Computational Intelligence And Deep Learning Methods For Neuro Rehabilitation Applications

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Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications

D. Jude Hemanth
Computational Intelligence and Deep Learning Methods for Neuro rehabilitation Applications Book PDF

Author : D. Jude Hemanth
Publisher : Elsevier
Page : 304 pages
File Size : 51,6 Mb
Release : 2023-11-17
Category : Science
ISBN : 9780443137730

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Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications by D. Jude Hemanth Pdf

Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications explores the different possibilities of providing AI based neuro-rehabilitation methods to treat neurological disorders. This book provides in-depth knowledge on the challenges and solutions associated with the different varieties of neuro-rehabilitation through the inclusion of case studies and real-time scenarios in different geographical locations. Beginning with an overview of neuro-rehabilitation applications, the book discusses the role of machine learning methods in brain function grading for adults with Mild Cognitive Impairment, Brain Computer Interface for post-stroke patients, developing assistive devices for paralytic patients, and cognitive treatment for spinal cord injuries. Topics also include AI-based video games to improve the brain performances in children with autism and ADHD, deep learning approaches and magnetoencephalography data for limb movement, EEG signal analysis, smart sensors, and the application of robotic concepts for gait control. Incorporates artificial intelligence techniques into neuro-rehabilitation and presents novel ideas for this process Provides in-depth case studies and state-of-the-art methods, along with the experimental study Presents a block diagram based complete set-up in each chapter to help in real-time implementation

Computational Intelligence for Machine Learning and Healthcare Informatics

Rajshree Srivastava,Pradeep Kumar Mallick,Siddharth Swarup Rautaray,Manjusha Pandey
Computational Intelligence for Machine Learning and Healthcare Informatics Book PDF

Author : Rajshree Srivastava,Pradeep Kumar Mallick,Siddharth Swarup Rautaray,Manjusha Pandey
Publisher : Walter de Gruyter GmbH & Co KG
Page : 414 pages
File Size : 55,8 Mb
Release : 2020-06-22
Category : Computers
ISBN : 9783110649277

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Computational Intelligence for Machine Learning and Healthcare Informatics by Rajshree Srivastava,Pradeep Kumar Mallick,Siddharth Swarup Rautaray,Manjusha Pandey Pdf

This book presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It is intended to provide a unique compendium of current and emerging machine learning paradigms for healthcare informatics, reflecting the diversity, complexity, and depth and breadth of this multi-disciplinary area.

Advances in Neural Networks

Simone Bassis,Anna Esposito,Francesco Carlo Morabito,Eros Pasero
Advances in Neural Networks Book PDF

Author : Simone Bassis,Anna Esposito,Francesco Carlo Morabito,Eros Pasero
Publisher : Springer
Page : 539 pages
File Size : 40,9 Mb
Release : 2016-06-18
Category : Technology & Engineering
ISBN : 9783319337470

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Advances in Neural Networks by Simone Bassis,Anna Esposito,Francesco Carlo Morabito,Eros Pasero Pdf

This carefully edited book is putting emphasis on computational and artificial intelligent methods for learning and their relative applications in robotics, embedded systems, and ICT interfaces for psychological and neurological diseases. The book is a follow-up of the scientific workshop on Neural Networks (WIRN 2015) held in Vietri sul Mare, Italy, from the 20th to the 22nd of May 2015. The workshop, at its 27th edition became a traditional scientific event that brought together scientists from many countries, and several scientific disciplines. Each chapter is an extended version of the original contribution presented at the workshop, and together with the reviewers’ peer revisions it also benefits from the live discussion during the presentation. The content of book is organized in the following sections. 1. Introduction, 2. Machine Learning, 3. Artificial Neural Networks: Algorithms and models, 4. Intelligent Cyberphysical and Embedded System, 5. Computational Intelligence Methods for Biomedical ICT in Neurological Diseases, 6. Neural Networks-Based Approaches to Industrial Processes, 7. Reconfigurable Modular Adaptive Smart Robotic Systems for Optoelectronics Industry: The White'R Instantiation This book is unique in proposing a holistic and multidisciplinary approach to implement autonomous, and complex Human Computer Interfaces.

