Brain-Computer Interface: Using Deep Learning Appl ications
Using Deep Learning Applications
1st Edition
Published on 20. August 2023
Book
Hardback
320 pages
978-1-119-85720-4 (ISBN)
Description
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.
It is anticipated that research focusing on new development that would 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).
It is anticipated that research focusing on new development that would 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).
More details
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Dimensions
Height: 236 mm
Width: 158 mm
Thickness: 22 mm
Weight
658 gr
ISBN-13
978-1-119-85720-4 (9781119857204)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
M. G. Sumithra | Rajesh Kumar Dhanaraj | Mariofanna Milanova
Brain-Computer Interface
Using Deep Learning Applications
E-Book
02/2023
1st Edition
Wiley
€181.99
Available for download
M. G. Sumithra | Rajesh Kumar Dhanaraj | Mariofanna Milanova
Brain-Computer Interface
Using Deep Learning Applications
E-Book
01/2023
1st Edition
Wiley
€181.99
Available for download
Person
M. G. Sumithra, PhD, is a professor at Anna University Chennai, India. With 25 years of teaching experience, she has published 66 technical papers in refereed journals, 3 book chapters and 129 research papers in national and international conferences. She is a Nvidia Deep Learning Institute Certified Instructor for "Computer Vision".
Rajesh Kumar Dhanaraj, PhD, is a Professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He has contributed around 25 authored and edited books on various technologies, 17 patents, and more than 40 articles and papers in various refereed journals and international conferences. He is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).
Mariofanna Milanova, PhD, is a professor in the Department of Computer Science at University of Arkansas, Little Rock. She is an IEEE Senior Member and Nvidia's Deep Learning Institute University Ambassador. She has published more than 120 publications, over 53 journal papers, 35 book chapters, and numerous conference papers. She also has two patents.
Balamurugan Balusamy, PhD, is a professor in the School of Computing Science and Engineering, Galgotias University, Greater Noida, India. He is a Pioneer Researcher in the areas of big data and the IoT and has published more than 70 articles in various top international journals.
V. Chandranholds an M.E degree in VLSI Design at Government College of Technology, Coimbatore and is a Nvidia Certified Instructor for Deep learning for Computer Vison.
Rajesh Kumar Dhanaraj, PhD, is a Professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He has contributed around 25 authored and edited books on various technologies, 17 patents, and more than 40 articles and papers in various refereed journals and international conferences. He is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).
Mariofanna Milanova, PhD, is a professor in the Department of Computer Science at University of Arkansas, Little Rock. She is an IEEE Senior Member and Nvidia's Deep Learning Institute University Ambassador. She has published more than 120 publications, over 53 journal papers, 35 book chapters, and numerous conference papers. She also has two patents.
Balamurugan Balusamy, PhD, is a professor in the School of Computing Science and Engineering, Galgotias University, Greater Noida, India. He is a Pioneer Researcher in the areas of big data and the IoT and has published more than 70 articles in various top international journals.
V. Chandranholds an M.E degree in VLSI Design at Government College of Technology, Coimbatore and is a Nvidia Certified Instructor for Deep learning for Computer Vison.