Deep Learning For Eeg Based Brain Computer Interfaces Representations Algorithms And Applications

Author : Xiang Zhang,Lina Yao
Publisher : World Scientific
Page : 294 pages
File Size : 53,5 Mb
Release : 2021-09-14
Category : Computers
ISBN : 9781786349606

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Deep Learning For Eeg-based Brain-computer Interfaces: Representations, Algorithms And Applications by Xiang Zhang,Lina Yao Pdf

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)

Author : M.G. Sumithra,Rajesh Kumar Dhanaraj,Mariofanna Milanova,Balamurugan Balusamy,Chandran Venkatesan
Publisher : John Wiley & Sons
Page : 325 pages
File Size : 51,5 Mb
Release : 2023-03-14
Category : Computers
ISBN : 9781119857204

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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.

Author : Minkyu Ahn,Hong Gi Yeom,Hohyun Cho,Sung Chan Jun
Publisher : Frontiers Media SA
Page : 147 pages
File Size : 46,8 Mb
Release : 2022-06-06
Category : Science
ISBN : 9782889763283

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Deep Learning in Brain-Computer Interface by Minkyu Ahn,Hong Gi Yeom,Hohyun Cho,Sung Chan Jun Pdf

Author : Toshihisa Tanaka,Mahnaz Arvaneh
Publisher : Institution of Engineering and Technology
Page : 355 pages
File Size : 47,7 Mb
Release : 2018-09
Category : Technology & Engineering
ISBN : 9781785613982

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Signal Processing and Machine Learning for Brain-Machine Interfaces by Toshihisa Tanaka,Mahnaz Arvaneh Pdf

This book introduces signal processing and machine learning techniques for Brain Machine Interfacing/Brain Computer Interfacing (BMI/BCI), and their practical and future applications in neuroscience, medicine, and rehabilitation. This is an emerging and challenging technology in engineering, computing, machine learning, neuroscience and medicine, and so the book will interest researchers, engineers, professionals and specialists from all of these areas who need to know more about cutting edge technologies in the fields.

Author : Toshihisa Tanaka (Engineer),Mahnaz Arvaneh
Publisher : Unknown
Page : 128 pages
File Size : 49,7 Mb
Release : 2018
Category : COMPUTERS
ISBN : 1523119837

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Signal Processing and Machine Learning for Brain-machine Interfaces by Toshihisa Tanaka (Engineer),Mahnaz Arvaneh Pdf

Brain-machine interfacing or brain-computer interfacing (BMI/BCI) is an emerging and challenging technology used in engineering and neuroscience. The ultimate goal is to provide a pathway from the brain to the external world via mapping, assisting, augmenting or repairing human cognitive or sensory-motor functions. In this book an international panel of experts introduce signal processing and machine learning techniques for BMI/BCI and outline their practical and future applications in neuroscience, medicine, and rehabilitation, with a focus on EEG-based BMI/BCI methods and technologies. Topics covered include discriminative learning of connectivity pattern of EEG; feature extraction from EEG recordings; EEG signal processing; transfer learning algorithms in BCI; convolutional neural networks for event-related potential detection; spatial filtering techniques for improving individual template-based SSVEP detection; feature extraction and classification algorithms for image RSVP based BCI; decoding music perception and imagination using deep learning techniques; neurofeedback games using EEG-based Brain-Computer Interface Technology; affective computing system and more.

Author : Abdulmotaleb El Saddik,M. Shamim Hossain,Burak Kantarci
Publisher : Springer Nature
Page : 254 pages
File Size : 40,5 Mb
Release : 2019-12-03
Category : Medical
ISBN : 9783030278441

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Connected Health in Smart Cities by Abdulmotaleb El Saddik,M. Shamim Hossain,Burak Kantarci Pdf

This book reports on the theoretical foundations, fundamental applications and latest advances in various aspects of connected services for health information systems. The twelve chapters highlight state-of-the-art approaches, methodologies and systems for the design, development, deployment and innovative use of multisensory systems and tools for health management in smart city ecosystems. They exploit technologies like deep learning, artificial intelligence, augmented and virtual reality, cyber physical systems and sensor networks. Presenting the latest developments, identifying remaining challenges, and outlining future research directions for sensing, computing, communications and security aspects of connected health systems, the book will mainly appeal to academic and industrial researchers in the areas of health information systems, smart cities, and augmented reality.

