Adaptive nonlinear multivariate brain connectivity analysis of motor imagery movements using graph theory

Recent studies on motor imagery (MI)-based brain computer interaction (BCI) reported that the interaction of spatially separated brain areas in forms of functional or effective connectivity leads to a better insight of brain neural patterns during MI movements and can provide useful features for BCIs. However, existing studies suffer from unrealistic assumptions or technical weaknesses for processing brain signals, such as stationarity, linearity and bivariate analysis framework. Besides, volume conduction effect as a critical challenge in this area and the role of subcortical regions in connectivity analysis have not been considered and studied well. In this thesis, the neurophysiological connectivity patterns of healthy human brain during different MI movements are deeply investigated. At first, an adaptive nonlinear multivariate statespace model known as dual extended Kalman filter is proposed for connectivity pattern estimation. Several frequency domain functional and effective connectivity estimators are developed for nonlinear non-stationary signals. Evaluation results show superior parameter tracking performance and hence more accurate connectivity analysis by the proposed model. Secondly, source-space time-varying nonlinear multivariate brain connectivity during feet, left hand, right hand and tongue MI movements is investigated in a broad frequency range by using the developed connectivity estimators. Results reveal the similarities and the differences between MI tasks in terms of involved regions, density of interactions, distribution of interactions, functional connections and information flows. Finally, organizational principles of brain networks of MI movements measured by all considered connectivity estimators are extensively explored by graph theoretical approach where the local and global graph structures are quantified by computing different graph indexes. Results report statistical significant differences between and within the MI tasks by using the graph indexes extracted from the networks formed particularly by normalized partial directed coherence. This delivers promising distinctive features of the MI tasks for non-invasive BCI applications

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

Parametric Modelling of EEG Data for the Identification of Mental Tasks

Electroencephalographic (EEG) data is widely used as a biosignal for the identification of different mental states in the human brain. EEG signals can be captured by relatively inexpensive equipment and acquisition procedures are non-invasive and not overly complicated. On the negative side, EEG signals are characterized by low signal-to-noise ratio and non-stationary characteristics, which makes the processing of such signals for the extraction of useful information a challenging task.peer-reviewe

BRAIN ACTIVITIES FOR MOTOR MOVEMENT

Brain Computer Interface (BCI) is hardware and software system which allows interaction between the human’s brain and some surrounding activities without depending on their muscles or peripheral nerves. The main objectives of this project are to design a brain computer interface algorithm that takes Electroencephalography(EEG) signals as its input, translates them into commands for movement control and to test the performance of the designed algorithm on human subjects. The research covers the procedure of designing the BCI algorithm and this consists of three stages firstly recording EEG brain signals, secondly EEG signals pre-processing, Last stage is EEG signals classification. The EEG signals classification is divided into 2 parts which includes feature extraction and feature classification. Multivariate adaptive auto regressive (MVAAR) method is used in the feature extraction part because it is suitable for motor imaginary. Feature vectors are used to differentiate the different brain activity signals associated with the user’s attention, Linear Discriminate Linear (LDA) method is used in feature classification step to achieve these goals. The Feature extraction method MVAAR couldn’t extract the actual feature for the four movements so we couldn’t classify between them

Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

EEG Resting-State Brain Topological Reorganization as a Function of Age

Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated b y combining a variety of different imaging technologies (fMRI, EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signal srecordedatrestfrom71healthysubjects(age:20–63years). Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80

A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface

The advance in neuroscience and computer technology over the past decades have made brain-computer interface (BCI) a most promising area of neurorehabilitation and neurophysiology research. Limb motion decoding has gradually become a hot topic in the field of BCI. Decoding neural activity related to limb movement trajectory is considered to be of great help to the development of assistive and rehabilitation strategies for motor-impaired users. Although a variety of decoding methods have been proposed for limb trajectory reconstruction, there does not yet exist a review that covers the performance evaluation of these decoding methods. To alleviate this vacancy, in this paper, we evaluate EEG-based limb trajectory decoding methods regarding their advantages and disadvantages from a variety of perspectives. Specifically, we first introduce the differences in motor execution and motor imagery in limb trajectory reconstruction with different spaces (2D and 3D). Then, we discuss the limb motion trajectory reconstruction methods including experiment paradigm, EEG pre-processing, feature extraction and selection, decoding methods, and result evaluation. Finally, we expound on the open problem and future outlooks