Financial Times Book of the Year An urgent new novel about death, war, and memory from the highly acclaimed Croatian writer In this breathtaking final work, Daša Drndic reaches new heights. Andreas Ban’s suicide attempt has failed. Though very ill, he still finds the will to tap on the glass of history to summon those imprisoned within. Mercilessly, he dissects society and his environment, shunning all favors as he goes after the evils and hidden secrets of our times. History remembers the names of the perpetrators, not the victims—Ban remembers and honors the lost. He travels from Rijeka to Zagreb, from Belgrade to Tirana, from Parisian avenues to Italian castles. Ghosts follow him wherever he goes: chess grandmasters who disappeared during WWII; the lost inhabitants of Latvia; war criminals who found work in the CIA and died peacefully in their beds. Ban’s family is with him too, those already dead and those with one foot in the grave. As if left with only a few pieces in a chess game, Andreas Ban—and Daša Drndic—play a stunning last match against Death.

Winner of 2018 Warwick Prize for Women in Translation From the author of the highly acclaimed Trieste, a fierce novel about history, memory, and illness Andreas Ban, a psychologist who no longer psychologizes, a writer who no longer writes, lives alone in a coastal town in Croatia. His body is failing him. He sifts through the remnants of his life—his research, books, medical records, photographs—remembering old lovers and friends, the tragedies of WWII, the breakup of Yugoslavia. Ban’s memories of Belgrade (which he thought he had left behind) and of Amsterdam (a different world and life) alternate with meditations on hole-ridden time (ebbing away through its perforations), on his measly pension, on growing old and fragile, on the intelligence of rats and the agelessness of lobsters, on deadly nightshade. He tries to push the past away, "to land on a little island of time in which tomorrow does not exist, in which yesterday is buried.” Drndic´ leafs through the horrors of history with a cold unflinching wit. “The past is riddled with holes,” she writes. “Souvenirs can’t help here.” And they don't.

Longlisted for the Republic of Consciousness Prize 2019, a swift, biting novel from the late Croatian master, Dasa Drndic Two elderly people, Artur and Isabella, meet and have a passionate sexual encounter on New Year’s Eve. Details of the lives of Artur, a retired Yugoslav army captain, and Isabella, a Holocaust survivor, are revealed through police dossiers. As they fight loneliness and aging, they take comfort in small things: for Artur, a collection of 274 hats; for Isabella, a family of garden gnomes who live in her apartment. Later, we meet the ill-fated Pupi, who dreamed of becoming a sculptor but instead became a chemist and then a spy. As Eileen Battersby wrote, “As he stands, in the zoo, gazing at a pair of rhinos, in a city most likely present-day Belgrade, this battered Everyman feels very alone: ‘I would like to tell someone, anyone, I’d like to tell someone: I buried Mother today.’” Pupi sets out to correct his family’s crimes by returning silverware to its original Jewish owners through the help of an unlikely friend, a pawnbroker. Described by Dasa Drndic as “my ugly little book,” Doppelgänger was her personal favorite.

An old Italian woman seeks a reunion with her son, fathered by an SS officer and taken away by German authorities sixty-two years ago, while she remembers and discusses the atrocities committed in Northern Italy during World War II.

This book presents the conceptual and mathematical basis and the implementation of both electroencephalogram (EEG) and EEG signal processing in a comprehensive, simple, and easy-to-understand manner. EEG records the electrical activity generated by the firing of neurons within human brain at the scalp. They are widely used in clinical neuroscience, psychology, and neural engineering, and a series of EEG signal-processing techniques have been developed. Intended for cognitive neuroscientists, psychologists and other interested readers, the book discusses a range of current mainstream EEG signal-processing and feature-extraction techniques in depth, and includes chapters on the principles and implementation strategies.

