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Closed-Loop Deep Brain Stimulation With Reinforcement Learning and Neural Simulation 480 270 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Closed-Loop Deep Brain Stimulation With Reinforcement Learning and Neural Simulation

This article presents a significant advancement in the field of deep brain stimulation (DBS) through the development of a closed-loop system that integrates reinforcement learning (RL) and neural simulation techniques.…

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Tremor Suppression Using Functional Electrical Stimulation 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Tremor Suppression Using Functional Electrical Stimulation

Parkinson’s disease (PD) and essential tremor are two major causes of pathological tremor among people over 60 years old. Due to the side effects and complications of traditional tremor management…

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Characterizing Autism Spectrum Disorder Through Fusion of Local Cortical Activation and Global Functional Connectivity Using Game-Based Stimuli and a Mobile EEG System 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Characterizing Autism Spectrum Disorder Through Fusion of Local Cortical Activation and Global Functional Connectivity Using Game-Based Stimuli and a Mobile EEG System

The deficit in social interaction skills among individuals with autism spectrum disorder (ASD) is strongly influenced by personal experiences and social environments. Neuroimaging studies have previously highlighted the link between…

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Effective Phoneme Decoding With Hyperbolic Neural Networks for High-Performance Speech BCIs 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Effective Phoneme Decoding With Hyperbolic Neural Networks for High-Performance Speech BCIs

Objective: Speech brain-computer interfaces (speech BCIs), which convert brain signals into spoken words or sentences, have demonstrated great potential for high-performance BCI communication. Phonemes are the basic pronunciation units. For…

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Graph Neural Network-Based EEG Classification: A Survey 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Graph Neural Network-Based EEG Classification: A Survey

Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been…

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Enhanced Muscle Activation using Robotic Assistance within the Electromechanical Delay: Implications for Rehabilitation? 853 480 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Enhanced Muscle Activation using Robotic Assistance within the Electromechanical Delay: Implications for Rehabilitation?

Rehabilitation robots are expected to save time, money, and expand access to some physical therapies. Moreover, robots can perform certain actions that human therapists cannot which may open up novel…

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Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation
Author(s)3: Guangyi Zhang, Ali Etemad

Driver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring of the vigilance level of drivers to…

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Quantification of Motor Function Post-stroke using Novel Combination of Wearable Inertial and Mechanomyographic Sensors 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Quantification of Motor Function Post-stroke using Novel Combination of Wearable Inertial and Mechanomyographic Sensors
Author(s)3: Lewis Formstone, Weiguang Huo, Samuel Wilson, Alison McGregor, Paul Bentley, Ravi Vaidyanathan

Subjective clinical rating scales represent the gold-standard diagnosis of motor function following stroke, however in practice they suffer from well-recognized limitations including assessor variance, low inter-rater reliability and low resolution.…

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Domain-adaptive Fall Detection Using Deep Adversarial Training 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Domain-adaptive Fall Detection Using Deep Adversarial Training
Author(s)3: Kai-Chun Liu, Michael Chan, Heng-Cheng Kuo, Chia-Yeh Hsieh, Hsiang-Yun Huang, Chia-Tai Chan, Yu Tsao

Fall detection (FD) systems are important assistive technologies for healthcare that can detect emergency fall events and alert caregivers. However, it is not easy to obtain large-scale annotated fall events…

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