Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions

Structure-function relation in a stochastic whole-brain model at criticality

Understanding the relation between brain architecture and function is one of the central issues in neuroscience nowadays. In the last few years, important efforts have been devoted to map the large-scale structure of the human cortex, the so-called "connectome". An example is the neuroanatomical connectivity matrix of the entire human brain obtained through MR diffusion tractography. Recent studies proposed a stochastic model built on top of this connectivity matrix that displays a phase-transition and is able to reproduce several aspects of brain functioning when tuned to its critical point. This master thesis is aimed to review recent results on this subject and to get a deeper insight into the model by studying the distribution of the avalanches, the dynamical range and to investigate how the use of simulated connectivity matrices affects the dynamics. Furthermore, a theoretical description of the dynamics is proposed by introducing a master equation in order to understand the nature of the phase transition and the role of stochasticity.ope

PRINCIPLES OF INFORMATION PROCESSING IN NEURONAL AVALANCHES

How the brain processes information is poorly understood. It has been suggested that the imbalance of excitation and inhibition (E/I) can significantly affect information processing in the brain. Neuronal avalanches, a type of spontaneous activity recently discovered, have been ubiquitously observed in vitro and in vivo when the cortical network is in the E/I balanced state. In this dissertation, I experimentally demonstrate that several properties regarding information processing in the cortex, i.e. the entropy of spontaneous activity, the information transmission between stimulus and response, the diversity of synchronized states and the discrimination of external stimuli, are optimized when the cortical network is in the E/I balanced state, exhibiting neuronal avalanche dynamics. These experimental studies not only support the hypothesis that the cortex operates in the critical state, but also suggest that criticality is a potential principle of information processing in the cortex. Further, we study the interaction structure in population neuronal dynamics, and discovered a special structure of higher order interactions that are inherent in the neuronal dynamics

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Self-Organized Criticality as a Neurodynamical Correlate of Consciousness: A neurophysiological approach to measure states of consciousness based on EEG-complexity features

Background and Objectives This thesis was based on the hypothesis that the physics-derived theoretical framework of self-organized criticality can be applied to the neuronal dynamics of the human brain. From a consciousness science perspective, this is especially appealing as critical brain dynamics imply a vicinity a phase transition, which is associated with optimized information processing functions as well as the largest repertoire of configurations that a system explores throughout its temporal evolution. Hence, self-organised criticality could serve as a neurodynamical correlate for consciousness, which provides the possibility of deriving empirically testable neurophysiological indices suitable to characterise and quantify states of consciousness. The purpose of this work was to experimentally examine the feasibility of the self-organized criticality theory as a correlate for states of consciousness. Therefore, it was aimed at answering the following research questions based on the analysis of three 64 channel EEG datasets: (i) Can signatures of self-organized criticality be found on the level of the EEG in terms of scale-free distribution of neuronal avalanches and the presence of long-range temporal correlations (LRTC) in neuronal oscillations? (ii) Are criticality features suitable to differentiate state of consciousness in the spectrum of wakefulness? (iii) Can the neuronal dynamics be shifted towards the critical point of a phase transition associated with optimized information processing function by mind-body interventions? (iv) Can an explicit relationship to other nonlinear complexity features and power spectral density parameter be identified? (v) Do EEG-based criticality features reflect individual temperament traits? Material and Methods (1): Re-analysis: Thirty participants highly proficient in meditation (mean age 47 years, 11 females/19 males, meditation experience of at least 5 years practice or more than 1000 h of total meditation time) were measured with 64-channel EEG during one session consisting of a task-free baseline resting, a reading condition and three meditation conditions, namely thoughtless emptiness, presence monitoring and focused attention. (2): 64-channel EEG was recorded from 34 participants (mean age 36.0 ±13.4 years, 24 females/ 10 males) before, during and after a professional singing bowl massage. Further, psychometric data was assessed including absorption capacity defined as the individual’s capacity for engaging attentional resources in sensory and imaginative experiences measured by the Tellegen-Absorption Scale (TAS-D), subjective changes in in body sensation, emotional state, and mental state (CSP-14) as well as the phenomenology of consciousness (PCI-K). (3): Electrophysiological data (64 channels of EEG, EOG, ECG, skin conductance, and respiration) was recorded from 116 participants (mean age 40.0 ±13.4 years, 83 females/ 33 males) – in collaboration with the Institute of Psychology, Bundeswehr University Munich - during a task-free baseline resting state. The individual level of sensory processing sensitivity was assessed using the High Sensitive Person Scale (HSPS-G). The datasets were analysed applying analytical tools from self-organized criticality theory (detrended fluctuation analysis, neuronal avalanche analysis), nonlinear complexity algorithms (multiscale entropy, Higuchi’s fractal dimension) and power spectral density. In study 1 and 2, task conditions were contrasted, and effect sizes were compared using a paired two-tailed t-test calculated across participants, and features. T-values were corrected for multiple testing using false discovery rate. To calculate correlations between the EEG features, Spearman’s rank correlation was applied after determining that the distribution was not appropriate for parametric testing by the Shapiro-Wilk test. In addition, in study 1, a discrimination analysis was carried out to determine the classification performance of the EEG features. Here, partial least squares regression and receiver operating characteristics analysis was applied. To determine whether the EEG features reflect individual temperament traits, the individual level of absorption capacity (study 2) and sensory processing sensitivity (study 3) was correlated with the EEG features using Spearman’s rank correlation. Results Signatures of self-organized criticality in the form of scale-free distribution of neuronal avalanches and long-range temporal correlations (LRTCs) in the amplitude of neural oscillations were observed in three distinct EEG-datasets. EEG criticality as well as complexity features were suitable to characterise distinct states of consciousness. In study 1, compared to the task-free resting condition, all three meditative states revealed significantly reduced long-range temporal correlation with moderate effect sizes (presence monitoring: d= -0.49, p<.001; thoughtless emptiness: d= -0.37, p<.001; and focused attention: d= -0.28, p=.003). The critical exponent was suitable to differentiate between focused attention and presence monitoring (d= -0.32, p=.02). Further, in study 2, the criticality features significantly changed during the course of the experiment, whereby values indicated a shift towards the critical regime during the sound condition. Both analyses of the first and second dataset revealed that the critical exponent was significantly negatively correlated with the sample entropy, the scaling exponent resulting from the DFA denoting the amount of long-range temporal correlations as well as Higuchi’s fractal dimension in each condition, respectively. In addition, the critical scaling exponent was found to be significantly negatively correlated with the trait absorption (Spearman's ρ= -0.39, p= .007), whereas an association between critical dynamics and the level of sensory processing sensitivity could not be verified (study 3). Conclusion The findings of this thesis suggest that neuronal dynamics are governed by the phenomena of self-organized criticality. EEG-based criticality features were shown to be sensitive to detect experimentally induced alterations in the state of consciousness. Further, an explicit relationship with nonlinear measures determining the degree of neuronal complexity was identified. Thus, self-organized criticality seems feasible as a neurodynamical correlate for consciousness with the potential to quantify and characterize states of consciousness. Its agreement with the current most influencing theories in the field of consciousness research is discussed

Breaking Down the Barriers To Operator Workload Estimation: Advancing Algorithmic Handling of Temporal Non-Stationarity and Cross-Participant Differences for EEG Analysis Using Deep Learning

This research focuses on two barriers to using EEG data for workload assessment: day-to-day variability, and cross- participant applicability. Several signal processing techniques and deep learning approaches are evaluated in multi-task environments. These methods account for temporal, spatial, and frequential data dependencies. Variance of frequency- domain power distributions for cross-day workload classification is statistically significant. Skewness and kurtosis are not significant in an environment absent workload transitions, but are salient with transitions present. LSTMs improve day- to-day feature stationarity, decreasing error by 59% compared to previous best results. A multi-path convolutional recurrent model using bi-directional, residual recurrent layers significantly increases predictive accuracy and decreases cross-participant variance. Deep learning regression approaches are applied to a multi-task environment with workload transitions. Accounting for temporal dependence significantly reduces error and increases correlation compared to baselines. Visualization techniques for LSTM feature saliency are developed to understand EEG analysis model biases

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data