State-space modeling and estimation for multivariate brain signals

Brain signals are derived from underlying dynamic processes and interactions between populations of neurons in the brain. These signals are typically measured from distinct regions, in the forms of multivariate time series signals and exhibit non-stationarity. To analyze these multi-dimensional data with the latent dynamics, efficient statistical methods are needed. Conventional analyses of brain signals use stationary techniques and focus on analyzing a single dimensional signal, without taking into consideration the coherence between signals. Other conventional model is the discrete-state hidden Markov model (HMM) where the evolution of hidden states is characterized by a discrete Markov chain. These limitations can be overcome by modeling the signals using state-space model (SSM), that model the signals continuously and further estimate the interdependence between the brain signals. This thesis developed SSM based formulations for autoregressive models to estimate the underlying dynamics of brain activity based on measured signals from different regions. The hidden state and model estimations were performed by Kalman filter and maximum likelihood estimation based on the expectation maximization (EM) algorithm. Adaptive dynamic model time-varying autoregressive (TV-AR) was formulated into SSM, for the application of multi-channel electroencephalography (EEG) classification, where accuracy obtained was better than the conventional HMM. This research generalized the TV-AR to multivariate model to capture the dynamic integration of brain signals. Dynamic multivariate time-varying vector autoregressive (TV-VAR) model was used to investigate the dynamics of causal effects of one region has on another, which is known as effective connectivity. This model was applied to motor-imagery EEG and motortask functional magnetic resonance imaging (fMRI) data, where the results showed that the effective connectivity changes over time. These changing connectivity structures were found to reflect the behavior of underlying brain states. To detect the state-related change of brain activities based on effective connectivity, this thesis further developed a novel unified framework based on the switching vector autoregressive (SVAR) model. The framework was applied to simulation signals, epileptic EEG and motor-task fMRI. The results showed that the novel framework is able to simultaneously capture both slow and abrupt changes of effective connectivity according to the brain states. In conclusion, the developed SSM based approaches were effective for modeling the nonstationarity and connectivity in brain signals

A review of chewing detection for automated dietary monitoring

A healthy dietary lifestyle prevents diseases and leads to good physical conditions. Poor dietary habits, such as eating disorders, emotional eating and excessive unhealthy food consumption, may cause health complications. People’s eating habits are monitored through automated dietary monitoring (ADM), which is considered a part of our daily life. In this study, the Google Scholar database from the last 5 years was considered. Articles that reported chewing activity characteristics and various wearable sensors used to detect chewing activities automatically were reviewed. Key challenges, including chew count, various food types, food classification and a large number of samples, were identified for further chewing data analysis. The chewing signal’s highest reported classification accuracy value was 99.85%, which was obtained using a piezoelectric contactless sensor and multistage linear SVM with a decision tree classifier. The decision tree approach was more robust and its classification accuracy (75%–93.3%) was higher than those of the Viterbi algorithm-based finite-state grammar approach, which yielded 26%–97% classification accuracy. This review served as a comparative study and basis for developing efficient ADM systems

A unified estimation framework for state-related changes in effective brain connectivity

Objective: This paper addresses the critical problem of estimating time-evolving effective brain connectivity. Current approaches based on sliding window analysis or time-varying coefficient models do not simultaneously capture both slow and abrupt changes in causal interactions between different brain regions. Methods: To overcome these limitations, we develop a unified framework based on a switching vector autoregressive (SVAR) model. Here, the dynamic connectivity regimes are uniquely characterized by distinct vector autoregressive (VAR) processes and allowed to switch between quasi-stationary brain states. The state evolution and the associated directed dependencies are defined by a Markov process and the SVAR parameters. We develop a three-stage estimation algorithm for the SVAR model: 1) feature extraction using time-varying VAR (TV-VAR) coefficients, 2) preliminary regime identification via clustering of the TV-VAR coefficients, 3) refined regime segmentation by Kalman smoothing and parameter estimation via expectation-maximization algorithm under a state-space formulation, using initial estimates from the previous two stages. Results: The proposed framework is adaptive to state-related changes and gives reliable estimates of effective connectivity. Simulation results show that our method provides accurate regime change-point detection and connectivity estimates. In real applications to brain signals, the approach was able to capture directed connectivity state changes in functional magnetic resonance imaging data linked with changes in stimulus conditions, and in epileptic electroencephalograms, differentiating ictal from nonictal periods. Conclusion: The proposed framework accurately identifies state-dependent changes in brain network and provides estimates of connectivity strength and directionality. Significance: The proposed approach is useful in neuroscience studies that investigate the dynamics of underlying brain states

