This two-volume set constitutes the proceedings of the 13th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2019, held as part of the 21st International Conference, HCI International 2019, which took place in Orlando, FL, USA, in July 2019. The total of 1274 papers and 209 posters included in the 35 HCII 2019 proceedings volumes was carefully reviewed and selected from 5029 submissions. UAHCI 2019 includes a total of 95 regular papers; they were organized in topical sections named: universal access theory, methods and tools; novel approaches to accessibility; universal access to learning and education; virtual and augmented reality in universal access; cognitive and learning disabilities; multimodal interaction; and assistive environments.

This two-volume set constitutes the refereed proceedings of the 16th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2022, held as part of the 24th International Conference, HCI International 2022, held as a virtual event, in June-July 2022. A total of 1271 papers and 275 posters included in the 39 HCII 2022 proceedings volumes. UAHCI 2022 includes a total of 73 papers; they focus on topics related to universal access methods, techniques and practices, studies on accessibility, design for all, usability, UX and technology acceptance, emotion and behavior recognition for universal access, accessible media, access to learning and education, as well universal access to virtual and intelligent assistive environments.

This book includes a collection of expanded papers from the 2019 Sino-German Symposium on AI-supported educational technologies, which was held in Wuhan, China, March, 2019. The contributors are distinguished researchers from computer science and learning science. The contributions are organized in four sections: (1) Overviews and systematic perspectives , (2) Example Systems, (3) Algorithms, and (4) Insights gained from empirical studies. For example, different data mining and machine learning methods to quantify different profiles of a learner in different learning situations (including interaction patterns, cognitive modes, knowledge skills, interests and emotions etc.) as well as connections to measurements in psychology and learning sciences are discussed in the chapters.

Frontiers has organized a series of Research Topics to highlight the latest advancements in research across the field of cognition, with articles from Members of our accomplished Editorial Boards and from a curated list of experts in their field. This editorial initiative of particular relevance, led by Prof. Juan Lupiáñez, Specialty Chief Editor of the Attention section, is focused on new insights, novel developments, current challenges, latest discoveries, recent advances, and future perspectives in the field of attention.

Signal Processing for Neuroscientists introduces analysis techniques primarily aimed at neuroscientists and biomedical engineering students with a reasonable but modest background in mathematics, physics, and computer programming. The focus of this text is on what can be considered the 'golden trio' in the signal processing field: averaging, Fourier analysis, and filtering. Techniques such as convolution, correlation, coherence, and wavelet analysis are considered in the context of time and frequency domain analysis. The whole spectrum of signal analysis is covered, ranging from data acquisition to data processing; and from the mathematical background of the analysis to the practical application of processing algorithms. Overall, the approach to the mathematics is informal with a focus on basic understanding of the methods and their interrelationships rather than detailed proofs or derivations. One of the principle goals is to provide the reader with the background required to understand the principles of commercially available analyses software, and to allow him/her to construct his/her own analysis tools in an environment such as MATLAB®. - Multiple color illustrations are integrated in the text - Includes an introduction to biomedical signals, noise characteristics, and recording techniques - Basics and background for more advanced topics can be found in extensive notes and appendices - A Companion Website hosts the MATLAB scripts and several data files: http://www.elsevierdirect.com/companion.jsp?ISBN=9780123708670

Electroencephalograms (EEGs) are becoming increasingly important measurements of brain activity and they have great potential for the diagnosis and treatment of mental and brain diseases and abnormalities. With appropriate interpretation methods they are emerging as a key methodology to satisfy the increasing global demand for more affordable and effective clinical and healthcare services. Developing and understanding advanced signal processing techniques for the analysis of EEG signals is crucial in the area of biomedical research. This book focuses on these techniques, providing expansive coverage of algorithms and tools from the field of digital signal processing. It discusses their applications to medical data, using graphs and topographic images to show simulation results that assess the efficacy of the methods. Additionally, expect to find: explanations of the significance of EEG signal analysis and processing (with examples) and a useful theoretical and mathematical background for the analysis and processing of EEG signals; an exploration of normal and abnormal EEGs, neurological symptoms and diagnostic information, and representations of the EEGs; reviews of theoretical approaches in EEG modelling, such as restoration, enhancement, segmentation, and the removal of different internal and external artefacts from the EEG and ERP (event-related potential) signals; coverage of major abnormalities such as seizure, and mental illnesses such as dementia, schizophrenia, and Alzheimer’s disease, together with their mathematical interpretations from the EEG and ERP signals and sleep phenomenon; descriptions of nonlinear and adaptive digital signal processing techniques for abnormality detection, source localization and brain-computer interfacing using multi-channel EEG data with emphasis on non-invasive techniques, together with future topics for research in the area of EEG signal processing. The information within EEG Signal Processing has the potential to enhance the clinically-related information within EEG signals, thereby aiding physicians and ultimately providing more cost effective, efficient diagnostic tools. It will be beneficial to psychiatrists, neurophysiologists, engineers, and students or researchers in neurosciences. Undergraduate and postgraduate biomedical engineering students and postgraduate epileptology students will also find it a helpful reference.