Generalization of Deep Neural Networks for EEG Data Analysis

University of Technology Sydney. Faculty of Engineering and Information Technology.Electroencephalography (EEG) facilitates the neuroscientific research and applications by virtue of its properties such as non-invasion, affordability, mobility, etc. However, challenges including high artefacts pending, intra- and cross-subject variance, limited data availability, etc., pose the difficulty in reaching solid conclusions. This thesis explores how to utilize and generalize deep neural networks (DNN), which have set new performance records in various fields, to analyze EEG data to mitigate these challenges in reaching enlightening conclusions. This thesis brings the comprehension of research goals by the introduction of EEG and DNN background. It reviews the conventional EEG signal processing methods and highlights the challenges of EEG data analysis. As part of this work, EEG datasets from three stereotypical brain-computer interface (BCI) experiments are described in detail to assess the proposed methods by benchmarking. To illustrate the DNN centered methodology for addressing divergent challenges of EEG data analysis, a research map is compassed to show respective contributions in fulfilling the following specific goals: (1) Selection of appropriate DNN structures targeting EEG data captured during different BCI experiments. (2) Solutions to address the intra- and cross-subject variance of EEG data. (3) Utilization of brain-inspired computation such as memory network to improve the performance of processing EEG data. (4) Exploration of new computation paradigm, i.e., reinforcement learning (RL), to relieve the noise label challenge and to improve data utilization. By a series of published and in-preparation papers, this thesis demonstrates different achievements corresponding to the set goals: (1) Investigation of the computation traits of neural network structures and revelation of their effectiveness in EEG signal processing. The designed recurrent residual network (RRN), which is based on the recurrent structure, residual structures, etc., achieves the highest classification accuracy and provides coherent evidence and interpretation to the efficacy of conventional hand-crafted filters. (2) Invention of adversarial method in light of the domain adaptation (DA) and generative adversarial network (GAN) to address inter- and cross-subject variance. The proposed subject adaptation network (SAN), which borrows the philosophy of GAN but works in different ways, shows promising results among EEG sample clustering, sample-of-interest selection, EEG data alignment, etc. (3) Systematic study of memory networks and proposal of memory module based on self-organized maps (SOM). Work on a stacked version of differentiable neural computer (DNC), reveals that EEG features buried in the endurance experiment can be more effectively harvested by augmenting memory and consequently boost the performance. SOM-based memory network demonstrates its capability in reducing network complexity. (4) Implementation of reinforcement learning (RL) for EEG data analysis to relieve the noisy label challenge and to improve EEG data utilization. Instantiation of RL framework such as deep Q-network (DQN) demonstrates its feasibility and practicability for certain BCI experiments. Generally, through this sequence of work and papers, this thesis contributes from different aspects that well advance the EEG data analysis via DNN

EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels. In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition. RGNN considers the biological topology among different brain regions to capture both local and global relations among different EEG channels. Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in a graph neural network where the connection and sparseness of the adjacency matrix are inspired by neuroscience theories of human brain organization. In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to better handle cross-subject EEG variations and noisy labels, respectively. Extensive experiments on two public datasets, SEED and SEED-IV, demonstrate the superior performance of our model than state-of-the-art models in most experimental settings. Moreover, ablation studies show that the proposed adjacency matrix and two regularizers contribute consistent and significant gain to the performance of our RGNN model. Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition

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

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subjects active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large- scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the- art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.Comment: 10 page

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review