Recording

Hidden Brain State-Based Internal Evaluation Using Kernel Inverse Reinforcement Learning in Brain-Machine Interfaces
Hidden Brain State-Based Internal Evaluation Using Kernel Inverse Reinforcement Learning in Brain-Machine Interfaces 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Reinforcement learning (RL)-based brain machine interfaces (BMIs) assist paralyzed people in controlling neural prostheses without the need for real limb movement as supervised signals. The design of reward signal significantly… read more

Reconstructing Multi-Stroke Characters from Brain Signals toward Generalizable Handwriting Brain-Computer Interfaces
Reconstructing Multi-Stroke Characters from Brain Signals toward Generalizable Handwriting Brain-Computer Interfaces 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Handwriting Brain-Computer Interfaces (BCIs) provides a promising communication avenue for individuals with paralysis. While English-based handwriting BCIs have achieved rapid typewriting with 26 lowercase letters (mostly containing one stroke each),… read more

Prediction of rTMS Efficacy in Patients With Essential Tremor: Biomarkers From Individual Resting-State EEG Network
Prediction of rTMS Efficacy in Patients With Essential Tremor: Biomarkers From Individual Resting-State EEG Network 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
The pathogenesis of essential tremor (ET) remains unclear, and the efficacy of related drug treatment is inadequate for proper tremor control. Hence, in the current study, consecutive low-frequency repetitive transcranial… read more

A Scoping Review of Machine Learning Applied to Peripheral Nerve Interfaces
A Scoping Review of Machine Learning Applied to Peripheral Nerve Interfaces 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Peripheral nerve interfaces (PNIs) can enable communication with the peripheral nervous system and have a broad range of applications including in bioelectronic medicine and neuroprostheses. They can modulate neural activity… read more

Early Detection of Parkinson’s Disease Using Deep NeuroEnhanceNet With Smartphone Walking Recordings
Early Detection of Parkinson’s Disease Using Deep NeuroEnhanceNet With Smartphone Walking Recordings 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
With the development of digital medical technology, ubiquitous smartphones are emerging as valuable tools for the detection of complex and elusive diseases. This paper exploits smartphone walking recording for early… read more

Effect of Inverse Solutions, Connectivity Measures, and Node Sizes on EEG Source Network: A Simultaneous EEG Study
Effect of Inverse Solutions, Connectivity Measures, and Node Sizes on EEG Source Network: A Simultaneous EEG Study 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Brain network provides an essential perspective for studying normal and pathological brain activities. Reconstructing the brain network in the source space becomes more needed, for example, as a target in… read more

Non-invasive Ring Electrode with a Wireless Electrical Recording and Stimulating System for Monitoring Preterm Labor
Author(s)3: Yi Jae Lee, Changhyuk Lee, Eun Jin Wang, Dmytro Kotov, Hee Youn Kim, Jeong Ho Hwang, Ki Hoon Ahn, Soo Hyun Lee
Non-invasive Ring Electrode with a Wireless Electrical Recording and Stimulating System for Monitoring Preterm Labor 546 713 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Preterm labor and birth are the primary causes of neonatal morbidities and mortalities. The early detection and treatment of preterm uterine muscular contraction are crucial for the management of preterm labor. In this work, a ring electrode with a wireless electrical recording and stimulating (RE-WERS) system was designed, fabricated, and investigated for the noninvasive monitoring of uterine contraction/relaxation as a diagnostic and therapeutic tool for preterm labor. read more
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