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

Identification of EEG signal patterns between adults with dyslexia and normal controls

Electroencephalography (EEG) is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, it plays a vital role in detecting neurological disorders such as epilepsy. Dyslexia is a hidden learning disability with a neurological origin affecting a significant amount of the world population. Studies show unique brain structures and behaviours in individuals with dyslexia and these variations have become more evident with the use of techniques such as EEG, Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Positron Emission Tomography (PET). In this thesis, we are particularly interested in discussing the use of EEG to explore unique brain activities of adults with dyslexia. We attempt to discover unique EEG signal patterns between adults with dyslexia compared to normal controls while performing tasks that are more challenging for individuals with dyslexia. These tasks include real--‐word reading, nonsense--‐ word reading, passage reading, Rapid Automatized Naming (RAN), writing, typing, browsing the web, table interpretation and typing of random numbers. Each participant was instructed to perform these specific tasks while staying seated in front of a computer screen with the EEG headset setup on his or her head. The EEG signals captured during these tasks were examined using a machine learning classification framework, which includes signal preprocessing, frequency sub--‐band decomposition, feature extraction, classification and verification. Cubic Support Vector Machine (CSVM) classifiers were developed for separate brain regions of each specified task in order to determine the optimal brain regions and EEG sensors that produce the most unique EEG signal patterns between the two groups. The research revealed that adults with dyslexia generated unique EEG signal patterns compared to normal controls while performing the specific tasks. One of the vital discoveries of this research was that the nonsense--‐words classifiers produced higher Validation Accuracies (VA) compared to real--‐ words classifiers, confirming difficulties in phonological decoding skills seen in individuals with dyslexia are reflected in the EEG signal patterns, which was detected in the left parieto--‐occipital. It was also uncovered that all three reading tasks showed the same optimal brain region, and RAN which is known to have a relationship to reading also showed optimal performance in an overlapping region, demonstrating the likelihood that the association between reading and RAN reflects in the EEG signal patterns. Finally, we were able to discover brain regions that produced exclusive EEG signal patterns between the two groups that have not been reported before for writing, typing, web browsing, table interpretation and typing of random numbers

Review of EEG-based pattern classification frameworks for dyslexia

Dyslexia is a disability that causes difficulties in reading and writing despite average intelligence. This hidden disability often goes undetected since dyslexics are normal and healthy in every other way. Electroencephalography (EEG) is one of the upcoming methods being researched for identifying unique brain activation patterns in dyslexics. The aims of this paper are to examine pros and cons of existing EEG-based pattern classification frameworks for dyslexia and recommend optimisations through the findings to assist future research. A critical analysis of the literature is conducted focusing on each framework’s (1) data collection, (2) pre-processing, (3) analysis and (4) classification methods. A wide range of inputs as well as classification approaches has been experimented for the improvement in EEG-based pattern classification frameworks. It was uncovered that incorporating reading- and writing-related tasks to experiments used in data collection may help improve these frameworks instead of using only simple tasks, and those unwanted artefacts caused by body movements in the EEG signals during reading and writing activities could be minimised using artefact subspace reconstruction. Further, support vector machine is identified as a promising classifier to be used in EEG-based pattern classification frameworks for dyslexia

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

On Mapping EEG Information into Music

With the rise of ever-more affordable EEG equipment available to musicians, artists and researchers, designing and building a Brain-Computer Music Interface (BCMI) system has recently become a realistic achievement. This chapter discusses previous research in the fields of mapping, sonification and musification in the context of designing a BCMI system and will be of particular interest to those who seek to develop their own. Design of a BCMI requires unique consider-ations due to the characteristics of the EEG as a human interface device (HID). This chapter analyses traditional strategies for mapping control from brain waves alongside previous research in bio-feedback musical systems. Advances in music technology have helped provide more complex approaches with regards to how music can be affected and controlled by brainwaves. This, paralleled with devel-opments in our understanding of brainwave activity has helped push brain-computer music interfacing into innovative realms of real-time musical perfor-mance, composition and applications for music therapy

Brain Waves, A Cultural History: Oscillations of Neuroscience, Technology, Telepathy, and Transcendence

This project proceeds from a narrow question: What, if anything, is a brain wave? Beguiling in its simplicity, this question prompts a cultural-historical investigation that spans over 150 years of science, technology, and society. Proposed in 1869, the original theory of brain waves cites etheric undulations to explain reports of apparent thought transference. Though most modern thinkers no longer believe in outright telepathy, I argue that dreams of thought transmission and other mental miracles subtly persist—not in obscure and occult circles, but at the forefront of technoscience. A hybrid of science and fiction, brain waves represent an ideal subject through which to explore the ways in which technical language shrouds spiritual dreams. Today, the phrase “brain waves” often function as shorthand for electrical changes in the brain, particularly in the context of technologies that purport to “read” some aspect of mental function, or to transmit neural data to a digital device. While such technologies appear uniquely modern, the history of brain waves reveals that they are merely the millennial incarnation of a much older hope—a hope for transmission and transcendence via the brain’s emanations

