Nitish V. Thakor

Nitish V. Thakor (S’78–M’81–SM’89–F’97) is a Professor of Biomedical Engineering at Johns Hopkins University, Baltimore, MD, USA, and also the Director the Singapore Institute for Neurotechnology (SINAPSE) at the National University of Singapore. He has pioneered many technologies for brain monitoring to prosthetic arms and neuroprosthesis. He is an author of more than 250 refereed journal papers, dozen patents, and co-founder of three companies. He is currently the Editor-in-Chief of Medical & Biological Engineering & Computing. Dr. Thakor was the Editor-in-Chief of IEEE Transactions on Neural Systems and Rehabilitation Engineering from 2005 to 2011. He is a recipient of a Research Career Development Award from the National Institutes of Health (NIH), a Presidential Young Investigator Award from the National Science Foundation (NSF), and is a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), Founding Fellow of Biomedical Engineering Society (BMES), and Fellow of the International Federation for Medical and Biological Engineering (IFMBE). He is a recipient of the award of Technical Excellence in Neuroengineering from IEEE Engineering in Medicine and Biology Society, Distinguished Alumnus Award from Indian Institute of Technology, Bombay, India, and a Centennial Medal from the University of Wisconsin School of Engineering.

Associated articles

TBME, Featured Articles
Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations from Extreme Learning
Electromyogram (EMG) signals can be used to decode the intended movements of an amputee for control of a dexterous upper-limb prosthesis. However, normal prosthesis use involves changes in upper-limb positions that influence the EMG signals, hindering the ability of pattern... Read more
TNSRE, Featured Articles
Enhancement of Bilateral Cortical Somatosensory Evoked Potentials to Intact Forelimb Stimulation Following Thoracic Contusion Spinal Cord Injury in Rats
Abstract The adult central nervous system is capable of significant reorganization and adaptation following neurotrauma. After a thoracic contusive spinal cord injury (SCI) neuropathways that innervate the cord below the epicenter of injury are damaged, with minimal prospects for functional recovery. In contrast, pathways above the... Read more
TNSRE, Featured Articles
Decoding Native Cortical Representations for Flexion and Extension at Upper Limb Joints Using Electrocorticography
       Brain–machine interface (BMI) researchers have traditionally focused on modeling endpoint reaching tasks to provide the control of neurally driven prosthetic arms. Most previous research has focused on achieving an endpoint control through a Cartesian-coordinate-centered approach. However, a joint-centered approach could... Read more
TNSRE, Featured Articles
Functional Connectivity Analysis of Mental Fatigue Reveals Different Network Topological Alterations Between Driving and Vigilance Tasks
       Despite the apparent importance of mental fatigue detection, a reliable application is hindered due to the incomprehensive understanding of the neural mechanisms of mental fatigue. In this paper, we investigated the topological alterations of functional brain networks in the theta... Read more
TBME, Featured Articles
Stable Responsive EMG Sequence Prediction and Adaptive Reinforcement with Temporal Convolutional Networks
Movement prediction from EMG can be performed by compressing a short window of EMG into a feature-encoding that is meaningful for classification— an approach that can cause erratic prediction behavior. Temporal convolutional networks (TCN) leverage temporal information from EMG to achieve superior predictions for 3 simultaneous degrees-of-freedom that are more accurate and stable, have a very low response delay, and allow for novel types of interactive training. Addressing EMG decoding as a sequential prediction problem requires a new set of considerations that will lead to enhancements in the reliability, responsiveness, and movement complexity available from prosthesis control systems... Read more