Transactions on Neural Systems and Rehabilitation Engineering
Featured Articles
Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring
Aleš Procházka, Ondřej Dostál, Pavel Cejnar, Hagar Ibrahim Mohamed, Zbyšek Pavelek, Martin Vališ, Oldřich Vyšata
Ataxic gait monitoring and assessment of neurological disorders belong to important multidisciplinary areas that are supported by digital signal processing methods and machine learning tools. This paper presents the possibility of using accelerometric data to optimise deep learning convolutional neural...
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Posted on 26 MAR 2021
Articles
Standardization of Neurotechnology for Brain-Machine Interfacing: State of the Art and Recommendations
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Posted on 24 FEB 2021
Articles
Preliminary Minimum Reporting Requirements for In-Vivo Neural Interface Research: I. Implantable Neural Interfaces
Calvin Elber, Jean Delbeke, Jorge Cardoso, Martijn De Neeling, Sam E John, Jerry Skefos, Dimiter Prodanov, Chang Won Lee, Argus Sun, Zach Mckinney
The pace of research and development in neuroscience, neurotechnology, and neurorehabilitation is rapidly accelerating, with the number of publications doubling every 4.2 years. Maintaining this progress requires technological standards and scientific reporting guidelines to provide frameworks for communication and interoperability....
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Posted on 23 FEB 2021
Articles
A Roadmap towards Standards for Neurally Controlled End Effectors
Andrew Paek, Justin Brantley, Akshay Sujatha Ravindran, Kevin Nathan, Yongtian He, David Eguren, Jesus Cruz-Garza, Sho Nakagome, Dilranjan Wickramasuriya, Jiajun Chang, Md. Rashed-Al-Mahfuz, Md. Rafiul Amin, Nikunj Arunkumar Bhagat, Jose Luis Contreras-Vidal
The control and manipulation of various types of end effectors such as powered exoskeletons, prostheses, and neural cursors by brain-machine interface (BMI) systems has been the target of many research projects. A seamless plug and play interface between any BMI...
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Posted on 12 FEB 2021
Featured Articles
From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes
Rober Boshra, Kiret Dhindsa, Omar Boursalie, Kyle I. Ruiter, Ranil Sonnadara, Reza Samavi, Thomas E. Doyle, James P. Reilly, John F. Connolly
There has been increased effort to understand the neurophysiological effects of concussion aimed to move diagnosis and identification beyond current subjective behavioral assessments that suffer from poor sensitivity. Recent evidence suggests that event-related potentials (ERPs) measured with electroencephalography (EEG) are...
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Posted on 18 JUL 2019
Articles, Published Articles
Cardiac-DeepIED: Automatic Pixel-level Deep Segmentation for Cardiac Bi-ventricle Using Improved End-to-End Encoder-Decoder Network
Abstract
Accurate segmentation of cardiac bi-ventricle (CBV) from magnetic resonance (MR) images has a great significance to analyze and evaluate the function of the cardiovascular system. However, the complex structure of CBV image makes fully automatic segmentation as a well-known challenge....
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Posted on 8 MAR 2019
Featured Articles
MfeCNN: Mixture Feature Embedding Convolutional Neural Network for Data Mapping
Data mapping plays an important role in data integration and exchanges among institutions and organizations with different data standards. However, traditional rule-based approaches and machine learning methods fail to achieve satisfactory results for the data mapping problem. In this paper,...
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Posted on 6 AUG 2018
Featured Articles
PhosPred-RF: a novel sequence-based predictor for phosphorylation sites using sequential information only
Many recent efforts have been made for the development of machine learning based methods for fast and accurate phosphorylation site prediction. Currently, a majority of well-performing methods are based on hybrid information to build prediction models, such as evolutionary information,...
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Posted on 30 JUL 2017