Author : Aboul Ella Hassanien,Ahmad Taher Azar
Publisher : Springer
Page : 422 pages
File Size : 48,9 Mb
Release : 2014-11-01
Category : Technology & Engineering
ISBN : 9783319109787

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Brain-Computer Interfaces by Aboul Ella Hassanien,Ahmad Taher Azar Pdf

The success of a BCI system depends as much on the system itself as on the user’s ability to produce distinctive EEG activity. BCI systems can be divided into two groups according to the placement of the electrodes used to detect and measure neurons firing in the brain. These groups are: invasive systems, electrodes are inserted directly into the cortex are used for single cell or multi unit recording, and electrocorticography (EcoG), electrodes are placed on the surface of the cortex (or dura); noninvasive systems, they are placed on the scalp and use electroencephalography (EEG) or magnetoencephalography (MEG) to detect neuron activity. The book is basically divided into three parts. The first part of the book covers the basic concepts and overviews of Brain Computer Interface. The second part describes new theoretical developments of BCI systems. The third part covers views on real applications of BCI systems.

Author : Szczepan Paszkiel
Publisher : Springer Nature
Page : 132 pages
File Size : 40,6 Mb
Release : 2019-08-31
Category : Technology & Engineering
ISBN : 9783030305819

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Analysis and Classification of EEG Signals for Brain–Computer Interfaces by Szczepan Paszkiel Pdf

This book addresses the problem of EEG signal analysis and the need to classify it for practical use in many sample implementations of brain–computer interfaces. In addition, it offers a wealth of information, ranging from the description of data acquisition methods in the field of human brain work, to the use of Moore–Penrose pseudo inversion to reconstruct the EEG signal and the LORETA method to locate sources of EEG signal generation for the needs of BCI technology. In turn, the book explores the use of neural networks for the classification of changes in the EEG signal based on facial expressions. Further topics touch on machine learning, deep learning, and neural networks. The book also includes dedicated implementation chapters on the use of brain–computer technology in the field of mobile robot control based on Python and the LabVIEW environment. In closing, it discusses the problem of the correlation between brain–computer technology and virtual reality technology.

Author : Nitish V. Thakor
Publisher : Springer Nature
Page : 3686 pages
File Size : 47,8 Mb
Release : 2023-02-02
Category : Technology & Engineering
ISBN : 9789811655401

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Handbook of Neuroengineering by Nitish V. Thakor Pdf

This Handbook serves as an authoritative reference book in the field of Neuroengineering. Neuroengineering is a very exciting field that is rapidly getting established as core subject matter for research and education. The Neuroengineering field has also produced an impressive array of industry products and clinical applications. It also serves as a reference book for graduate students, research scholars and teachers. Selected sections or a compendium of chapters may be used as “reference book” for a one or two semester graduate course in Biomedical Engineering. Some academicians will construct a “textbook” out of selected sections or chapters. The Handbook is also meant as a state-of-the-art volume for researchers. Due to its comprehensive coverage, researchers in one field covered by a certain section of the Handbook would find other sections valuable sources of cross-reference for information and fertilization of interdisciplinary ideas. Industry researchers as well as clinicians using neurotechnologies will find the Handbook a single source for foundation and state-of-the-art applications in the field of Neuroengineering. Regulatory agencies, entrepreneurs, investors and legal experts can use the Handbook as a reference for their professional work as well.​

Author : Jean-Jacques Rousseau,Bill Kapralos
Publisher : Springer Nature
Page : 723 pages
File Size : 49,6 Mb
Release : 2023-07-29
Category : Computers
ISBN : 9783031376603

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Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges by Jean-Jacques Rousseau,Bill Kapralos Pdf

This 4-volumes set constitutes the proceedings of the ICPR 2022 Workshops of the 26th International Conference on Pattern Recognition Workshops, ICPR 2022, Montreal, QC, Canada, August 2023. The 167 full papers presented in these 4 volumes were carefully reviewed and selected from numerous submissions. ICPR workshops covered domains related to pattern recognition, artificial intelligence, computer vision, image and sound analysis. Workshops’ contributions reflected the most recent applications related to healthcare, biometrics, ethics, multimodality, cultural heritage, imagery, affective computing, etc.