Deep Learning for EEG-Based Brain-Computer Interfaces is an exciting book that describes how emerging deep learning improves the future development of Brain-Computer Interfaces (BCI) in terms of representations, algorithms and applications. BCI bridges humanity's neural world and the physical world by decoding an individuals' brain signals into commands recognizable by computer devices.This book presents a highly comprehensive summary of commonly-used brain signals; a systematic introduction of around 12 subcategories of deep learning models; a mind-expanding summary of 200+ state-of-the-art studies adopting deep learning in BCI areas; an overview of a number of BCI applications and how deep learning contributes, along with 31 public BCI data sets. The authors also introduce a set of novel deep learning algorithms aimed at current BCI challenges such as robust representation learning, cross-scenario classification, and semi-supervised learning. Various real-world deep learning-based BCI applications are proposed and some prototypes are presented. The work contained within proposes effective and efficient models which will provide inspiration for people in academia and industry who work on BCI.Related Link(s)

EEG-Based Diagnosis of Alzheimer Disease: A Review and Novel Approaches for Feature Extraction and Classification Techniques provides a practical and easy-to-use guide for researchers in EEG signal processing techniques, Alzheimer's disease, and dementia diagnostics. The book examines different features of EEG signals used to properly diagnose Alzheimer's Disease early, presenting new and innovative results in the extraction and classification of Alzheimer's Disease using EEG signals. This book brings together the use of different EEG features, such as linear and nonlinear features, which play a significant role in diagnosing Alzheimer's Disease. - Includes the mathematical models and rigorous analysis of various classifiers and machine learning algorithms from a perspective of clinical deployment - Covers the history of EEG signals and their measurement and recording, along with their uses in clinical diagnostics - Analyzes spectral, wavelet, complexity and other features of early and efficient Alzheimer's Disease diagnostics - Explores support vector machine-based classification to increase accuracy

A study of epilepsy from an engineering perspective, this volume begins by summarizing the physiology and the fundamental ideas behind the measurement, analysis and modeling of the epileptic brain. It introduces the EEG and provides an explanation of the type of brain activity likely to register in EEG measurements, offering an overview of how these EEG records are and have been analyzed in the past. The book focuses on the problem of seizure detection and surveys the physiologically based dynamic models of brain activity. Finally, it addresses the fundamental question: can seizures be predicted? Based on the authors' extensive research, the book concludes by exploring a range of future possibilities in seizure prediction.

This book presents advanced methodologies in two areas related to electroencephalogram (EEG) signals: detection of epileptic seizures and identification of mental states in brain computer interface (BCI) systems. The proposed methods enable the extraction of this vital information from EEG signals in order to accurately detect abnormalities revealed by the EEG. New methods will relieve the time-consuming and error-prone practices that are currently in use. Common signal processing methodologies include wavelet transformation and Fourier transformation, but these methods are not capable of managing the size of EEG data. Addressing the issue, this book examines new EEG signal analysis approaches with a combination of statistical techniques (e.g. random sampling, optimum allocation) and machine learning methods. The developed methods provide better results than the existing methods. The book also offers applications of the developed methodologies that have been tested on several real-time benchmark databases. This book concludes with thoughts on the future of the field and anticipated research challenges. It gives new direction to the field of analysis and classification of EEG signals through these more efficient methodologies. Researchers and experts will benefit from its suggested improvements to the current computer-aided based diagnostic systems for the precise analysis and management of EEG signals. /div

Magnetoencephalography (MEG) is an invaluable functional brain imaging technique that provides direct, real-time monitoring of neuronal activity necessary for gaining insight into dynamic cortical networks. Our intentions with this book are to cover the richness and transdisciplinary nature of the MEG field, make it more accessible to newcomers and experienced researchers and to stimulate growth in the MEG area. The book presents a comprehensive overview of MEG basics and the latest developments in methodological, empirical and clinical research, directed toward master and doctoral students, as well as researchers. There are three levels of contributions: 1) tutorials on instrumentation, measurements, modeling, and experimental design; 2) topical reviews providing extensive coverage of relevant research topics; and 3) short contributions on open, challenging issues, future developments and novel applications. The topics range from neuromagnetic measurements, signal processing and source localization techniques to dynamic functional networks underlying perception and cognition in both health and disease. Topical reviews cover, among others: development on SQUID-based and novel sensors, multi-modal integration (low field MRI and MEG; EEG and fMRI), Bayesian approaches to multi-modal integration, direct neuronal imaging, novel noise reduction methods, source-space functional analysis, decoding of brain states, dynamic brain connectivity, sensory-motor integration, MEG studies on perception and cognition, thalamocortical oscillations, fetal and neonatal MEG, pediatric MEG studies, cognitive development, clinical applications of MEG in epilepsy, pre-surgical mapping, stroke, schizophrenia, stuttering, traumatic brain injury, post-traumatic stress disorder, depression, autism, aging and neurodegeneration, MEG applications in cognitive neuropharmacology and an overview of the major open-source analysis tools.