ION TRANSPORT IN PASSIVATING LAYERS FORMING ON SURFACE OF LITHIUM ELECTRODE IN PROPYLENE-CARBONATE SOLUTIONS

The aim is to reveal the ion transport mechanism in the passivating films forming at contact of the lithium and lithium alloys with electrolytes on base of the non-aqueous solvents. The flowing of the ion injected currents in the lithium-film-solution system has been discovered firstly; the model of the currents limited with the spatial charge has been proposed; the transport characteristics of the passivating films have been determined; the equivalent circuit of the lithium electrode has been proposed. The electrolytic electrode composition improving sufficiently the charge-discharge characteristics of the re-charged anode for lithium battery has been proposed. The procedure determining the transport parameters of the passivating films on the lithium electrode has been developedAvailable from VNTIC / VNTIC - Scientific & Technical Information Centre of RussiaSIGLERURussian Federatio

Review of 5G wireless cellular network on Covid-19 pandemic: digital healthcare & challenges

Since the coronavirus disease (COVID-19) began in 2020, it has changed the way people live such as social life and healthcare. One of the simplest ways to avoid wide spread of the virus is to minimize physical contact and avoid going to a crowded place. Besides that, it also has prompted countries across the world to employ digital technologies such as wireless communication systems to combat this global crisis. Digital healthcare is one of the solutions that play a crucial role to support the healthcare sector in order to prevent and minimize physical contact through telehealth and telemedicine such as monitoring, diagnosis and patient care. 5G network has the potential to advance digital healthcare along with its key technology such as enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency Communication (URLLC), and massive Machine Type Communication (mMTC). Despite the benefits of digital healthcare by leveraging the 5G technology, there are still challenges to be overcome such as privacy protection issues, 5G deployment and limited connectivity. In this review, it highlights the relevance and challenges of 5G wireless cellular networks for digital healthcare during the COVID-19 pandemic. It also provides potential solutions and future research areas for researchers on 5G to reduce COVID-19 related health risks

Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm

This paper considers identifying effective cortical connectivity from scalp EEG. Recent studies use time-varying multivariate autoregressive (TV-MAR) models to better describe the changing connectivity between cortical regions where the TV coefficients are estimated by Kalman filter (KF) within a state-space framework. We extend this approach by incorporating Kalman smoothing (KS) to improve the KF estimates, and the expectation-maximization (EM) algorithm to infer the unknown model parameters from EEG. We also consider solving the volume conduction problem by modeling the induced instantaneous correlations using a full noise covariate. Simulation results show the superiority of KS in tracking the coefficient changes. We apply two derived frequency domain measures i.e. TV partial directed coherence (TV-PDC) and TV directed transfer function (TV-DTF), to investigate dynamic causal interactions between motor areas in discriminating motor imagery (MI) of left and right hand. Event-related changes of information flows around beta-band, in a unidirectional way between left and right hemispheres are observed during MI. A difference in inter-hemispheric connectivity patterns is found between left and right-hand movements, implying potential usage for BCI

A development of education technology for smart learning program

The current education system in Malaysia has been of great concern to the society. The conventional methods of education currently being used do not allow students to be actively involved in the whole learning process. Many researches were conducted to find the best ways to enhance the quality of education and mostly with the help of technology. This study implemented science, technology, engineering and mathematics (STEM) as an integrated smart learning program for primary school students in Malaysia. A set of developed technologies were introduced to cope with the primary school syllabus. It consisted the heart diagnostic, modern agriculture, smart bicycle, monopoly game, speech technology and crocodile clip. In heart diagnostic, student learned how to capture the electrocardiography (ECG) signals, read the ECG graph, calculate the width of the graph and use mathematics to understand the characteristic of the ECG signals. In another technology that we have developed is named modern agriculture. Heat and water level sensors were used to measure the temperature and the water that is needed for healthy growth of plants. In the science subjects, they know that plants require light in order to secure and they are capable to quantify the amount of light in relationship to the temperature. Using the above concepts, several other technologies which we called smart bicycle, speech technology and crocodile clip software were used as a tools to assist learning for these primary school children. This project was implemented at Sekolah Kebangsaan Taman Universiti 1, Johor Bahru, Malaysia. Eight teen students were selected to participate in the trial. Since the current evaluation system is based on exam oriented learning in order to evaluate the performance of the student. This created stress for teachers and students as the passing percentage of the school reflects the current status of the school as being a top notch or below average standing. In order to address this issue, we developed a monopoly like game based system, where students are actually being evaluated by playing this game. In the game the student has to answer sets of questions as they program in order to win the game. The result showed that the technology integration into the learning process had produced positive influence on the students’ interest towards STEM and thus enhanced their academic performance. Teachers have also becoming more innovative in teaching activities with the aid of technology