Likun Xia

Review on EEG and ERP predictive biomarkers for major depressive disorder

Abstract The selection of suitable antidepressants for Major Depressive Disorder (MDD) has been challenging and is mainly based on subjective assessments that include minimal scientific evidence. Objective measures that are extracted from neuroimaging modalities such as electroencephalograms (EEGs) could be a potential solution to this problem. This approach is achieved by the successful prediction of antidepressant treatment efficacy early in the patients care. EEG-based relevant research studies have shown promising results. These studies are based on derived measures from EEG and event-related potentials (ERPs), which are called neurophysiological predictive biomarkers for MDD. This paper seeks to provide a detailed review on such research studies along with their possible limitations. In addition, this paper provides a comparison of these methods based on EEG/ERP common datasets from MDD and healthy controls. This paper also proposes recommendations to improve these methods, e.g., EEG integration with other modalities such as functional magnetic resonance imaging (fMRI) and magnetoencephalograms (MEG), to achieve better evidence of the efficacy than EEG alone, to eventually improve the treatment selection process.

A novel method for vegetation encroachment monitoring of transmission lines using a single 2D camera

AbstractThe dangerous, overgrown vegetation/trees under high voltage transmission lines right-of-ways (ROWs) have caused severe blackouts/flashovers due to interference with power lines which leads to short circuiting among the conductors. Therefore, these dangerous encroachments are monitored periodically along the electrical distribution networks ROWs through visual inspection, or by airborne system. Each of these methods has its own attributes and limitations and have proved to be costly, time consuming and not much accurate. In these circumstances, it is necessary for the electrical utilities to review their vegetation management practices so as to avoid incidents of unintended encroachments. In tropical countries, overgrown vegetation is a common cause for power line failure. This paper proposes an innovative concept of utilizing a single camera for monitoring dangerous vegetation (trees, shrubs and plants, etc.) under transmission lines ROWs. The main focus is on using an imaging device (camera) integrated on each transmission pole to automate inspection for the vegetation encroachments endangering the transmission lines. These cameras are envisioned to be connected wirelessly to each other, forming a series of wireless camera networks that can be monitored remotely. A single camera mounted on power poles acquires images and sends them wirelessly to the base station. At base station, algorithm (software) trained by image processing and pattern recognition techniques is used to identify (height, depth, and width of encroached vegetation, etc.) excess vegetation encroachments within and outside ROWs. The performance evaluation of a real time developed test-bed scenario proves the feasibilities of integrating the method for transmission line maintenance.

Automatic diagnosis of alcohol use disorder using EEG features

Alcohol use disorder (AUD) has been considered as a social and health issue worldwide. More importantly, the screening of AUD patients has been challenging due to the subjectivity imparted by self-test reports. Automated methods involving neuroimaging modality such as quantitative electroencephalography (QEEG) have shown promising research results. However, the QEEG methods were developed only for alcohol dependents (AD) and healthy controls. Therefore, this study sought to propose a machine learning (ML) method to classify 1) between alcohol abusers and healthy controls, and 2) among healthy controls, alcohol abusers, and alcoholics. The proposed ML method involved QEEG feature extraction, selection of most relevant features, and classification of the study participants into their relevant groups. The study participants such as 12 alcohol abusers (mean age 56.70ź15.33 years), 18 alcoholics (mean age 46.80ź9.29 years), and 15 healthy controls (mean 42.67ź15.90 years) were recruited to acquire EEG data. The data were recorded during 10 minutes of eyes closed (EC) and eyes open (EO) conditions. Furthermore, the EEG data were utilized to extract QEEG features such as absolute power (AP) and relative power (RP). Methods such as t-test and principal component analysis (PCA) were employed to select most relevant QEEG features. Finally, the discriminant QEEG features were used as inputs to the classification models: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Multilayer back-Propagation Network (MLP), and Logistic Model Trees (LMT), supported by 10-fold cross validation. As results, the LMT has achieved best performance rendering a classification accuracy (96%), sensitivity (97%) and specificity (93%). In addition, a further classification for each subgroup of AUD patients has achieved accuracy (>90%). In conclusion, the results implicated significant neurophysiological differences among alcohol abusers, alcoholics, and controls. Moreover, the AUD patients exhibited significantly decreased theta as compared with the healthy controls.

Quantification of physiological disparities and task performance in stress and control conditions

In mental stress studies, cerebral activation and autonomic nervous system are important distinctly. This study aims to analyze disparities associated with scalp potential, which may have impact on autonomic activation of heart during mental stress. Ten healthy subjects participated in this study that performed arithmetic tasks in stress and control environment. Task difficulty was calculated from their correct responses. During the experiment, electroencephalogram (EEG) and electrocardiogram (ECG) signals were recorded concurrently. Sympathetic innervation of heart was estimated from heart rate (HR), which is extracted from the ECG. The value of theta Fz/alpha Pz was measured from EEG scalp potential. The results show a significant surge in the value of theta Fz/alpha Pz in stress as compared to baseline (p<;0.013) and control (p<;0.042). The results also present tachycardia while in stress as compared to baseline (p<;0.05). Task difficulty in stress is also considerably higher than control environment (p<;0.003).

