are often characterized by repetitive behaviors and by deficits in social skills and communicative abilities. They are usually not willing to communicate with other people. However, researches indicate that most of them are willing to a...
Article
Full-text available
Abnormalities in schizophrenia are thought to be associated with functional disconnections between different brain regions. Most previous studies on schizophrenia have considered high-band connectivity in preference to the Alpha band, as there has been some uncertainty correlating the latter to the condition. In this paper we attempt to clarify thi...
Article
A new model to remove ocular artifacts (OA) from electroencephalograms (EEGs) is presented. The model is based on discrete wavelet transformation (DWT) and adaptive noise cancellation (ANC). Using simulated and measured data, the accuracy of the model is compared with the accuracy of other existing methods based on stationary wavelet transforms and...
Article
Full-text available
In this paper, the complexity and chaos of EEG (electroencephalogram) signals exhibited in schizophrenic patients are analyzed using four nonlinear features: C0-complexity, Kolmogorov entropy together with an estimation of the correlation dimension and Lempel-Ziv complexity. The first two of these being novel applications of these measures. EEGs fr...
Conference Paper
With the booms of mobile communication, especially mobile smart phone, technologies to identify individuals for mobile security calls for some more strict requirements in user-friendly, real-time and ubiquitous aspects. In addition to traditional approaches (for example, password check), some advanced biometric methodologies have been applied in pr...
Article
Technical advances in the neuroelectric recordings and in the computational tools for the analysis of the brain activity and connectivity make it now possible to follow and to quantify, in real time, the interactive brain activity in a group of subjects engaged in social interactions. The degree of interaction between persons can then be assessed b...
Conference Paper
Full-text available
There have been a number of research projects which have addressed depression, the focus often being on aspects of pharmacology and psychology. Relatively few of the investigations have tried to integrate depression and the related issues into a pervasive depression prevention system incorporating user-centered design. In this paper we propose an a...
Conference Paper
Mental health care is becoming an increasing concern in home care projects. As an integral part of Telecare and Telehealth systems, portable EEG recording and real-time analysis are increasingly being used as non-intrusive monitoring techniques. In home environments without the supervision of a physician and absence of electromagnetic shielding, th...
Conference Paper
Individual identification plays an important role in privacy protection and information security. Especially, with the development of brain science, individual identification based on Electroencephalograph (EEG) may be applicable. The key to realize EEG-based identification is to find the signal features with unique individual characteristics in sp...
Conference Paper
Security issue is always challenging to the real world applications. Many biometric approaches, such as fingerprint, iris and retina, have been proposed to improve recognizing accuracy or practical facility in individual identification in security. However, there is little research on individual identification using EEG methodology mainly because o...
Conference Paper
Energy conservation, coverage and connectivity are three critical application requirements in wireless sensor networks. Related researches have either concerned coverage, connectivity, and energy conservation separately or required sensing/communication range restrictions. In this paper, we aim to maximize the network lifetime, while maintaining co...

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Projects

Project (1)
Project
Reseach on alarming theory of potential depression risk and key technology of bio-sensing based on biological and psychological multimodal information