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Synergies of Neural Networks, Neurorobotics, and Brain-Computer Interface Technology: Advancements and Applications

Submission Deadline: June 30, 2024

Guest Editors

Prof. Dr. Mustafa M Matalgah
University of Mississippi, United States
 
Dr. Sathishkumar Karupusamy
Gobi Arts & Science College, Tamilnadu, India
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Dr. Bilal Alhayani
Yildiz Technical University. Istanbul. Turkey
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Special Issue Information

This special issue is dedicated to exploring the dynamic intersection of Neural Networks, Neurorobotics, and Brain-Computer Interface (BCI) technology. This issue aims to provide an in-depth technical platform for researchers and practitioners to share their latest findings, methodologies, and applications related to the symbiotic evolution of these fields. The special issue seeks to delve into the integration of neural networks, neurorobotics, and BCIs, unraveling their potential for enhancing human-machine interactions, healthcare, assistive technologies, and beyond.
 
Topics of Interest: Contributions to this special issue should address, but are not limited to, the following technically oriented subjects:
Bio-Inspired Neural Architectures: Novel neural network models and architectures inspired by the structure and functioning of the human brain, advancing learning and adaptability in AI systems.
Neurorobotics for Autonomous Systems: Innovations in neurorobotics that empower robots to perceive, interact with, and navigate the environment autonomously through bio-inspired algorithms.
BCI-Driven Prosthetics and Assistive Devices: Applications of BCI technology in developing advanced prosthetics, exoskeletons, and assistive devices that restore mobility and functionality for individuals with motor impairments.
Cognitive Neuroscience and AI Fusion: Exploration of the synergy between cognitive neuroscience findings and AI techniques, unraveling insights into human cognition and enhancing AI system performance.
Neuro feedback and Neuro rehabilitation: Utilizing BCI-enabled neurofeedback for cognitive training, neurorehabilitation, and treating neurological disorders.
Ethical and Privacy Considerations: Addressing the ethical implications of neural network technologies, neurorobotics, and BCIs, including privacy, security, and neuroethics.
Neural Control of Robotic Systems: Investigating neural control paradigms for real-time interaction and control of robotic systems, enabling intuitive and natural human-machine interfaces.
BCI-Enhanced Virtual and Augmented Reality: Integration of BCI technology with virtual and augmented reality environments for immersive experiences and therapeutic applications.
Neural Interfaces and Implants: Advancements in neural interface technologies, including brain implants and neurostimulation techniques, for seamless integration with AI systems and robots.
Neural Networks in Brain Signal Analysis: Leveraging neural networks for processing and analyzing brain signals, enhancing the accuracy and speed of BCI systems.

Manuscript Submission Information

Authors can submit their manuscripts through the Online Submission System. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to guest editors for perusal first.
 
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere. All manuscripts are thoroughly refereed through a blind peer-review process. A guide for authors and other relevant information for the submission of manuscripts is available on the Author Submission Guide page.
 
The Article Processing Charge for this special issue is 500 USD. Submitted papers should be well formatted and use good English. 

Published Papers

Classifying Alzheimer’s Disease Phases from sMRI Data Using an Adaptive Clonal Selection Approach
Mathews Emmanuel* and J. Jabez

Improving Tomato Disease Classification Using BR-TomatoCNN: An Efficient Model Utilizing Bottleneck Residuals
U. Shruthi*, V. Nagaveni, and Sunil G. L.

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