Association of mental stress with video games

In this paper, the sympathetic and parasympathetic responses are found out to monitor mental stress during playing car racing game. Mental stress is very important issue to address because of its severe consequences. The heart rate variability (HRV) analysis is performed to find these responses. HRV features are derived from the electrocardiogram (ECG) signals of six healthy participants and analyzed in time and frequency domain. HRV features such as mean heart rate; mean normal-to-normal (NN) interval, standard deviation of NN (SDNN) interval, root mean square of successive NN periods (RMSSD), low frequency (LF) components, high frequency (HF) components, ratio of LF/HF (LF/HF), normalized LF(LF(n.u.)) and HF (HF(n.u.)) were derived to monitor mental stress in terms of sympathetic and parasympathetic activity. HRV is helpful to use because it can detect the changes in autonomic nervous system that are well defined to predict mental stress with certainty.

Analysis of EEG signals regularity in adults during video game play in 2D and 3D

Video games have long been part of the entertainment industry. Nonetheless, it is not well known how video games can affect us with the advancement of 3D technology. The purpose of this study is to investigate the EEG signals regularity when playing video games in 2D and 3D modes. A total of 29 healthy subjects (24 male, 5 female) with mean age of 21.79 (1.63) years participated. Subjects were asked to play a car racing video game in three different modes (2D, 3D passive and 3D active). In 3D passive mode, subjects needed to wear a passive polarized glasses (cinema type) while for 3D active, an active shutter glasses was used. Scalp EEG data was recorded during game play using 19-channel EEG machine and linked ear was used as reference. After data were pre-processed, the signal irregularity for all conditions was computed. Two parameters were used to measure signal complexity for time series data: i) Hjorth-Complexity and ii) Composite Permutation Entropy Index (CPEI). Based on these two parameters, our results showed that the complexity level increased from eyes closed to eyes open condition; and further increased in the case of 3D as compared to 2D game play.

Effective techniques for vegetation monitoring of transmission lines right-of-ways

The monitoring of high-voltage transmission lines right-of-ways, regarding the cleaning and clearing of vegetation is done periodically by visual survey, video surveillance, or by air-borne LiDAR (Light Detection And Ranging) scanners. Geographical Information Systems (GIS) containing the geo-referenced data of lands, assets from where the transmission lines passes, vegetation near the line and the applications utilized are essential tools for the improvement of transmission lines maintenance. This paper proposes a concept of multispectral satellite stereo images to find out the 3D depth of vegetation endangering transmission lines right-of-ways, and also a new Multimedia Wireless Sensor Networks (MWSNs) based cost effective, less time consuming and a more accurate technique for the automated power line inspection against vegetation encroachment.

Local approximation improvement of trajectory piecewise linear macromodels through Chebyshev interpolating polynomials

We introduce the concept of two dimensional (2D) scalability of trajectory piecewise linear (TPWL) through the exploitation of Chebyshev interpolating polynomials in each piecewise region. The goal of 2D scalability is to improve the local approximation properties of TPWL macromodels. Horizontal scalability is achieved through the reduction of number of linearization points along the trajectory; vertical scalability is obtained by extending the scope of macromodel to predict the response of a nonlinear system for inputs far from training trajectory. In this way more efficient macromodels are obtained in terms of simulation speed up of complex nonlinear systems. The methodology developed is to predict the nonlinear responses generated by faults introduced in Micro Electro-Mechanical Systems (MEMS) accelerometer during fabrication, that are used to obtain the seismic images for oil and gas discovery. We provide the implementation details and illustrate the 2D scalability concept with an example using nonlinear transmission line.

High level fault modeling and fault propagation in analog circuits using NLARX automated model generation technique

It is known that fault modeling and fault propagation in analog circuits are extremely important and more challenging than in digital circuits. Several automated model generation (AMG) techniques are developed to model the nonlinear behavior of faulty analog circuits. However, most of the modeling techniques are performed under the MATLAB environment which is impractical and the models cannot be utilized in electronic circuits. To perform high level fault modeling (HLFM) and fault propagation (FP) on system level, the models need to be translated into hardware description language (HDL) models such as VHDL-AMS or Verilog-AMS models. In this paper, several faults are modeled for transistor level analog circuits using nonlinear autoregressive exogenous (NLARX) AMG technique in MATLAB. The resulting MATLAB models are translated into VHDL-AMS behavioral models. HLFM and FP are successfully implemented for benchmark analog circuits: inverting amplifier and biquadratic low-pass filter circuits.

Automated Model Generation of Analog Circuits through Modified Trajectory Piecewise Linear Approach with Cheby Shev Newton Interpolating Polynomials

In this paper we propose an efficient scalability approach for the trajectory piecewise linear (TPWL) macro models through the utilization of Chebyshev interpolating polynomials in each piecewise region. The scalability achieved is in two dimensions (2D) that mainly improve the local approximation properties of TPWL macro models. Horizontal scalability is achieved by decreasing the number of linearization points along the trajectory, vertical scalability is obtained by extending the range of macro model to predict the response of a nonlinear system for inputs far from training trajectory. In this way more efficient macro models are obtained in terms of simulation speed up of complex nonlinear systems. We provide the implementation details and illustrate the 2D scalability concept with an example using nonlinear transmission line.