Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Neuro-Robotics Systems: Sensing, Cognition, Learning, and Control
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Neuro-Robotics Systems: Sensing, Cognition, Learning, and Control

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 4941

Special Issue Editors

Dr. Diego R. Faria

E-Mail Website
Guest Editor
Reader in Robotics and Intelligent Adaptive Systems, Department of Computer Science, School of Physics, Engineering and Computer Science, University of Hertfordshire, Hertfordshire AL10 9AB, UK
Interests: human–robot interaction; grasping and dexterous manipulation; artificial perception systems/autonomous systems; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Carfì

E-Mail Website
Guest Editor
Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, 16145 Genova, Italy
Interests: robotics; human robot interaction; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Timothy Patten

E-Mail Website
Guest Editor
School of Mechanical and Mechatronic Engineering, Robotics Institute, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: robotics; vision; planning; active perception
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Markus Vincze

E-Mail Website
Guest Editor
Automation and Control Institute, Vienna University of Technology, Gusshausstrasse 27-29 / E376, 1040 Vienna, Austria
Interests: robot vision; service robots; object detection; scene understanding; robots at home
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neuro-robotics systems play an important role as technology advances to promote human wellbeing and quality of life. By combining studies of neuroscience, robotics, and artificial intelligence, new models, applications, and hardware are being designed to promote the fusion of these fields towards human-centred research and real-life applications to assist humans. Considering the field of cognitive robotics, intelligent behaviour is the main aspect pursued, given a processing architecture, so learning and reasoning about how to behave in response to complex goals in a complex world full of uncertainties must be designed. Neuro-robotics systems within the Cognitive Robotics perspective must consider the Sensing (perception)—Reasoning (learning)—(inter)Action Loop, which is human-centric. This Special Issue (SI) aims to put together neuro-robotic systems and cognitive robotics research perspectives in order to promote quality research towards human-centric application outputs that might have a potential impact on human lives. Given that, this SI invites original research within the following topics but are not limited to:

  • Biomimetic context awareness, expectation, and intention understanding in neuro-robotics systems;
  • Multimodal sensor fusion and communication;
  • Feedback and actuation in neuro-robotics systems;
  • Knowledge representation, information acquisition, and decision making in neuro-robotics systems;
  • Systematic approach of brain-inspired modelling, learning of sensory and motor systems;
  • Locomotion and manipulation in biological and robot systems;
  • Artificial Intelligence for bio-mechatronics/robot systems;
  • Affective and cognitive sciences for bio-mechatronics, cognitive robot systems, computational intelligence, neuro-mechanical systems;
  • Brain-inspired approaches for: (i) rehabilitation robot system; (ii) medical healthcare robot system; (iii) prosthetic device system, (iv) assistive robot system; (v) wearable robot system for personal cooperative assistance;
  • Intelligent learning and skill transfer system for neuro-robotic systems;
  • Applied Machine Learning for neuro-robotics systems and cognitive robotics;
  • Brain–computer interfaces for robotic and intelligent systems.

Dr. Diego R. Faria
Dr. Alessandro Carfì
Dr. Timothy Patten
Prof. Dr. Markus Vincze
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. 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 the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 4135 KiB  
Article
Bridging Neuroscience and Robotics: Spiking Neural Networks in Action
by Alexander Jones, Vaibhav Gandhi, Adam Y. Mahiddine and Christian Huyck
Sensors 2023, 23(21), 8880; https://0-doi-org.brum.beds.ac.uk/10.3390/s23218880 - 1 Nov 2023
Cited by 1 | Viewed by 1252
Abstract
Robots are becoming increasingly sophisticated in the execution of complex tasks. However, an area that requires development is the ability to act in dynamically changing environments. To advance this, developments have turned towards understanding the human brain and applying this to improve robotics. [...] Read more.
Robots are becoming increasingly sophisticated in the execution of complex tasks. However, an area that requires development is the ability to act in dynamically changing environments. To advance this, developments have turned towards understanding the human brain and applying this to improve robotics. The present study used electroencephalogram (EEG) data recorded from 54 human participants whilst they performed a two-choice task. A build-up of motor activity starting around 400 ms before response onset, also known as the lateralized readiness potential (LRP), was observed. This indicates that actions are not simply binary processes but rather, response-preparation is gradual and occurs in a temporal window that can interact with the environment. In parallel, a robot arm executing a pick-and-place task was developed. The understanding from the EEG data and the robot arm were integrated into the final system, which included cell assemblies (CAs)—a simulated spiking neural network—to inform the robot to place the object left or right. Results showed that the neural data from the robot simulation were largely consistent with the human data. This neurorobotics study provides an example of how to integrate human brain recordings with simulated neural networks in order to drive a robot. Full article
(This article belongs to the Special Issue Neuro-Robotics Systems: Sensing, Cognition, Learning, and Control)
►▼ Show Figures

Figure 1

16 pages, 386 KiB  
Article
Enhancing Cross-Subject Motor Imagery Classification in EEG-Based Brain–Computer Interfaces by Using Multi-Branch CNN
by Radia Rayan Chowdhury, Yar Muhammad and Usman Adeel
Sensors 2023, 23(18), 7908; https://0-doi-org.brum.beds.ac.uk/10.3390/s23187908 - 15 Sep 2023
Cited by 4 | Viewed by 2727
Abstract
A brain–computer interface (BCI) is a computer-based system that allows for communication between the brain and the outer world, enabling users to interact with computers using neural activity. This brain signal is obtained from electroencephalogram (EEG) signals. A significant obstacle to the development [...] Read more.
A brain–computer interface (BCI) is a computer-based system that allows for communication between the brain and the outer world, enabling users to interact with computers using neural activity. This brain signal is obtained from electroencephalogram (EEG) signals. A significant obstacle to the development of BCIs based on EEG is the classification of subject-independent motor imagery data since EEG data are very individualized. Deep learning techniques such as the convolutional neural network (CNN) have illustrated their influence on feature extraction to increase classification accuracy. In this paper, we present a multi-branch (five branches) 2D convolutional neural network that employs several hyperparameters for every branch. The proposed model achieved promising results for cross-subject classification and outperformed EEGNet, ShallowConvNet, DeepConvNet, MMCNN, and EEGNet_Fusion on three public datasets. Our proposed model, EEGNet Fusion V2, achieves 89.6% and 87.8% accuracy for the actual and imagined motor activity of the eegmmidb dataset and scores of 74.3% and 84.1% for the BCI IV-2a and IV-2b datasets, respectively. However, the proposed model has a bit higher computational cost, i.e., it takes around 3.5 times more computational time per sample than EEGNet_Fusion. Full article
(This article belongs to the Special Issue Neuro-Robotics Systems: Sensing, Cognition, Learning, and Control)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop