Analysis of EEG signals using complex brain networks

The human brain is so complex that two mega projects, the Human Brain Project and the BRAIN Initiative project, are under way in the hope of answering important questions for peoples' health and wellbeing. Complex networks become powerful tools for studying brain function due to the fact that network topologies on real-world systems share small world properties. Examples of these networks are the Internet, biological networks, social networks, climate networks and complex brain networks. Complex brain networks in real time biomedical signal processing applications are limited because some graph algorithms (such as graph isomorphism), cannot be solved in polynomial time. In addition, they are hard to use in single-channel EEG applications, such as clinic applications in sleep scoring and depth of anaesthesia monitoring. The first contribution of this research is to present two novel algorithms and two graph models. A fast weighted horizontal visibility algorithm (FWHVA) overcoming the speed limitations for constructing a graph from a time series is presented. Experimental results show that the FWHVA can be 3.8 times faster than the Fast Fourier Transfer (FFT) algorithm when input signals exceed 4000 data points. A linear time graph isomorphism algorithm (HVGI) can determine the isomorphism of two horizontal visibility graphs (HVGs) in a linear time domain. This is an efficient way to measure the synchronized index between two time series. Difference visibility graphs (DVGs) inherit the advantages of horizontal visibility graphs. They are noise-robust, and they overcome a pitfall of visibility graphs (VG): that the degree distribution (DD) doesn't satisfy a pure power-law. Jump visibility graphs (JVGs) enhance brain graphs allowing the processing of non-stationary biomedical signals. This research shows that the DD of JVGs always satisfies a power-lower if the input signals are purely non-stationary. The second highlight of this work is the study of three clinical biomedical signals: alcoholic, epileptic and sleep EEGs. Based on a synchronization likelihood and maximal weighted matching method, this work finds that the processing repeated stimuli and unrepeated stimuli in the controlled drinkers is larger than that in the alcoholics. Seizure detections based on epileptic EEGs have also been investigated with three graph features: graph entropy of VGs, mean strength of HVGs, and mean degrees of JVGs. All of these features can achieve 100% accuracy in seizure identification and differentiation from healthy EEG signals. Sleep EEGs are evaluated based on VG and DVG methods. It is shown that the complex brain networks exhibit more small world structure during deep sleep. Based on DVG methods, the accuracy peaks at 88:9% in a 5-state sleep stage classification from 14; 943 segments from single-channel EEGs. This study also introduces two weighted complex network approaches to analyse the nonlinear EEG signals. A weighted horizontal visibility graph (WHVG) is proposed to enhance noise-robustness properties. Tested with two Chaos signals and an epileptic EEG database, the research shows that the mean strength of the WHVG is more stable and noise-robust than those features from FFT and entropy. Maximal weighted matching algorithms have been applied to evaluate the difference in complex brain networks of alcoholics and controlled drinkers. The last contribution of this dissertation is to develop an unsupervised classifier for biomedical signal pattern recognition. A Multi-Scale Means (MSK-Means) algorithm is proposed for solving the subject-dependent biomedical signals classification issue. Using JVG features from the epileptic EEG database, the MSK-Means algorithm is 4:7% higher in identifying seizures than those by the K-means algorithm and achieves 92:3% accuracy for localizing the epileptogenic zone. The findings suggest that the outcome of this thesis can improve the performance of complex brain networks for biomedical signal processing and nonlinear time series analysis

Molecular targets of alcohol action: translational research for pharmacotherapy development and screening.

Alcohol abuse and dependence are multifaceted disorders with neurobiological, psychological, and environmental components. Research on other complex neuropsychiatric diseases suggests that genetically influenced intermediate characteristics affect the risk for heavy alcohol consumption and its consequences. Diverse therapeutic interventions can be developed through identification of reliable biomarkers for this disorder and new pharmacological targets for its treatment. Advances in the fields of genomics and proteomics offer a number of possible targets for the development of new therapeutic approaches. This brain-focused review highlights studies identifying neurobiological systems associated with these targets and possible pharmacotherapies, summarizing evidence from clinically relevant animal and human studies, as well as sketching improvements and challenges facing the fields of proteomics and genomics. Concluding thoughts on using results from these profiling technologies for medication development are also presented

Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification

EEG technology finds applications in several domains. Currently, most EEG systems require subjects to wear several electrodes on the scalp to be effective. However, several channels might include noisy information, redundant signals, induce longer preparation times and increase computational times of any automated system for EEG decoding. One way to reduce the signal-to-noise ratio and improve classification accuracy is to combine channel selection with feature extraction, but EEG signals are known to present high inter-subject variability. In this work we introduce a novel algorithm for subject-independent channel selection of EEG recordings. Considering multi-channel trial recordings as statistical units and the EEG decoding task as the class of reference, the algorithm (i) exploits channel-specific 1D-Convolutional Neural Networks (1D-CNNs) as feature extractors in a supervised fashion to maximize class separability; (ii) it reduces a high dimensional multi-channel trial representation into a unique trial vector by concatenating the channels' embeddings and (iii) recovers the complex inter-channel relationships during channel selection, by exploiting an ensemble of AutoEncoders (AE) to identify from these vectors the most relevant channels to perform classification. After training, the algorithm can be exploited by transferring only the parametrized subgroup of selected channel-specific 1D-CNNs to new signals from new subjects and obtain low-dimensional and highly informative trial vectors to be fed to any classifier

Pairing Neutral Cues with Alcohol Intoxication: New Findings in Executive and Attention Networks

Rationale: Alcohol-associated stimuli capture attention, yet drinkers differ in the precise stimuli that become paired with intoxication. Objectives: Extending our prior work to examine the influence of alcoholism risk factors, we paired abstract visual stimuli with intravenous alcohol delivered covertly and examined brain responses to these Pavlovian conditioned stimuli in fMRI when subjects were not intoxicated. Methods: Sixty healthy drinkers performed task-irrelevant alcohol conditioning that presented geometric shapes as conditioned stimuli. Shapes were paired with a rapidly rising alcohol limb (CS+) using intravenous alcohol infusion targeting a final peak breath alcohol concentration of 0.045 g/dL or saline (CS−) infusion at matched rates. On day two, subjects performed monetary delay discounting outside the scanner to assess delay tolerance and then underwent event-related fMRI while performing the same task with CS+, CS−, and an irrelevant symbol. Results: CS+ elicited stronger activation than CS− in frontoparietal executive/attention and orbitofrontal reward-associated networks. Risk factors including family history, recent drinking, sex, and age of drinking onset did not relate to the [CS+ > CS−] activation. Delay-tolerant choice and [CS+ > CS−] activation in right inferior parietal cortex were positively related. Conclusions: Networks governing executive attention and reward showed enhanced responses to stimuli experimentally paired with intoxication, with the right parietal cortex implicated in both alcohol cue pairing and intertemporal choice. While different from our previous study results in 14 men, we believe this paradigm in a large sample of male and female drinkers offers novel insights into Pavlovian processes less affected by idiosyncratic drug associations

Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: Insights from the canadian biomarker integration network in depression

Subsequent to global initiatives in mapping the human brain and investigations of neurobiological markers for brain disorders, the number of multi-site studies involving the collection and sharing of large volumes of brain data, including electroencephalography (EEG), has been increasing. Among the complexities of conducting multi-site studies and increasing the shelf life of biological data beyond the original study are timely standardization and documentation of relevant study parameters. We presentthe insights gained and guidelines established within the EEG working group of the Canadian Biomarker Integration Network in Depression (CAN-BIND). CAN-BIND is a multi-site, multi-investigator, and multiproject network supported by the Ontario Brain Institute with access to Brain-CODE, an informatics platform that hosts a multitude of biological data across a growing list of brain pathologies. We describe our approaches and insights on documenting and standardizing parameters across the study design, data collection, monitoring, analysis, integration, knowledge-translation, and data archiving phases of CAN-BIND projects. We introduce a custom-built EEG toolbox to track data preprocessing with open-access for the scientific community. We also evaluate the impact of variation in equipment setup on the accuracy of acquired data. Collectively, this work is intended to inspire establishing comprehensive and standardized guidelines for multi-site studies

Electroencephalogram classification of brain states using deep learning approach

The oldest diagnostic method in the field of neurology is electroencephalography (EEG). To grasp the information contained in EEG signals, numerous deep machine learning architectures have been developed recently. In brain computer interface (BCI) systems, classification is crucial. Many recent studies have effectively employed deep learning algorithms to learn features and classify various sorts of data. A systematic review of EEG classification using deep learning was conducted in this research, resulting in 90 studies being discovered from the Web of Science and PubMed databases. Researchers looked at a variety of factors in these studies, including the task type, EEG pre-processing techniques, input type, and the depth of learning. This study summarises the current methodologies and performance results in EEG categorization using deep learning. A series of practical recommendations is provided in the hopes of encouraging or directing future research using EEG datasets to use deep learning