Trends in Deep Learning Methodologies

Vincenzo Piuri,Sandeep Raj,Angelo Genovese,Rajshree Srivastava
Trends in Deep Learning Methodologies Book PDF

Author : Vincenzo Piuri,Sandeep Raj,Angelo Genovese,Rajshree Srivastava
Publisher : Academic Press
Page : 308 pages
File Size : 42,8 Mb
Release : 2020-11-12
Category : Computers
ISBN : 9780128232682

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Trends in Deep Learning Methodologies by Vincenzo Piuri,Sandeep Raj,Angelo Genovese,Rajshree Srivastava Pdf

Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models. Provides insights into the theory, algorithms, implementation and the application of deep learning techniques Covers a wide range of applications of deep learning across smart healthcare and smart engineering Investigates the development of new models and how they can be exploited to find appropriate solutions

Artificial Intelligence in the Age of Neural Networks and Brain Computing

Robert Kozma,Cesare Alippi,Yoonsuck Choe,Francesco Carlo Morabito
Artificial Intelligence in the Age of Neural Networks and Brain Computing Book PDF

Author : Robert Kozma,Cesare Alippi,Yoonsuck Choe,Francesco Carlo Morabito
Publisher : Academic Press
Page : 398 pages
File Size : 53,7 Mb
Release : 2023-10-27
Category : Computers
ISBN : 9780323958165

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Artificial Intelligence in the Age of Neural Networks and Brain Computing by Robert Kozma,Cesare Alippi,Yoonsuck Choe,Francesco Carlo Morabito Pdf

Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making Edited by high-level academics and researchers in intelligent systems and neural networks Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks

Deep Learning and Other Soft Computing Techniques

Nguyen Hoang Phuong,Vladik Kreinovich
Deep Learning and Other Soft Computing Techniques Book PDF

Author : Nguyen Hoang Phuong,Vladik Kreinovich
Publisher : Springer Nature
Page : 282 pages
File Size : 47,9 Mb
Release : 2023-06-26
Category : Technology & Engineering
ISBN : 9783031294471

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Deep Learning and Other Soft Computing Techniques by Nguyen Hoang Phuong,Vladik Kreinovich Pdf

This book focuses on the use of artificial intelligence (AI) and computational intelligence (CI) in medical and related applications. Applications include all aspects of medicine: from diagnostics (including analysis of medical images and medical data) to therapeutics (including drug design and radiotherapy) to epidemic- and pandemic-related public health policies. Corresponding techniques include machine learning (especially deep learning), techniques for processing expert knowledge (e.g., fuzzy techniques), and advanced techniques of applied mathematics (such as innovative probabilistic and graph-based techniques). The book also shows that these techniques can be used in many other applications areas, such as finance, transportation, physics. This book helps practitioners and researchers to learn more about AI and CI methods and their biomedical (and related) applications—and to further develop this important research direction.

Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications

Alonso, Eduardo,Mondrag¢n, Esther
Computational Neuroscience for Advancing Artificial Intelligence Models Methods and Applications Book PDF

Author : Alonso, Eduardo,Mondrag¢n, Esther
Publisher : IGI Global
Page : 396 pages
File Size : 50,9 Mb
Release : 2010-11-30
Category : Computers
ISBN : 9781609600235

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Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications by Alonso, Eduardo,Mondrag¢n, Esther Pdf

"This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro-psychological models and computational models"--

Computational Intelligence and Its Applications in Healthcare

Jitendra Kumar Verma,Sudip Paul,Prashant Johri
Computational Intelligence and Its Applications in Healthcare Book PDF

Author : Jitendra Kumar Verma,Sudip Paul,Prashant Johri
Publisher : Academic Press
Page : 258 pages
File Size : 43,5 Mb
Release : 2020-08-01
Category : Science
ISBN : 9780128206195

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Computational Intelligence and Its Applications in Healthcare by Jitendra Kumar Verma,Sudip Paul,Prashant Johri Pdf

Computational Intelligence and Its Applications in Healthcare presents rapidly growing applications of computational intelligence for healthcare systems, including intelligent synthetic characters, man-machine interface, menu generators, user acceptance analysis, pictures archiving, and communication systems. Computational intelligence is the study of the design of intelligent agents, which are systems that act intelligently: they do what they think are appropriate for their circumstances and goals; they're flexible to changing environments and goals; they learn from experience; and they make appropriate choices given perceptual limitations and finite computation. Computational intelligence paradigms offer many advantages in maintaining and enhancing the field of healthcare. Provides coverage of fuzzy logic, neural networks, evolutionary computation, learning theory, probabilistic methods, telemedicine, and robotics applications Includes coverage of artificial intelligence and biological applications, soft computing, image and signal processing, and genetic algorithms Presents the latest developments in computational methods in healthcare Bridges the gap between obsolete literature and current literature