Author : Juri Fedjaev
Publisher : GRIN Verlag
Page : 67 pages
File Size : 54,6 Mb
Release : 2019-01-14
Category : Technology & Engineering
ISBN : 9783668865020

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Decoding EEG Brain Signals using Recurrent Neural Networks by Juri Fedjaev Pdf

Master's Thesis from the year 2017 in the subject Electrotechnology, grade: 1,0, Technical University of Munich (Neurowissenschaftliche Systemtheorie), language: English, abstract: Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable direct communication between humans and computers by analyzing brain activity. Specifically, modern BCIs are capable of translating imagined movements into real-life control signals, e.g., to actuate a robotic arm or prosthesis. This type of BCI is already used in rehabilitation robotics and provides an alternative communication channel for patients suffering from amyotrophic lateral sclerosis or severe spinal cord injury. Current state-of-the-art methods are based on traditional machine learning, which involves the identification of discriminative features. This is a challenging task due to the non-linear, non-stationary and time-varying characteristics of EEG signals, which led to stagnating progress in classification performance. Deep learning alleviates the efforts for manual feature engineering through end-to-end decoding, which potentially presents a promising solution for EEG signal classification. This thesis investigates how deep learning models such as long short-term memory (LSTM) and convolutional neural networks (CNN) perform on the task of decoding motor imagery movements from EEG signals. For this task, both a LSTM and a CNN model are developed using the latest advances in deep learning, such as batch normalization, dropout and cropped training strategies for data augmentation. Evaluation is performed on a novel EEG dataset consisting of 20 healthy subjects. The LSTM model reaches the state-of-the-art performance of support vector ma- chines with a cross-validated accuracy of 66.20%. The CNN model that employs a time-frequency transformation in its first layer outperforms the LSTM model and reaches a mean accuracy of 84.23%. This shows that deep learning approaches deliver competitive performance without the need for hand-crafted features, enabling end-to-end classification.

Author : Siuly Siuly,Yan Li,Yanchun Zhang
Publisher : Springer
Page : 256 pages
File Size : 54,8 Mb
Release : 2017-01-03
Category : Technology & Engineering
ISBN : 9783319476537

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EEG Signal Analysis and Classification by Siuly Siuly,Yan Li,Yanchun Zhang Pdf

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

Author : Yizhang Jiang,Yu-Dong Zhang,Mohammad Khosravi
Publisher : Frontiers Media SA
Page : 239 pages
File Size : 43,6 Mb
Release : 2021-10-13
Category : Science
ISBN : 9782889714681

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Advanced Deep-Transfer-Leveraged Studies on Brain-Computer Interfacing by Yizhang Jiang,Yu-Dong Zhang,Mohammad Khosravi Pdf

Author : Li Hu,Zhiguo Zhang
Publisher : Springer Nature
Page : 437 pages
File Size : 45,8 Mb
Release : 2019-10-12
Category : Medical
ISBN : 9789811391132

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EEG Signal Processing and Feature Extraction by Li Hu,Zhiguo Zhang Pdf

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.

Author : Amir Shmuel,Albert Yang,Yogesh Rathi,Hyunjin Park
Publisher : Frontiers Media SA
Page : 151 pages
File Size : 44,6 Mb
Release : 2023-02-07
Category : Science
ISBN : 9782832513804

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Deep learning techniques and their applications to the healthy and disordered brain - during development through adulthood and beyond by Amir Shmuel,Albert Yang,Yogesh Rathi,Hyunjin Park Pdf