DEEP LEARNING FOR EEG-BASED BRAIN-COMPUTER INTERFACES

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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) Press Release — Melding Our Minds with the Outside World Sample Chapter(s) Preface Chapter 2: Brain Signal Acquisition Chapter 3: Deep Learning Foundations Contents: Preface Background: Introduction Brain Signal Acquisition Deep Learning Foundations Deep Learning-Based BCI and Its Applications: Deep Learning-Based BCI Deep Learning-Based BCI Applications Recent Advances on Deep Learning for EEG-Based BCI: Robust Brain Signal Representation Learning Cross-Scenario Classification Semi-Supervised Classification Typical Deep Learning for EEG-Based BCI Applications: Authentication Visual Reconstruction Language Interpretation Intent Recognition in Assisted Living Patient-Independent Neurological Disorder Detection Future Directions and Conclusion Bibliography Index Readership: Advanced undergraduate and graduate students, researchers and practitioners in the fields of computer science, data mining, artificial intelligence, and neuroscience. Will also be of interest to industry or companies invested in combining brain signals with real world applications including user authentication, neurological diagnosis, autonomous cars, smart homes, IoT, etc.

COGNITIVE MODELING OF HUMAN MEMORY AND LEARNING: A NON-INVASIVE BRAIN-COMPUTER INTERFACING APPROACH

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【簡介】 Proposes computational models of human memory and learning using a brain-computer interfacing (BCI) approach Human memory modeling is important from two perspectives. First, the precise fitting of the model to an individual's short-term or working memory may help in predicting memory performance of the subject in future. Second, memory models provide a biological insight to the encoding and recall mechanisms undertaken by the neurons present in active brain lobes, participating in the memorization process. This book models human memory from a cognitive standpoint by utilizing brain activations acquired from the cortex by electroencephalographic (EEG) and functional near-infrared-spectroscopic (f-NIRs) means. Cognitive Modeling of Human Memory and Learning A Non-invasive Brain-Computer Interfacing Approach begins with an overview of the early models of memory. The authors then propose a simplistic model of Working Memory (WM) built with fuzzy Hebbian learning. A second perspective of memory models is concerned with Short-Term Memory (STM)-modeling in the context of 2-dimensional object-shape reconstruction from visually examined memorized instances. A third model assesses the subjective motor learning skill in driving from erroneous motor actions. Other models introduce a novel strategy of designing a two-layered deep Long Short-Term Memory (LSTM) classifier network and also deal with cognitive load assessment in motor learning tasks associated with driving. The book ends with concluding remarks based on principles and experimental results acquired in previous chapters. Examines the scope of computational models of memory and learning with special emphasis on classification of memory tasks by deep learning-based models Proposes two algorithms of type-2 fuzzy reasoning: Interval Type-2 fuzzy reasoning (IT2FR) and General Type-2 Fuzzy Sets (GT2FS) Considers three classes of cognitive loads in the motor learning tasks for driving learners Cognitive Modeling of Human Memory and Learning A Non-invasive Brain-Computer Interfacing Approach will appeal to researchers in cognitive neuro-science and human/brain-computer interfaces. It is also beneficial to graduate students of computer science/electrical/electronic engineering. 【目錄】

BRAIN VS COMPUTER (2版)

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In this follow up to Brain vs Computer: The Challenge of the Century, Jean-Pierre Fillard brings together diverse perspectives to address the recurring theme of rivalry between man and machine. Accelerated by recent events such as the Covid-19 pandemic that caught the world by surprise and brought it to a standstill, the use of technology has become more relevant than ever. What new conclusions can we draw in this debate featuring humans (brain) on the one side, and artificial intelligence (computer) on the other? Featuring brand new content including a complementary perspective from the arts, the author balances the argument from the traditional scientific approach of logic, rationality, and computation with instinct, intuition, and emotion. Read together with his latest offerings Longevity in a 2.0 World and Transhumanism: A Realistic Future? this trilogy culminates in an attempt to answer one of the most exciting questions of our time.