Artificial Intelligence for Neurological Disorders

Ajith Abraham,Sujata Dash,Subhendu Kumar Pani,Laura García-Hernández
Artificial Intelligence for Neurological Disorders Book PDF

Author : Ajith Abraham,Sujata Dash,Subhendu Kumar Pani,Laura García-Hernández
Publisher : Academic Press
Page : 434 pages
File Size : 40,8 Mb
Release : 2022-09-23
Category : Medical
ISBN : 9780323902786

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Artificial Intelligence for Neurological Disorders by Ajith Abraham,Sujata Dash,Subhendu Kumar Pani,Laura García-Hernández Pdf

Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation. The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances. Discusses various AI and ML methods to apply for neurological research Explores Deep Learning techniques for brain MRI images Covers AI techniques for the early detection of neurological diseases and seizure prediction Examines cognitive therapies using AI and Deep Learning methods

Deep Learning for Medical Decision Support Systems

Utku Kose,Omer Deperlioglu,Jafar Alzubi,Bogdan Patrut
Deep Learning for Medical Decision Support Systems Book PDF

Author : Utku Kose,Omer Deperlioglu,Jafar Alzubi,Bogdan Patrut
Publisher : Springer Nature
Page : 185 pages
File Size : 51,6 Mb
Release : 2020-06-17
Category : Technology & Engineering
ISBN : 9789811563256

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Deep Learning for Medical Decision Support Systems by Utku Kose,Omer Deperlioglu,Jafar Alzubi,Bogdan Patrut Pdf

This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today’s problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation.

Computational Intelligence for Oncology and Neurological Disorders

Mrutyunjaya Panda,Ajith Abraham,Biju Gopi,Reuel Ajith
Computational Intelligence for Oncology and Neurological Disorders Book PDF

Author : Mrutyunjaya Panda,Ajith Abraham,Biju Gopi,Reuel Ajith
Publisher : CRC Press
Page : 292 pages
File Size : 48,5 Mb
Release : 2024-07-15
Category : Computers
ISBN : 9781040085622

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Computational Intelligence for Oncology and Neurological Disorders by Mrutyunjaya Panda,Ajith Abraham,Biju Gopi,Reuel Ajith Pdf

With the advent of computational intelligence-based approaches, such as bio-inspired techniques, and the availability of clinical data from various complex experiments, medical consultants, researchers, neurologists, and oncologists, there is huge scope for CI-based applications in medical oncology and neurological disorders. This book focuses on interdisciplinary research in this field, bringing together medical practitioners dealing with neurological disorders and medical oncology along with CI investigators. The book collects high-quality original contributions, containing the latest developments or applications of practical use and value, presenting interdisciplinary research and review articles in the field of intelligent systems for computational oncology and neurological disorders. Drawing from work across computer science, physics, mathematics, medical science, psychology, cognitive science, oncology, and neurobiology among others, it combines theoretical, applied, computational, experimental, and clinical research. It will be of great interest to any neurology or oncology researchers focused on computational approaches.

Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence

Anitha S. Pillai,Bindu Menon
Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence Book PDF

Author : Anitha S. Pillai,Bindu Menon
Publisher : Academic Press
Page : 356 pages
File Size : 44,7 Mb
Release : 2022-02-23
Category : Science
ISBN : 9780323886260

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Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence by Anitha S. Pillai,Bindu Menon Pdf

Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence focuses on how the neurosciences can benefit from advances in AI, especially in areas such as medical image analysis for the improved diagnosis of Alzheimer’s disease, early detection of acute neurologic events, prediction of stroke, medical image segmentation for quantitative evaluation of neuroanatomy and vasculature, diagnosis of Alzheimer’s Disease, autism spectrum disorder, and other key neurological disorders. Chapters also focus on how AI can help in predicting stroke recovery, and the use of Machine Learning and AI in personalizing stroke rehabilitation therapy. Other sections delve into Epilepsy and the use of Machine Learning techniques to detect epileptogenic lesions on MRIs and how to understand neural networks. Provides readers with an understanding on the key applications of artificial intelligence and machine learning in the diagnosis and treatment of the most important neurological disorders Integrates recent advancements of artificial intelligence and machine learning to the evaluation of large amounts of clinical data for the early detection of disorders such as Alzheimer’s Disease, autism spectrum disorder, Multiple Sclerosis, headache disorder, Epilepsy, and stroke Provides readers with illustrative examples of how artificial intelligence can be applied to outcome prediction, neurorehabilitation and clinical exams, including a wide range of case studies in predicting and classifying neurological disorders

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications

Om Prakash Jena,Bharat Bhushan,Utku Kose
Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications Book PDF

Author : Om Prakash Jena,Bharat Bhushan,Utku Kose
Publisher : CRC Press
Page : 332 pages
File Size : 46,9 Mb
Release : 2022-02-25
Category : Computers
ISBN : 9781000533972

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Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications by Om Prakash Jena,Bharat Bhushan,Utku Kose Pdf

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features: Covers the fundamentals of ML and DL in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in ML/DL models Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios. Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India. Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.