The Human Auditory System

This book presents the latest findings in clinical audiology with a strong emphasis on new emerging technologies that facilitate and optimize a better assessment of the patient. The book has been edited with a strong educational perspective (all chapters include an introduction to their corresponding topic and a glossary of terms). The book contains material suitable for graduate students in audiology, ENT, hearing science and neuroscience

Sensor-based artificial intelligence to support people with cognitive and physical disorders

A substantial portion of the world's population deals with disability. Many disabled people do not have equal access to healthcare, education, and employment opportunities, do not receive specific disability-related services, and experience exclusion from everyday life activities. One way to face these issues is through the use of healthcare technologies. Unfortunately, there is a large amount of diverse and heterogeneous disabilities, which require ad-hoc and personalized solutions. Moreover, the design and implementation of effective and efficient technologies is a complex and expensive process involving challenging issues, including usability and acceptability. The work presented in this thesis aims to improve the current state of technologies available to support people with disorders affecting the mind or the motor system by proposing the use of sensors coupled with signal processing methods and artificial intelligence algorithms. The first part of the thesis focused on mental state monitoring. We investigated the application of a low-cost portable electroencephalography sensor and supervised learning methods to evaluate a person's attention. Indeed, the analysis of attention has several purposes, including the diagnosis and rehabilitation of children with attention-deficit/hyperactivity disorder. A novel dataset was collected from volunteers during an image annotation task, and used for the experimental evaluation using different machine learning techniques. Then, in the second part of the thesis, we focused on addressing limitations related to motor disability. We introduced the use of graph neural networks to process high-density electromyography data for upper limbs amputees’ movement/grasping intention recognition for enabling the use of robotic prostheses. High-density electromyography sensors can simultaneously acquire electromyography signals from different parts of the muscle, providing a large amount of spatio-temporal information that needs to be properly exploited to improve recognition accuracy. The investigation of the approach was conducted using a recent real-world dataset consisting of electromyography signals collected from 20 volunteers while performing 65 different gestures. In the final part of the thesis, we developed a prototype of a versatile interactive system that can be useful to people with different types of disabilities. The system can maintain a food diary for frail people with nutrition problems, such as people with neurocognitive diseases or frail elderly people, which may have difficulties due to forgetfulness or physical issues. The novel architecture automatically recognizes the preparation of food at home, in a privacy-preserving and unobtrusive way, exploiting air quality data acquired from a commercial sensor, statistical features extraction, and a deep neural network. A robotic system prototype is used to simplify the interaction with the inhabitant. For this work, a large dataset of annotated sensor data acquired over a period of 8 months from different individuals in different homes was collected. Overall, the results achieved in the thesis are promising, and pave the way for several real-world implementations and future research directions

Rediscovering The Interpersonal: Models Of Networked Communication In New Media Performance

This paper examines the themes of human perception and participation within the contemporary paradigm and relates the hallmarks of the major paradigm shift which occurred in the mid-20th century from a structural view of the world to a systems view. In this context, the author’s creative practice is described, outlining a methodology for working with the communication networks and interpersonal feedback loops that help to define our relationships to each other and to media since that paradigm shift. This research is framed within a larger field of inquiry into the impact of contemporary New Media Art as we experience it. This thesis proposes generative/cybernetic/systems art as the most appropriate media to model the processes of cultural identity production and networked communication. It reviews brief definitions of the systems paradigm and some key principles of cybernetic theory, with emphasis on generative, indeterminate processes. These definitions provide context for a brief review of precedents for the use of these models in the arts, (especially in process art, experimental video, interactive art, algorithmic composition, and sound art) since the mid-20th century, in direct correlation to the paradigm shift into systems thinking. Research outcomes reported here describe a recent body of generative art performances that have evolved from this intermedial, research-based creative practice, and discuss its use of algorithms, electronic media, and performance to provide audiences with access to an intuitive model of the interpersonal in a networked world

Training the Conductor of the Brainwave Symphony: In Search of a Common Mechanism of Action for All Methods of Neurofeedback

There are several different methods of neurofeedback, most of which presume an operant conditioning model whereby the subject learns to control their brain activity in particular regions of the brain and/or at particular brainwave frequencies based on reinforcement. One method, however, called infra-low frequency [ILF] neurofeedback cannot be explained through this paradigm, yet it has profound effects on brain function. Like a conductor of a symphony, recent evidence demonstrates that the primary ILF (typically between 0.01–0.1 Hz), which correlates with the fluctuation of oxygenated and deoxygenated blood in the brain, regulates all of the classic brainwave bands (i.e. alpha, theta, delta, beta, gamma). The success of ILF neurofeedback suggests that all forms of neurofeedback may work through a similar mechanism that does not fit the operant conditioning paradigm. This chapter focuses on the possible mechanisms of action for ILF neurofeedback, which may be generalized, based on current evidence