Wiley-BCI-2021: Brain-Computer Interface: Using Deep Learning Applications |
Submission link | https://easychair.org/conferences/?conf=wileybci2021 |
Poster | download |
Abstract registration deadline | March 15, 2021 |
Submission deadline | June 30, 2021 |
Call for Book Chapter - Scrivener Publishing - WILEY
Brain-Computer Interface: Using Deep Learning Applications
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.These issues include, among others, output inconsistency, long calibration time and low reliability levels. 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). In the BCI region, researchers are trying to complement BCI with deep learning algorithms. However as brain impulses are high-dimensional, chaotic, and very non-stationary, there are some restrictions. Additionally, relative to image data in computer vision fields, datasets are greatly reduced. Therefore more research based on deep learning as BCI applications and a detailed assessment of how this technology can be used to incorporate the use of the interface in practice would be helpful. The main purpose of this book is to create a platform for debate, putting together the efforts of researchers to make progress in BCIs based on deep learning. Opinions/survey data on the practical problems of applying deep learning to BCI are also welcome.
List of Topics
- Introduction: Brain computer interface, Deep Learning
- Hard-coded features in the brain and DNN
- Resting state fMRI - large data analytics in Neuroimaging
- Transfer learning for BCI with minimum calibration
- Data augmentation for the limited training dataset
- Signal Processing and Machine Learning for Brain-Machine Interfaces
- Data preprocessing of Electroencephalography
- Statistical learning for Brain computer Interface
- Feature extracting techniques for brain signals.
- Role of Brain-Computer Interface in health informatics
- Limitations and Critical issues in applying deep learning to BCI
- Analysis of electroencephalography Deep learning algorithms.
- Brain–Computer Interfaces for Human Augmentation
- Non-EEG-based human–computer interface
- Improving the quality of elderly living with Brain-Computer Interface
Editors
- Dr. Sumithra Manimegalai Govindan, KPR Institute of Engineering and Technology, Coimbatore, India.
- Dr.Rajesh Kumar Dhanaraj, Galgotias University, India.
- Dr.Mariofanna Milanova, University of Arkansas, United States.
- Dr.Balamurugan Balusamy, Galgotias University, India.
- Prof.Chandran Venkatesan, KPR Institute of Engineering and Technology, Coimbatore, India.
Submission Guidelines
- Abstract Submission (Approx. 500 words): 15 March 2021
- Abstract Acceptance notification: 25 March 2021
- Full Chapter Submission: 30 May 2021
- Chapter Acceptance: 30 June 2021
- Final Chapter Submission (in words): 30 Nov 2021
The minimum length of the chapter should not be less than 20-25 pages (7,000 to 10,000 words). The full chapter includes Title of paper, Authors Name, Authors Affiliations, email ID, Corresponding author details, department, Authors Bio, etc. with (Font Size 12, Font Style: Times New Roman, Line Space: 1 Point, Headings: 12+Bold) with APA reference style. Figure resolution needs to be 600 dpi at the size it will be used. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.
Publication
The purpose of Scrivener Publishing is to publish books and journals in the technology applied sciences for both the practitioner in the industry (engineer and technician) and the researcher in academia. This high- quality content is essential to our professional customers and is sold globally as print and electronic as well as in aggregated databases. We believe our content helps businesses and industries achieve cost efficiencies and higher productivity. We also believe that our new journal titles will be unique and important resources for both the practitioner and researcher in the fields represented. Finally, by partnering with Wiley, the leading engineering publisher, on our books through our joint imprint, Wiley- Scrivener, Scrivener Publishing will offer our authors global marketing, sales, and distribution both in print and digital. This publication is anticipated to be released in December 2021.
The book will be indexed by Scopus.
Contact
All questions about submissions should be emailed to: bcidl2021@gmail.com (Mobile Number: +91-9790827719)