Machine Learning and Deep Learning Techniques for Medical Science

K. Gayathri Devi,Kishore Balasubramanian,Le Anh Ngoc
Machine Learning and Deep Learning Techniques for Medical Science Book PDF

Author : K. Gayathri Devi,Kishore Balasubramanian,Le Anh Ngoc
Publisher : CRC Press
Page : 351 pages
File Size : 54,8 Mb
Release : 2022-05-11
Category : Technology & Engineering
ISBN : 9781000583366

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Machine Learning and Deep Learning Techniques for Medical Science by K. Gayathri Devi,Kishore Balasubramanian,Le Anh Ngoc Pdf

The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing the conditions with accurate results. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis. The contributors explore the recent trends, innovations, challenges, and solutions, as well as case studies of the applications of ML and DL in intelligent system-based disease diagnosis. The chapters also highlight the basics and the need for applying mathematical aspects with reference to the development of new medical models. Authors also explore ML and DL in relation to artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, and pattern recognition approaches to functional magnetic resonance imaging images. This book is for students and researchers of computer science and engineering, electronics and communication engineering, and information technology; for biomedical engineering researchers, academicians, and educators; and for students and professionals in other areas of the healthcare sector. Presents key aspects in the development and the implementation of ML and DL approaches toward developing prediction tools, models, and improving medical diagnosis Discusses the recent trends, innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis Examines DL theories, models, and tools to enhance health information systems Explores ML and DL in relation to AI prediction tools, discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities Dr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamil Nadu, India. Dr. Kishore Balasubramanian is an Assistant Professor (Senior Scale) at the Department of EEE at Dr. Mahalingam College of Engineering & Technology, Tamil Nadu, India. Dr. Le Anh Ngoc is a Director of Swinburne Innovation Space and Professor in Swinburne University of Technology (Vietnam).

Brain-Computer Interface

M.G. Sumithra,Rajesh Kumar Dhanaraj,Mariofanna Milanova,Balamurugan Balusamy,Chandran Venkatesan
Brain Computer Interface Book PDF

Author : M.G. Sumithra,Rajesh Kumar Dhanaraj,Mariofanna Milanova,Balamurugan Balusamy,Chandran Venkatesan
Publisher : John Wiley & Sons
Page : 325 pages
File Size : 55,9 Mb
Release : 2023-02-10
Category : Computers
ISBN : 9781119857754

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Brain-Computer Interface by M.G. Sumithra,Rajesh Kumar Dhanaraj,Mariofanna Milanova,Balamurugan Balusamy,Chandran Venkatesan Pdf

BRAIN-COMPUTER INTERFACE It covers all the research prospects and recent advancements in the brain-computer interface using deep learning. The brain-computer interface (BCI) is an emerging technology that is developing to be more functional in practice. The aim is to establish, through experiences with electronic devices, a communication channel bridging the human neural networks within the brain to the external world. For example, creating communication or control applications for locked-in patients who have no control over their bodies will be one such use. Recently, from communication to marketing, recovery, care, mental state monitoring, and entertainment, the possible application areas have been expanding. Machine learning algorithms have advanced BCI technology in the last few decades, and in the sense of classification accuracy, performance standards have been greatly improved. For BCI to be effective in the real world, however, some problems remain to be solved. Research focusing on deep learning is anticipated to bring solutions in this regard. Deep learning has been applied in various fields such as computer vision and natural language processing, along with BCI growth, outperforming conventional approaches to machine learning. As a result, a significant number of researchers have shown interest in deep learning in engineering, technology, and other industries; convolutional neural network (CNN), recurrent neural network (RNN), and generative adversarial network (GAN). Audience Researchers and industrialists working in brain-computer interface, deep learning, machine learning, medical image processing, data scientists and analysts, machine learning engineers, electrical engineering, and information technologists.